Search results for: spatial rainfall prediction

Authors: Lotsmart Fonjong

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The livelihood of rural communities in the West African state of Cameroon, which is largely dictated by natural forces (rainfall, temperatures, and soil), is today threatened by climate change and armed conflict. This paper investigates the extent to which rural communities are aware of climate change, how their perceptions of changes across different agro-ecological zones have impacted farming practices, output, and lifestyles, on the one hand, and the extent to which local armed conflicts are confounding their efforts and adaptation abilities. The paper is based on a survey conducted among small farmers in selected localities within the forest and savanna ecological zones of the conflict-ridden Northwest and Southwest Cameroon. Attention is paid to farmers’ gender, scale, and type of farming. Farmers’ perception of/and response to climate change are analysed alongside local rainfall and temperature data and mobilization for climate justice. Findings highlight the fact that farmers’ perception generally corroborates local climatic data. Climatic instability has negatively affected farmers’ output, food prices, standards of living, and food security. However, the vulnerability of the population varies across ecological zones, gender, and crop types. While these factors also account for differences in local response and adaptation to climate change, ongoing armed conflicts in these regions have further complicated opportunities for climate-driven agricultural innovations, inputs, and exchange of information among farmers. This situation underlines how poor communities, as victims, are forced into many complex problems outsider their making. It is therefore important to mainstream farmers’ perceptions and differences into policy strategies that consider both climate change and Anglophone conflict as national security concerns foe sustainable development in Cameroon.

Keywords: adaptation policies, climate change, conflict, small farmers, cameroon

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Authors: Seung Hwan Park, Cheng-Sool Park, Jun Seok Kim, Youngji Yoo, Daewoong An, Jun-Geol Baek

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Depending on the big data analysis becomes important, yield prediction using data from the semiconductor process is essential. In general, yield prediction and analysis of the causes of the failure are closely related. The purpose of this study is to analyze pattern affects the final test results using a die map based clustering. Many researches have been conducted using die data from the semiconductor test process. However, analysis has limitation as the test data is less directly related to the final test results. Therefore, this study proposes a framework for analysis through clustering using more detailed data than existing die data. This study consists of three phases. In the first phase, die map is created through fail bit data in each sub-area of die. In the second phase, clustering using map data is performed. And the third stage is to find patterns that affect final test result. Finally, the proposed three steps are applied to actual industrial data and experimental results showed the potential field application.

Keywords: die-map clustering, feature extraction, pattern recognition, semiconductor manufacturing process

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Authors: Zou Yihui

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The integration of the traditional landscape of the ancient city and the poet's emotions and symbolization into ancient poetry is the unique cultural gene and spiritual core of the historical city, and re-understanding the historical landscape pattern from the poetry is conducive to continuing the historical city context and improving the current situation of the gradual decline of the poetry of the modern historical urban landscape. Starting from Tang poetry uses semantic analysis methods、combined with text mining technology, entry mining, word frequency analysis, and cluster analysis of the landscape information of Tang Chang'an City were carried out, and the method framework for analyzing the urban landscape form based on poetry text was constructed. Nearly 160 poems describing the landscape of Tang Chang'an City were screened, and the poetic landscape characteristics of Tang Chang'an City were sorted out locally in order to combine with modern urban spatial development to continue the urban spatial context.

Keywords: Tang Chang'an City, poetic texts, semantic analysis, historical landscape

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Authors: J. Balaraju, M. Govinda Raj, C. S. N. Murthy

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Every industry is constantly looking for enhancement of its day to day production and productivity. This can be possible only by maintaining the men and machinery at its adequate level. Prediction of performance characteristics plays an important role in performance evaluation of the equipment. Analytical and statistical approaches will take a bit more time to solve complex problems such as performance estimations as compared with software-based approaches. Keeping this in view the present study deals with an Artificial Neural Network (ANN) modelling of a Load-Haul-Dump (LHD) machine to predict the performance characteristics such as reliability, availability and preventive maintenance (PM). A feed-forward-back-propagation ANN technique has been used to model the Levenberg-Marquardt (LM) training algorithm. The performance characteristics were computed using Isograph Reliability Workbench 13.0 software. These computed values were validated using predicted output responses of ANN models. Further, recommendations are given to the industry based on the performed analysis for improvement of equipment performance.

