Search results for: weather based advisory

Authors: Abdellatif Chebboub

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Agricultural production is sensitive to weather and thus directly affected by climate change. Plausible estimates of these climate change impacts require combined use of climate, crop, and economic models. Results from previous studies vary substantially due to differences in models, scenarios, and data. This paper is part of a collective effort to systematically integrate these three types of models. We focus on the economic component of the assessment, investigating how nine global economic models of agriculture represent endogenous responses to seven standardized climate change scenarios produced by two climate and five crop models. These responses include adjustments in yields, area, consumption, and international trade. We apply biophysical shocks derived from the Intergovernmental Panel on Climate Change’s representative concentration pathway with end-of-century radiative forcing of 8.5 W/m2. The mean biophysical yield effect with no incremental CO2 fertilization is a 17% reduction globally by 2050 relative to a scenario with unchanging climate. Endogenous economic responses reduce yield loss to 11%, increase area of major crops by 11%, and reduce consumption by 3%. Agricultural production, cropland area, trade, and prices show the greatest degree of variability in response to climate change, and consumption the lowest. The sources of these differences include model structure and specification; in particular, model assumptions about ease of land use conversion, intensification, and trade. This study identifies where models disagree on the relative responses to climate shocks and highlights research activities needed to improve the representation of agricultural adaptation responses to climate change.

Keywords: climate change, agriculture, weather change, danger of climate change

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Authors: Annapurna Basavaraju, Andreas Marn, Franz Heitmeir

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The Advisory Council for Aeronautics Research in Europe (ACARE) is cognizant for the overall reduction of NOx emissions by 80% in its vision 2020. Moreover small turbo engines have higher fuel specific emissions compared to large engines due to their limited combustion chamber size. In order to fulfill these requirements, novel combustion concepts are essential. This motivates to carry out the research on the current state of art, catalytic stabilized combustion chamber using hydrogen in small jet engines which are designed and investigated both numerically and experimentally during this project. Catalytic combustion concepts can also be adopted for low caloric fuels and are therefore not constrained to only hydrogen. However, hydrogen has high heating value and has the major advantage of producing only the nitrogen oxides as pollutants during the combustion, thus eliminating the interest on other emissions such as Carbon monoxides etc. In the present work, the combustion chamber is designed based on the ‘Rich catalytic Lean burn’ concept. The experiments are conducted for the characteristic operating range of an existing engine. This engine has been tested successfully at Institute of Thermal Turbomachinery and Machine Dynamics (ITTM), Technical University Graz. One of the facts that the efficient combustion is a result of proper mixing of fuel-air mixture, considerable significance is given to the selection of appropriate mixer. This led to the design of three diverse configurations of mixers and is investigated experimentally and numerically. Subsequently the best mixer would be equipped in the main combustion chamber and used throughout the experimentation. Furthermore, temperatures and pressures would be recorded at various locations inside the combustion chamber and the exhaust emissions will also be analyzed. The instrumented combustion chamber would be inspected at the engine relevant inlet conditions for nine different sets of catalysts at the Hot Flow Test Facility (HFTF) of the institute.

Keywords: catalytic combustion, gas turbine, hydrogen, mixer, NOx emissions

Procedia PDF Downloads 273

Authors: Richard Ren

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Air pollution is a serious danger to international well-being and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.

Keywords: air quality prediction, air pollution, artificial intelligence, machine learning algorithms

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Authors: Seane Mabitsela

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The Territory of Namibia was entrusted to South Africa as a Mandate under the League of Nations Covenant. After the dissolution of the League of Nations and the commencement of United Nations operations, South Africa's conception of its legal obligations under the mandate varied from those of other members of the United Nations. Because of that, the General Assembly requested the International Court of Justice for an Advisory Opinion on the international obligations of South Africa arising therefrom. The International Court of Justice declared that South West Africa was still a mandatory territory under the Covenant of the League of Nations. It also held that South Africa continued to transmit petitions from inhabitants of the territory, the supervisory functions to be exercised by the United Nations, to which the annual reports and the petitions were to be submitted. Subject to this judgement, the question of South West Africa remained a dispute relating to the mandate brought before the International Court of Justice against South Africa. The International Court of Justice and South Africa dispute reflected the nature of the Namibian inhabitants’ appeal for recognition at the United Nations.

Keywords: International Court of Justice, Namibia, petitions, United Nations

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Authors: Birtukan Atinkut Asmare

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Climate change disproportionately affects many Africans who heavily rely on climate-exposed sectors such as rain-fed agriculture and fishing, rendering them highly vulnerable. Gender plays a significant role, as men and women experience unequal impacts and vulnerabilities due to gender norms, labor divisions, resource access, and power dynamics. Drawing on an integrated framework, this study sheds light on the gendered impacts of climate change on household’s livelihood, their vulnerability, and adaptation in rural Ethiopia's Lake Tana Basin. This study utilized mixed research methods, integrating diverse qualitative techniques such as focus group discussions, key informant interviews, and field observations, along with quantitative data gathered through household surveys. The findings reveal that women-headed households were more vulnerable to climate change than male-headed households. Flood was the major climate-induced hazards in the area that threatened the lives and livelihoods of households. In response to climate change, households undertook different adaptation measures such as agroforestry practices, crop diversification, seasonal migration, petty trading, charcoal and fuel wood sales. However, the adaptation strategies were slightly varied based on the gender of the household head. Women-headed households specifically engaged in fuelwood collection and selling and petty trading activities. The main constraints for adaptation were limited access to technologies, extension services, information, and financial services. Therefore, this research urges attention from research, policy, and advisory services on rural households who are trying to survive in the face of climate change.