Keywords: load-haul-dump, LHD, artificial neural network, ANN, performance, reliability, availability, preventive maintenance

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Authors: Mohamed Aly, Aliaa Rehan Youssef, Emad Sawerees, Mounir Guirgis

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Relevance: Osteoarthritis (OA) is the most common degenerative disease seen in all populations. It causes disability and substantial socioeconomic burden. Evidence supports that exercise are the most effective conservative treatment for patients with OA. Therapists experience and clinical judgment play major role in exercise prescription and scientific evidence for this regard is lacking. The development of clinical prediction rules to identify patients who are most likely benefit from exercise may help solving this dilemma. Purpose: This study investigated whether body mass index and functional ability at baseline can predict patients’ response to a selected exercise program. Approach: Fifty-six patients, aged 35 to 65 years, completed an exercise program consisting of open kinetic chain strengthening and passive stretching exercises. The program was given for 3 sessions per week, 45 minutes per session, for 6 weeks Evaluation: At baseline and post treatment, pain severity was assessed using the numerical pain rating scale, whereas functional ability was being assessed by step test (ST), time up and go test (TUG) and 50 feet time walk test (50 FTW). After completing the program, global rate of change (GROC) score of greater than 4 was used to categorize patients as successful and non-successful. Thirty-eight patients (68%) had successful response to the intervention. Logistic regression showed that BMI and 50 FTW test were the only significant predictors. Based on the results, patients with BMI less than 34.71 kg/m2 and 50 FTW test less than 25.64 sec are 68% to 89% more likely to benefit from the exercise program. Conclusions: Clinicians should consider the described strengthening and flexibility exercise program for patents with BMI less than 34.7 Kg/m2 and 50 FTW faster than 25.6 seconds. The validity of these predictors should be investigated for other exercise.

Keywords: clinical prediction rule, knee osteoarthritis, physical therapy exercises, validity

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Authors: Sukhbir Singh

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This paper describes the modeling of novel solar air collector (NSAC) system by using artificial neural network (ANN) model. The objective of the study is to demonstrate the application of the ANN model to predict the performance of the NSAC with acetamide as a phase change material (PCM) storage. Input data set consist of time, solar intensity and ambient temperature wherever as outlet air temperature of NSAC was considered as output. Experiments were conducted between 9.00 and 24.00 h in June and July 2014 underneath the prevailing atmospheric condition of Kurukshetra (city of the India). After that, experimental results were utilized to train the back propagation neural network (BPNN) to predict the outlet air temperature of NSAC. The results of proposed algorithm show that the BPNN is effective tool for the prediction of responses. The BPNN predicted results are 99% in agreement with the experimental results.

Keywords: Evacuated tube solar air collector, Artificial neural network, Phase change material, solar air collector

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Authors: Tegan H. Emerson, Colin C. Olson, George Stantchev, Jason A. Edelberg, Michael Wilson

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Detecting small targets at range is difficult because there is not enough spatial information present in an image sub-region containing the target to use correlation-based methods to differentiate it from dynamic confusers present in the scene. Moreover, this lack of spatial information also disqualifies the use of most state-of-the-art deep learning image-based classifiers. Here, we use characteristics of target tracks extracted from video sequences as data from which to derive distinguishing topological features that help robustly differentiate targets of interest from confusers. In particular, we calculate persistent homology from time-delayed embeddings of dynamic statistics calculated from motion tracks extracted from a wide field-of-view video stream. In short, we use topological methods to extract features related to target motion dynamics that are useful for classification and disambiguation and show that small targets can be detected at range with high probability.

Keywords: motion tracks, persistence images, time-delay embedding, topological data analysis

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Authors: Jan Henrik Wosnitza

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The logistic regression has developed into a standard approach for estimating conditional probabilities in a wide range of applications including credit risk prediction. The article at hand contributes to the current literature on logistic regression fourfold: First, it is demonstrated that the binary logistic regression automatically meets its model assumptions under very general conditions. This result explains, at least in part, the logistic regression's popularity. Second, the requirement of homoscedasticity in the context of binary logistic regression is theoretically substantiated. The variances among the groups of defaulted and non-defaulted obligors have to be the same across the level of the aggregated default indicators in order to achieve linear logits. Third, this article sheds some light on the question why nonlinear logits might be superior to linear logits in case of a small amount of data. Fourth, an innovative methodology for estimating correlations between obligor-specific log-odds is proposed. In order to crystallize the key ideas, this paper focuses on the example of credit risk prediction. However, the results presented in this paper can easily be transferred to any other field of application.

Keywords: correlation, credit risk estimation, default correlation, homoscedasticity, logistic regression, nonlinear logistic regression

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Authors: Mohammad Reza Dahmardeh Ghaleno, Mohammad Nohtani, Saeedeh Khaledi

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Hydrological models are applied to simulation and prediction floods in watersheds. WetSpa is a distributed, continuous and physically model with daily or hourly time step that explains of precipitation, runoff and evapotranspiration processes for both simple and complex contexts. This model uses a modified rational method for runoff calculation. In this model, runoff is routed along the flow path using Diffusion-Wave Equation which depend on the slope, velocity and flow route characteristics. Garmabrood watershed located in Mazandaran province in Iran and passing over coordinates 53° 10´ 55" to 53° 38´ 20" E and 36° 06´ 45" to 36° 25´ 30"N. The area of the catchment is about 1133 km2 and elevations in the catchment range from 213 to 3136 m at the outlet, with average slope of 25.77 %. Results of the simulations show a good agreement between calculated and measured hydrographs at the outlet of the basin. Drawing upon Nash-Sutcliffe Model Efficiency Coefficient for calibration periodic model estimated daily hydrographs and maximum flow rate with an accuracy up to 61% and 83.17 % respectively.