Keywords: agriculture, climate change impacts, ethiopia, gender

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Authors: A. Ascenso, C. Silveira, B. Augusto, S. Rafael, S. Coelho, J. Ferreira, A. Monteiro, P. Roebeling, A. I. Miranda

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Innovative nature-based solutions (NBSs) can provide answers to the challenges that urban areas are currently facing due to urban densification and extreme weather conditions. The effects of NBSs are recognized and include improved quality of life, mental and physical health and improvement of air quality, among others. Part of the work developed in the scope of the UNaLab project, which aims to guide cities in developing and implementing their own co-creative NBSs, intends to assess the impacts of NBSs on air quality, using Eindhoven city as a case study. The state-of-the-art online air quality modelling system WRF-CHEM was applied to simulate meteorological and concentration fields over the study area with a spatial resolution of 1 km2 for the year 2015. The baseline simulation (without NBSs) was validated by comparing the model results with monitored data retrieved from the Eindhoven air quality database, showing an adequate model performance. In addition, land use changes were applied in a set of simulations to assess the effects of different types of NBSs. Finally, these simulations were compared with the baseline scenario and the impacts of the NBSs were assessed. Reductions on pollutant concentrations, namely for NOx and PM, were found after the application of the NBSs in the Eindhoven study area. The present work is particularly important to support public planners and decision makers in understanding the effects of their actions and planning more sustainable cities for the future.

Keywords: air quality, modelling approach, nature based solutions, urban area

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Authors: Athanasios A. Gavrilidis, Mihai R. Nita, Larissa N. Stoia, Diana A. Onose

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Contemporary global environmental challenges must be primarily addressed at local levels. Cities are under continuous pressure as they must ensure high quality of life levels for their citizens and at the same time to adapt and address specific environmental issues. Innovative solutions using natural features or mimicking natural systems are endorsed by the scientific community as efficient approaches for both mitigating climate change effects and the decrease of environmental quality and for maintaining high standards of living for urban dwellers. The aim of this study was to assess whether Romanian cities’ authorities are considering nature-based innovation as solutions for their planning, management, and environmental issues. Data were gathered by applying 140 questionnaires to urban authorities throughout the country. The questionnaire was designed for assessinglocal policy makers’ perspective over the efficiency of nature-based innovations as a tool to address specific challenges. It also focused on extracting data about financing sources and challenges they must overcome for adopting nature-based approaches. The gather results from the municipalities participating in our study were statistically processed, and they revealed that Romanian city managers acknowledge the benefits of nature-based innovations, but investments in this sector are not on top of their priorities. More than 90% of the selected cities have agreed that in the last 10 years, their major concern was to expand the grey infrastructure (roads and public amenities) using traditional approaches. When asked how they would react if faced with different socio-economic and environmental challenges, local urban managers indicated investments nature-based solutions as a priority only in case of biodiversity loss and extreme weather, while for other 14 proposed scenarios, they would embrace the business-as-usual approach. Our study indicates that while new concepts of sustainable urban planning emerge within the scientific community, local authorities need more time to understand and implement them. Without the proper knowledge, personnel, policies, or dedicated budgets, local administrators will not embrace nature-based innovations as solutions for their challenges.

Keywords: nature based innovations, perception analysis, policy making, urban planning

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Authors: Fatemehsadat Mortazavizadeh, Amin Dehghani, Majid Mirzaei, Nurulhuda Binti Mohammad Ramli, Adnan Dehghani

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Flood is Malaysia's most common and serious natural disaster. Kelantan River Basin is a tropical basin that experiences a rainy season during North-East Monsoon from November to March. It is also one of the hardest hit areas in Peninsular Malaysia during the heavy monsoon rainfall. Considering the consequences of the flood events, it is essential to develop the flood inundation map as part of the mitigation approach. In this study, the delineation of flood inundation zone in the area of Kelantan River basin using a hydrodynamic model is done by HEC-RAS, QGIS and ArcMap. The streamflow data has been generated with the weather generator based on the observation data. Then, the data is statistically analyzed with the Extreme Value (EV1) method for 2-, 5-, 25-, 50- and 100-year return periods. The minimum depth, maximum depth, mean depth, and the standard deviation of all the scenarios, including the OBS, are observed and analyzed. Based on the results, generally, the value of the data increases with the return period for all the scenarios. However, there are certain scenarios that have different results, which not all the data obtained are increasing with the return period. Besides, OBS data resulted in the middle range within Scenario 1 to Scenario 40.

Keywords: flood inundation, kelantan river basin, hydrodynamic model, extreme value analysis

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Authors: P. Lauk, K. E. Seegel, T. Tähemaa

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The fuel consumption of modern, high wing loading, commercial aircraft in the first stage of flight is high because the usable flight level is lower and the weather conditions (jet stream) have great impact on aircraft performance. To reduce the fuel consumption, it is necessary to raise during first stage of flight the L/D ratio value within Cl 0.55-0.65. Different variable geometrical wing trailing edge modifications of SC(2)-410 airfoil were compared at M 0.78 using the CFD software STAR-CCM+ simulation based Reynolds-averaged Navier-Stokes (RANS) equations. The numerical results obtained show that by increasing the width of the airfoil by 4% and by modifying the trailing edge airfoil, it is possible to decrease airfoil drag at Cl 0.70 for up to 26.6% and at the same time to increase commercial aircraft L/D ratio for up to 5.0%. Fuel consumption can be reduced in proportion to the increase in L/D ratio.

Keywords: L/D ratio, miniflaps, mini-TED, supercritical airfoil

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Authors: Raghad Alhusari, Farag Omar, Moustafa Fadel

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A traditional greenhouse is a metal frame agricultural building used for cultivation plants in a controlled environment isolated from external climatic changes. Using greenhouses in agriculture is an efficient way to reduce the water consumption, where agriculture field is considered the biggest water consumer world widely. Controlling greenhouse environment yields better productivity of plants but demands an increase of electric power. Although various control approaches have been used towards greenhouse automation, most of them are applied to traditional greenhouses with ventilation fans and/or evaporation cooling system. Such approaches are still demanding high energy and water consumption. The aim of this research is to develop a fuzzy control system that minimizes water and energy consumption by utilizing outside weather conditions and underground heat exchanger to maintain the optimum climate of the greenhouse. The proposed control system is implemented on an experimental model of thermally isolated greenhouse structure with dimensions of 6x5x2.8 meters. It uses fans for extracting heat from the ground heat exchanger system, motors for automatic open/close of the greenhouse windows and LED as lighting system. The controller is integrated also with environmental condition sensors. It was found that using the air-to-air horizontal ground heat exchanger with 90 mm diameter and 2 mm thickness placed 2.5 m below the ground surface results in decreasing the greenhouse temperature of 3.28 ˚C which saves around 3 kW of consumed energy. It also eliminated the water consumption needed in evaporation cooling systems which are traditionally used for cooling the greenhouse environment.

Keywords: automation, earth-to-air heat exchangers, fuzzy control, greenhouse, sustainable buildings

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Authors: Lucille Alonso, Florent Renard

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The characterizations of urban heat island (UHI) and their interactions with climate change and urban climates are the main research and public health issue, due to the increasing urbanization of the population. These solutions require a better knowledge of the UHI and micro-climate in urban areas, by combining measurements and modelling. This study is part of this topic by evaluating microclimatic conditions in dense urban areas in the Lyon Metropolitan Area (France) using a combination of data traditionally used such as topography, but also from LiDAR (Light Detection And Ranging) data, Landsat 8 satellite observation and Sentinel and ground measurements by bike. These bicycle-dependent weather data collections are used to build the database of the variable to be modelled, the air temperature, over Lyon’s hyper-center. This study aims to model the air temperature, measured during 6 mobile campaigns in Lyon in clear weather, using multiple linear regressions based on 33 explanatory variables. They are of various categories such as meteorological parameters from remote sensing, topographic variables, vegetation indices, the presence of water, humidity, bare soil, buildings, radiation, urban morphology or proximity and density to various land uses (water surfaces, vegetation, bare soil, etc.). The acquisition sources are multiple and come from the Landsat 8 and Sentinel satellites, LiDAR points, and cartographic products downloaded from an open data platform in Greater Lyon. Regarding the presence of low, medium, and high vegetation, the presence of buildings and ground, several buffers close to these factors were tested (5, 10, 20, 25, 50, 100, 200 and 500m). The buffers with the best linear correlations with air temperature for ground are 5m around the measurement points, for low and medium vegetation, and for building 50m and for high vegetation is 100m. The explanatory model of the dependent variable is obtained by multiple linear regression of the remaining explanatory variables (Pearson correlation matrix with a |r| < 0.7 and VIF with < 5) by integrating a stepwise sorting algorithm. Moreover, holdout cross-validation is performed, due to its ability to detect over-fitting of multiple regression, although multiple regression provides internal validation and randomization (80% training, 20% testing). Multiple linear regression explained, on average, 72% of the variance for the study days, with an average RMSE of only 0.20°C. The impact on the model of surface temperature in the estimation of air temperature is the most important variable. Other variables are recurrent such as distance to subway stations, distance to water areas, NDVI, digital elevation model, sky view factor, average vegetation density, or building density. Changing urban morphology influences the city's thermal patterns. The thermal atmosphere in dense urban areas can only be analysed on a microscale to be able to consider the local impact of trees, streets, and buildings. There is currently no network of fixed weather stations sufficiently deployed in central Lyon and most major urban areas. Therefore, it is necessary to use mobile measurements, followed by modelling to characterize the city's multiple thermal environments.

Keywords: air temperature, LIDAR, multiple linear regression, surface temperature, urban heat island

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Authors: Ahmeda Ali, Mary Codd, Susan Brundage

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Importance: This research focuses on devising and improving Health Service Executive (HSE) policy and legislation and therefore improving patient trauma care and outcomes in Ireland. Objectives: The study measures components of the Trauma System in the district hospital setting of the Cavan/Monaghan Hospital Group (CMHG), HSE, Ireland, and uses the collected data to identify the strengths and weaknesses of the CMHG Trauma System organisation, to include governance, injury data, prevention and quality improvement, scene care and facility-based care, and rehabilitation. The information will be made available to local policy makers to provide objective situational analysis to assist in future trauma service planning and service provision. Design, setting and participants: From 28 April to May 28, 2016 a cross-sectional survey using World Health Organisation (WHO) Trauma System Assessment Tool (TSAT) was conducted among healthcare professionals directly involved in the level III trauma system of CMHG. Main outcomes: Identification of the strengths and weaknesses of the Trauma System of CMHG. Results: The participants who reported inadequate funding for pre hospital (62.3%) and facility based trauma care at CMHG (52.5%) were high. Thirty four (55.7%) respondents reported that a national trauma registry (TARN) exists but electronic health records are still not used in trauma care. Twenty one respondents (34.4%) reported that there are system wide protocols for determining patient destination and adequate, comprehensive legislation governing the use of ambulances was enforced, however, there is a lack of a reliable advisory service. Over 40% of the respondents reported uncertainty of the injury prevention programmes available in Ireland; as well as the allocated government funding for injury and violence prevention. Conclusions: The results of this study contributed to a comprehensive assessment of the trauma system organisation. The major findings of the study identified three fundamental areas: the inadequate funding at CMHG, the QI techniques and corrective strategies used, and the unfamiliarity of existing prevention strategies. The findings direct the need for further research to guide future development of the trauma system at CMHG (and in Ireland as a whole) in order to maximise best practice and to improve functional and life outcomes.

Keywords: trauma, education, management, system

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Authors: Md Ruhul Amin

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This study was conducted in the experimental field of the Department of Entomology, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, Bangladesh during November 2017 to May 2018 with a view to understanding the seasonal abundance of the major sucking pests namely thrips, aphid and red spider mite on rose. The findings showed that the thrips started to build up their population from the middle of January with abundance 1.0 leaf⁻¹, increased continuously, reached to the peak level (2.6 leaf⁻¹) in the middle of February and then declined. Aphid started to build up their population from the second week of November with abundance 6.0 leaf⁻¹, increased continuously, reached to the peak level (8.4 leaf⁻¹) in the last week of December and then declined. Mite started to build up their population from the first week of December with abundance 0.8 leaf⁻¹, increased continuously, reached to the peak level (8.2 leaf⁻¹) in the second week of March and then declined. Thrips and mite prevailed until the last week of April, and aphid showed their abundance till last week of May. The daily mean temperature, relative humidity, and rainfall had an insignificant negative correlation with thrips and significant negative correlation with aphid abundance. The daily mean temperature had significant positive, relative humidity had an insignificant positive, and rainfall had an insignificant negative correlation with mite abundance. The multiple linear regression analysis showed that the weather parameters together contributed 38.1, 41.0 and 8.9% abundance on thrips, aphid and mite on rose, respectively and the equations were insignificant.

Keywords: aphid, mite, thrips, weather factors

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Authors: Ursachi Andrei

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The Smart Grid Simulator is a computer software based on advanced algorithms which has as the main purpose to lower the energy bill in the most optimized price efficient way as possible for private households, companies or energy providers. It combines the energy provided by a number of solar modules and wind turbines with the consumption of one household or a cluster of nearby households and information regarding weather conditions and energy prices in order to predict the amount of energy that can be produced by renewable energy sources and the amount of energy that will be bought from the distributor for the following day. The user of the system will not only be able to minimize his expenditures on energy fractures, but also he will be informed about his hourly consumption, electricity prices fluctuation and money spent for energy bought as well as how much money he saved each day and since he installed the system. The paper outlines the algorithm that supports the Smart Grid Simulator idea and presents preliminary test results that support the discussion and implementation of the system.

Keywords: smart grid, sustainable energy, applied science, renewable energy sources

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Authors: Dimphna Ezikanyi, Gloria Sakwari

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Airborne pollen and fungal spores are major triggers of allergies, and their abundance and seasonality depend on plant responses to climatic and meteorological variables. A survey of seasonal prevalence of airborne pollen and fungal spores in Nsukka, Enugu, South- East Nigeria and relationship to climatic variables were carried out from Jan-June, 2017. The aim of the study was to access climate change and variability over time in the area and their accrued influence on modern pollen and spores rain. Decadal change in climate was accessed from variables collected from meteorological centre in the study area. Airborne samples were collected monthly using a modified Tauber-like pollen samplers raised 5 ft above ground level. Aerosamples collected were subjected to acetolysis. Dominant pollen recorded were those of Poaceae, Elaeis guinensis Jacq. and Casuarina equisetifolia L. Change in weather brought by onset of rainfall evoked sporulation and dispersal of diverse spores into ambient air especially potent allergenic spores with the spores of Ovularia, Bispora, Curvularia, Nigrospora, Helminthosporium preponderant; these 'hydrophilic fungi' were abundant in the rainy season though in varying quantities. Total fungal spores correlated positively with monthly rainfall and humidity but negatively with temperature. There was a negative though not significant correlation between total pollen count and rainfall. The study revealed a strong influence of climatic variables on abundance and spatial distribution of pollen and fungal spores in the ambient atmosphere.

Keywords: allergy, fungal spores, pollen, weather parameters

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Authors: Samy A. Khalil, U. Ali Rahoma

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The measurements of solar radiation, satellite data has been routinely utilize to estimate solar energy. However, the temporal coverage of satellite data has some limits. The reanalysis, also known as "retrospective analysis" of the atmosphere's parameters, is produce by fusing the output of NWP (Numerical Weather Prediction) models with observation data from a variety of sources, including ground, and satellite, ship, and aircraft observation. The result is a comprehensive record of the parameters affecting weather and climate. The effectiveness of reanalysis datasets (ERA-5) for North Africa was evaluate against high-quality surfaces measured using statistical analysis. Estimating the distribution of global solar radiation (GSR) over five chosen areas in North Africa through ten-years during the period time from 2011 to 2020. To investigate seasonal change in dataset performance, a seasonal statistical analysis was conduct, which showed a considerable difference in mistakes throughout the year. By altering the temporal resolution of the data used for comparison, the performance of the dataset is alter. Better performance is indicate by the data's monthly mean values, but data accuracy is degraded. Solar resource assessment and power estimation are discuses using the ERA-5 solar radiation data. The average values of mean bias error (MBE), root mean square error (RMSE) and mean absolute error (MAE) of the reanalysis data of solar radiation vary from 0.079 to 0.222, 0.055 to 0.178, and 0.0145 to 0.198 respectively during the period time in the present research. The correlation coefficient (R2) varies from 0.93 to 99% during the period time in the present research. This research's objective is to provide a reliable representation of the world's solar radiation to aid in the use of solar energy in all sectors.

Keywords: solar energy, ERA-5 analysis data, global solar radiation, North Africa

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Authors: Alessandra Swiny, Michalis Georgiou, Yiorgos Hadjichristou

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This paper focuses on the questions raised through the work of Unit 5: ‘In/Out of crisis, emergent and adaptive’; an architectural research-based studio at the University of Nicosia. It focusses on sustainable architectural and urban explorations tackling with the ever growing crises in its various types, phases and locations. ‘Great crisis situations’ are seen as ‘great chances’ that trigger investigations for further development and evolution of the built environment in an ultimate sustainable approach. The crisis is taken as an opportunity to rethink the urban and architectural directions as new forces for inventions leading to emergent and adaptive built environments. The Unit 5’s identity and environment facilitates the students to respond optimistically, alternatively and creatively towards the global current crisis. Mark Wigley’s notion that “crises are ultimately productive” and “They force invention” intrigued and defined the premises of the Unit. ‘Weather and nature are coauthors of the built environment’ Jonathan Hill states in his ‘weather architecture’ discourse. The weather is constantly changing and new environments, the subnatures are created which derived from the human activities David Gissen explains. The above set of premises triggered innovative responses by the Unit’s students. They thoroughly investigated the various kinds of crisis and their causes in relation to their various types of Terrains. The tools used for the research and investigation were chosen in contradictive pairs to generate further crisis situations: The re-used/salvaged competed with the new, the handmade rivalling with the fabrication, the analogue juxtaposed with digital. Students were asked to delve into state of art technologies in order to propose sustainable emergent and adaptive architectures and Urbanities, having though always in mind that the human and the social aspects of the community should be the core of the investigation. The resulting unprecedented spatial conditions and atmospheres of the emergent new ways of living are deemed to be the ultimate aim of the investigation. Students explored a variety of sites and crisis conditions such as: The vague terrain of the Green Line in Nicosia, the lost footprints of the sinking Venice, the endangered Australian coral reefs, the earthquake torn town of Crevalcore, and the decaying concrete urbanscape of Athens. Among other projects, ‘the plume project’ proposes a cloud-like, floating and almost dream-like living environment with unprecedented spatial conditions to the inhabitants of the coal mine of Centralia, USA, not just to enable them to survive but even to prosper in this unbearable environment due to the process of the captured plumes of smoke and heat. Existing water wells inspire inversed vertical structures creating a new living underground network, protecting the nomads from catastrophic sand storms in the Araoune of Mali. “Inverted utopia: Lost things in the sand”, weaves a series of tea-houses and a library holding lost artifacts and transcripts into a complex underground labyrinth by the utilization of the sand solidification technology. Within this methodology, crisis is seen as a mechanism for allowing an emergence of new and fascinating ultimate sustainable future cultures and cities.

Keywords: adaptive built environments, crisis as opportunity, emergent urbanities, forces for inventions

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Authors: Fei Gao, Xinyang Dong, Yisu Ge, Shufang Lu, Libo Weng

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Vehicle color recognition plays an important role in traffic accident investigation. However, due to the influence of illumination, weather, and noise, vehicle color recognition still faces challenges. In this paper, a vehicle color classification network based on spatial cluster loss and channel attention mechanism (SCNet) is proposed for vehicle color recognition. A channel attention module is applied to extract the features of vehicle color representative regions and reduce the weight of nonrepresentative color regions in the channel. The proposed loss function, called spatial clustering loss (SC-loss), consists of two channel-specific components, such as a concentration component and a diversity component. The concentration component forces all feature channels belonging to the same class to be concentrated through the channel cluster. The diversity components impose additional constraints on the channels through the mean distance coefficient, making them mutually exclusive in spatial dimensions. In the comparison experiments, the proposed method can achieve state-of-the-art performance on the public datasets, VCD, and VeRi, which are 96.1% and 96.2%, respectively. In addition, the ablation experiment further proves that SC-loss can effectively improve the accuracy of vehicle color recognition.

Keywords: feature extraction, convolutional neural networks, intelligent transportation, vehicle color recognition

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Authors: Getaneh Berie Tarekegn

Abstract:

A precise localization system is crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. The most common method for providing continuous positioning services in outdoor environments is by using a global navigation satellite system (GNSS). Due to nonline-of-sight, multipath, and weather conditions, GNSS systems do not perform well in dense urban, urban, and suburban areas.This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. It also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 0.39 m, and more than 90% of the errors are less than 0.82 m. According to numerical results, SRCLoc improves positioning performance and reduces radio map construction costs significantly compared to traditional methods.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

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Authors: Huicong Jia, Donghua Pan

Abstract:

As a key component of disaster reduction management, disaster emergency relief and reconstruction is an important process. Based on disaster system theory, this study analyzed the Jiangsu tornado from the formation mechanism of disasters, through to the economic losses, loss of life, and social infrastructure losses along the tornado disaster chain. The study then assessed the emergency relief and reconstruction efforts, based on an analytic hierarchy process method. The results were as follows: (1) An unstable weather system was the root cause of the tornado. The potentially hazardous local environment, acting in concert with the terrain and the river network, was able to gather energy from the unstable atmosphere. The wind belt passed through a densely populated district, with vulnerable infrastructure and other hazard-prone elements, which led to an accumulative disaster situation and the triggering of a catastrophe. (2) The tornado was accompanied by a hailstorm, which is an important triggering factor for a tornado catastrophe chain reaction. (3) The evaluation index (EI) of the emergency relief and reconstruction effect for the ‘‘6.23’’ tornado disaster in Yancheng was 91.5. Compared to other relief work in areas affected by disasters of the same magnitude, there was a more successful response than has previously been experienced. The results provide new insights for studies of disaster systems and the recovery measures in response to tornado catastrophe in China.

Keywords: China, disaster system, emergency relief, tornado catastrophe

Procedia PDF Downloads 241

Authors: Tina Naser Torabi

Abstract:

Prevalence of allergy is affected by different factors according to its base and seasonal weather changes, and it also needs various treatments.Although reasons of allergy existence are not clear but generally, allergens cause reaction between antigen and antibody because of their antigenic traits. In this state, allergens cause immune system to make mistake and identify safe material as threat, therefore function of immune system impaired because of histamine secretion. There are different reasons for allergy, but herbal reasons are on top of the list, although animal causes cannot be ignored. Important point is that allergenic compounds, cause making dedicated antibody, so in general every kind of allergy is different from the other one. Therefore, most of the plants in herbal allergenic category can cause various allergies for human beings, such as respiratory allergies, nutritional allergies, injection allergies, infection allergies, touch allergies, that each of them show different symptoms based on the reason of allergy and also each of them requires different prevention and treatment. Geographical condition is another effective factor in allergy. Seasonal changes, weather condition, herbal coverage variety play important roles in different allergies. It goes without saying that humid climate and herbal coverage variety in different seasons especially spring cause most allergies in human beings in Iran and India that are discussed in this article. These two countries are good choices for allergy prevalence because of their condition, various herbal coverage, human and animal factors. Hay fever is one of the allergies, although the reasons of its prevalence are unknown yet. It is one of the most popular allergies in Iran and India because of geographical, human, animal and herbal factors. Hay fever is on top of the list in these two countries. Significant point about these two countries is that herbal factor is the most important factor in prevalence of hay fever. Variety of herbal coverage especially in spring during herbal pollination is the main reason of hay fever prevalence in these two countries. Based on the research result of Pharmacognosy and Phytochemistry, pollination of some plants in spring is major reason of hay fever prevalence in these countries. If airborne pollens in pollination season enter the human body through air, they will cause allergic reactions in eyes, nasal mucosa, lungs, and respiratory system, and if these particles enter the body of potential person through food, they will cause allergic reactions in mouth, stomach, and other digestive systems. Occasionally, chemical materials produced by human body such as Histamine cause problems like: developing of nasal polyps, nasal blockage, sleep disturbance, risk of asthma developing, blood vasodilation, sneezing, eye tears, itching and swelling of eyes and nasal mucosa, Urticaria, decrease in blood pressure, and rarely trauma, anesthesia, anaphylaxis and finally death. This article is going to study the reasons of hay fever prevalence in Iran and India and presents prevention and treatment Method from Phytochemistry and Pharmocognocy point of view by using local natural products in these two countries.

Keywords: hay fever, India, Iran, natural treatment, phytochemistry

Procedia PDF Downloads 141

Authors: Khatia Tavidashvili, Nino Jamrishvili, Valerian Omsarashvili

Abstract:

Studies of the vertical distribution of the air temperature in the atmosphere have great value for the solution of different problems of meteorology and climatology (meteorological forecast of showers, thunderstorms, and hail, weather modification, estimation of climate change, etc.). From the end of May 2015 in Kakheti after 25-year interruption, the work of anti-hail service was restored. Therefore, in connection with climate change, the need for the detailed study of the contemporary regime of the vertical distribution of the air temperature above this territory arose. In particular, the indicated information is necessary for the optimum selection of rocket means with the works on the weather modification (fight with the hail, the regulation of atmospheric precipitations, etc.). Construction of the detailed maps of the potential damage distribution of agricultural crops from the hail, etc. taking into account the dimensions of hailstones in the clouds according to the data of radar measurements and height of locality are the most important factors. For now, in Georgia, there is no aerological probing of atmosphere. To solve given problem we processed information about air temperature profiles above Telavi, at 27 km above earth's surface. Information was gathered during four observation time (4, 10, 16, 22 hours with local time. After research, we found vertical distribution of the average monthly values of the air temperature above Kakheti in ‎2012-2017 from January to December. Research was conducted from 0.543 to 27 km above sea level during four periods of research. In particular, it is obtained: -during January the monthly average air temperature linearly diminishes with 2.6 °C on the earth's surface to -57.1 °C at the height of 10 km, then little it changes up to the height of 26 km; the gradient of the air temperature in the layer of the atmosphere from 0.543 to 8 km - 6.3 °C/km; height of zero isotherm - is 1.33 km. -during July the air temperature linearly diminishes with 23.5 °C to -64.7 °C at the height of 17 km, then it grows to -47.5 °C at the height of 27 km; the gradient of the air temperature of - 6.1 °C/km; height of zero isotherm - is 4.39 km, which on 0.16 km is higher than in the sixties of past century.

Keywords: hail, Kakheti, meteorology, vertical distribution of the air temperature

Procedia PDF Downloads 145

Authors: Pedro B. Antunes, Paulo J. Ramísio

Abstract:

Road runoff is a linear source of diffuse pollution that can cause significant environmental impacts. During rainfall events, pollutants from both stationary and mobile sources, which have accumulated on the road surface, are dragged through the superficial runoff. Road runoff in coastal zones may present high levels of salinity and chlorides due to the proximity of the sea and transported marine aerosols. Appearing to be correlated to this process, organic matter concentration may also be significant. This study assesses this phenomenon with the purpose of identifying the relationships between monitored water quality parameters and intrinsic site variables. To achieve this objective, an extensive monitoring program was conducted on a Portuguese coastal highway. The study included thirty rainfall events, in different weather, traffic and salt deposition conditions in a three years period. The evaluations of various water quality parameters were carried out in over 200 samples. In addition, the meteorological, hydrological and traffic parameters were continuously measured. The salt deposition rates (SDR) were determined by means of a wet candle device, which is an innovative feature of the monitoring program. The SDR, variable throughout the year, appears to show a high correlation with wind speed and direction, but mostly with wave propagation, so that it is lower in the summer, in spite of the favorable wind direction in the case study. The distance to the sea, topography, ground obstacles and the platform altitude seems to be also relevant. It was confirmed the high salinity in the runoff, increasing the concentration of the water quality parameters analyzed, with significant amounts of seawater features. In order to estimate the correlations and patterns of different water quality parameters and variables related to weather, road section and salt deposition, the study included exploratory data analysis using different techniques (e.g. Pearson correlation coefficients, Cluster Analysis and Principal Component Analysis), confirming some specific features of the investigated road runoff. Significant correlations among pollutants were observed. Organic matter was highlighted as very dependent of salinity. Indeed, data analysis showed that some important water quality parameters could be divided into two major clusters based on their correlations to salinity (including organic matter associated parameters) and total suspended solids (including some heavy metals). Furthermore, the concentrations of the most relevant pollutants seemed to be very dependent on some meteorological variables, particularly the duration of the antecedent dry period prior to each rainfall event and the average wind speed. Based on the results of a monitoring case study, in a coastal zone, it was proven that SDR, associated with the hydrological characteristics of road runoff, can contribute for a better knowledge of the runoff characteristics, and help to estimate the specific nature of the runoff and related water quality parameters.

Keywords: coastal zones, monitoring, road runoff pollution, salt deposition

Procedia PDF Downloads 213

Authors: S. Al-Fadala, J. Chakkamalayath, S. Al-Bahar, A. Al-Aibani, S. Ahmed

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Globally, the use of pozzolan in blended cement is gaining great interest due to the desirable effect of pozzolan from the environmental and energy conservation standpoint and the technical benefits they provide to the performance of cement. The deterioration of concrete structures in the marine environment and extreme climates demand the use of pozzolana cement in concrete construction in the Gulf region. Also, natural sources of cement clinker materials are limited in the Gulf region, and cement industry imports the raw materials for the production of Portland cement, resulting in an increase in the greenhouse gas effect due to the CO₂ emissions generated from transportation. Even though the Gulf region has vast deposits of natural pozzolana, it is not explored properly for the production of high performance concrete. Hence, an optimum use of regionally available natural pozzolana for the production of blended cement can result in sustainable construction. This paper investigates the effect of incorporating natural pozzolan sourced from the Gulf region on the performance of blended cement in terms of heat evolution and strength development. For this purpose, a locally produced Ordinary Portland Cement (OPC) and pozzolan-incorporated blended cements containing different amounts of natural pozzolan (volcanic ash) were prepared on laboratory scale. The strength development and heat evolution were measured and quantified. Promising results of strength development were obtained for blends with the percentages of Volcanic Ash (VA) replacement varying from 10 to 30%. Results showed that the heat of hydration decreased with increase in percentage of replacement of OPC with VA, indicating increased retardation in hydration due to the addition of VA. This property could be used in mass concreting in which a reduction in heat of hydration is required to reduce cracking in concrete, especially in hot weather concreting.

Keywords: blended cement, hot weather, hydration, volcanic ash

Procedia PDF Downloads 301

Authors: Kasif Khan, Azlan Allahwala, Moiz Ali, Hasan Lodhi, Umer Amjad

Abstract:

The number of runs scored by batsmen and wickets taken by bowlers serves as a natural way of quantifying the performance of a cricketer. Traditionally the batsmen and bowlers are rated on their batting or bowling average respectively. However, in a game like Cricket, it is not sufficient to evaluate performance on the basis of average. The biasness in selecting batsman and bowler on the basis of their past performance. The objective is to predict the best player and comparing their performance on the basis of venue, opponent, weather, and particular position. On the basis of predictions and analysis, and comparison the best team is selected for next upcoming series of Pakistan. The system is based and will be built to aid analyst in finding best possible team combination of Pakistan for a particular match and by providing them with advisories so that they can select the best possible team combination. This will also help the team management in identifying a perfect batting order and the bowling order for each match.

Keywords: data analysis, Pakistan cricket players, quantifying individual performance, cricket

Procedia PDF Downloads 271

Authors: Leen Al Kassab, Sarah Johns, Helen Noble, Nawal Nour, Elizabeth Janiak, Sarrah Shahawy

Abstract:

Introduction: Immigrant and refugee women experience disparities in sexual and reproductive health (SRH) outcomes, partially as a result of barriers to SRH literacy and to regular healthcare access and engagement. Despite the existing data highlighting growing needs for culturally relevant and structurally competent care, interventions are scarce and not well-documented. Methods: In this IRB-approved study, we used a community-based participatory research approach, with the assistance of a community advisory board, to conduct a qualitative needs assessment of SRH knowledge and service engagement with immigrant and refugee women from Africa or the Middle East and currently residing in Boston. We conducted a total of nine focus group discussions (FGDs) in partnership with medical, community, and religious centers, in six languages: Arabic, English, French, Somali, Pashtu, and Dari. A total of 44 individuals participated. We explored migrant and refugee women’s current and evolving SRH care needs and gaps, specifically related to the development of interventions and clinical best practices targeting SRH literacy, healthcare engagement, and informed decision-making. Recordings of the FGDs were transcribed verbatim and translated by interpreter services. We used open coding with multiple coders who resolved discrepancies through consensus and iteratively refined our codebook while coding data in batches using Dedoose software. Results: Participants reported immigrant adaptation experiences, discrimination, and feelings of trust, autonomy, privacy, and connectedness to family, community, and the healthcare system as factors surrounding SRH knowledge and needs. The context of previously learned SRH knowledge was commonly noted to be in schools, at menstruation, before marriage, from family members, partners, friends, and online search engines. Common themes included empowering strength drawn from religious and cultural communities, difficulties bridging educational gaps with their US- born daughters, and a desire for more SRH education from multiple sources, including family, health care providers, and religious experts & communities. Regarding further SRH education, participants’ preferences varied regarding ideal platform (virtual vs. in-person), location (in religious and community centers or not), smaller group sizes, and the involvement of men. Conclusions: Based on these results, empowering SRH initiatives should include both community and religious center-based, as well as clinic-based, interventions. Interventions should be composed of frequent educational workshops in small groups involving age-grouped women, daughters, and (sometimes) men, tailored SRH messaging, and the promotion of culturally, religiously, and linguistically competent care.

Keywords: community, immigrant, religion, sexual & reproductive health, women's health

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Authors: Y. Bhatt, N. Ghosh, N. Tiwari

Abstract:

Acreage response function are modeled taking account of expected harvest prices, weather related variables and other non-price variables allowing for partial adjustment possibility. At the outset, based on the literature on price expectation formation, we explored suitable formulations for estimating the farmer’s expected prices. Assuming that farmers form expectations rationally, the prices of food and biofuel crops are modeled using time-series methods for possible ARCH/GARCH effects to account for volatility. The prices projected on the basis of the models are then inserted to proxy for the expected prices in the acreage response functions. Food crop acreages in different growing states are found sensitive to their prices relative to those of one or more of the biofuel crops considered. The required percentage improvement in food crop yields is worked to offset the acreage loss.

Keywords: acreage response function, biofuel, food security, sustainable development

Procedia PDF Downloads 278

Authors: Javad Sadidi, Ehsan Babaei, Hani Rezayan

Abstract:

The advent of web 2.0 gives a possibility to scale down the costs of data collection and mapping, specifically if the process is done by volunteers. Every volunteer can be thought of as a free and ubiquitous sensor to collect spatial, descriptive as well as multimedia data for tourist services. The lack of large-scale information, such as real-time climate and weather conditions, population density, and other related data, can be considered one of the important challenges in developing countries for tourists to make the best decision in terms of time and place of travel. The current research aims to design and implement a spatiotemporal web map service using volunteer-submitted data. The service acts as a tourist-guide service in which tourists can search interested places based on their requested time for travel. To design the service, three tiers of architecture, including data, logical processing, and presentation tiers, have been utilized. For implementing the service, open-source software programs, client and server-side programming languages (such as OpenLayers2, AJAX, and PHP), Geoserver as a map server, and Web Feature Service (WFS) standards have been used. The result is two distinct browser-based services, one for sending spatial, descriptive, and multimedia volunteer data and another one for tourists and local officials. Local official confirms the veracity of the volunteer-submitted information. In the tourist interface, a spatiotemporal search engine has been designed to enable tourists to find a tourist place based on province, city, and location at a specific time of interest. Implementing the tourist-guide service by this methodology causes the following: the current tourists participate in a free data collection and sharing process for future tourists, a real-time data sharing and accessing for all, avoiding a blind selection of travel destination and significantly, decreases the cost of providing such services.

Keywords: VGI, tourism, spatiotemporal, browser-based, web mapping

Procedia PDF Downloads 63

Authors: Lubna Eljabu, Mohammad Etemad, Stan Matwin

Abstract:

Port authorities have many challenges in congested ports to allocate their resources to provide a safe and secure loading/ unloading procedure for cargo vessels. Selecting a destination port is the decision of a vessel master based on many factors such as weather, wavelength and changes of priorities. Having access to a tool which leverages AIS messages to monitor vessel’s movements and accurately predict their next destination port promotes an effective resource allocation process for port authorities. In this research, we propose a method, namely, Reference Route of Trajectory (RRoT) to assist port authorities in predicting inflow and outflow traffic in their local environment by monitoring Automatic Identification System (AIS) messages. Our RRoT method creates a reference route based on historical AIS messages. It utilizes some of the best trajectory similarity measure to identify the destination of a vessel using their recent movement. We evaluated five different similarity measures such as Discrete Fr´echet Distance (DFD), Dynamic Time Warping (DTW), Partial Curve Mapping (PCM), Area between two curves (Area) and Curve length (CL). Our experiments show that our method identifies the destination port with an accuracy of 98.97% and an fmeasure of 99.08% using Dynamic Time Warping (DTW) similarity measure.

Keywords: spatial temporal data mining, trajectory mining, trajectory similarity, resource optimization

Procedia PDF Downloads 90

Authors: Getaneh Berie Tarekegn

Abstract:

A precise localization system is crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. The most common method for providing continuous positioning services in outdoor environments is by using a global navigation satellite system (GNSS). Due to nonline-of-sight, multipath, and weather conditions, GNSS systems do not perform well in dense urban, urban, and suburban areas.This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a novel semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. We also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 39 cm, and more than 90% of the errors are less than 82 cm. That is, numerical results proved that, in comparison to traditional methods, the proposed SRCLoc method can significantly improve positioning performance and reduce radio map construction costs.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

Procedia PDF Downloads 43