Keywords: watershed simulation, WetSpa, runoff, flood prediction

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Authors: ȘercăIanu Mihai, Aldea Mihaela, Iacoboaea Cristina, Luca Oana, Nenciu Ioana

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The emergence and neutralization of illegal waste dumps represent a global concern for waste management ecosystems with a particularly pronounced impact on disadvantaged communities. All over the world, and in this particular case in Romania, a relevant number of people resided in houses lacking any legal forms such as land ownership documents or building permits. These areas are referred to as “informal settlements”. An increasing number of regions and cities in Romania are struggling to manage their waste dumps, especially in the context of increasing poverty and lack of regulation related to informal settlements. An example of such informal settlement can be found at the terminus of Bistra Street in Câlnic, which falls under the jurisdiction of the Municipality of Reșița in Caras Severin County. The article presents a case study that focuses on employing remote sensing techniques and spatial data to monitor and map illegal waste practices, with subsequent integration into a geographic information system tailored for the Reșița community. In addition, the paper outlines the steps involved in devising strategies aimed at enhancing waste management practices in disadvantaged areas, aligning with the shift toward a circular economy. Results presented in the paper contain a spatial mapping and visualization methodology calibrated with in situ data collection applicable for identifying illegal landfills. The emergence and neutralization of illegal dumps pose a challenge in the field of waste management. These approaches, which prove effective where conventional solutions have failed, need to be replicated and adopted more wisely.

Keywords: waste dumps, waste management, monitoring, GIS, informal settlements

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Authors: Raja Lahiani

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This study is concerned with scrutinising translations into English and French of references to locations in the desert of pre-Islamic Arabia. These references are used in the Source Text (ST) within a poetic image. Reference is made to the names of three different mountains in Arabia, namely Qatan, Sitar, and Yadhbul. As these mountains are referred to in the context of the poet’s description of the density and expansion of the clouds, it is crucial to know that while Sitar and Yadhbul are close to each other, Qatan is far away from them. This distance was functional for the poet to describe the expansion of the clouds. This reflects the spacious place (desert) he handled, and the fact that it was possible for him to physically see what he described. The purpose of this image is for the poet to communicate the vastness of the space he managed to see as he was in a moment of contemplation. Thus, knowledge of this characteristic about the setting is capital for the receiver to understand the communicative function of the verse. A corpus of eighteen translations is gathered. These vary between verse and prose renderings. The methodology adopted in this research work is comparative. Comparison is conducted at both the synchronic and diachronic levels; every translation shall be compared to the ST and then to previous translations. The comparative work will prove at the end that the translators who target historical facts do not necessarily succeed in preserving the image of the ST. It also proves that the more recent the translation is, the deeper the translator’s awareness is the link between imagery, setting, and point of view. Since the late eighteenth century and until nowadays, pre-Islamic poetry has been translated into Western languages. Translators differ as to motives, sources, priorities and intellectual backgrounds. A translator's skopoi undoubtedly affect the way s/he handles aspects of the ST. When it comes to culture-specific aspects and details related to setting, the problem is even more complex. Setting is a very important factor that reveals a great deal of the culture of pre-Islamic Arabia as this is remote in place, historical framework and literary tradition from its translators. History is present in pre-Islamic poetry, which justifies the important literature that has been written to extract information and data from it. These are imbedded not only by signalling given facts, events, and meditations but also by means of references to specific locations and landmarks that used to exist at the time. Spatial setting is an integral part of a literary text as it places it within its historical context. The importance of the translator’s awareness of spatial anthropological data before indulging in the process of translation is tested. This is also crucial in measuring the effect of setting loss and setting gain in translation. The findings of this research would ultimately evaluate the extent to which a comparative methodology is reliable in investigating the role of spatial setting awareness in translation.

Keywords: historical context, translation, comparative literature, spatial setting

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Authors: Tsitsi Nyukurayi Muparari, Walter Timo De Vries, Jaap Zevenbergen

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The nature of Land administration accommodates diversity in terms of both spatial data handling activities and the expertise involved, which supposedly aims to satisfy the unpredictable demands of land data and the diverse demands of the customers arising from the land. However, it is known that strategic decisions of restructuring are in most cases repelled in favour of complex structures that strive to accommodate professional diversity and diverse roles in the field of Land administration. Yet despite of this widely accepted knowledge, there is scanty theoretical knowledge concerning the psychological methodologies that can extract the deeper perceptions from the diverse spatial expertise in order to explain the invisible control arm of the polarised reception of the ideas of change. This paper evaluates Q methodology in the context of a cadastre and land registry merger (under one agency) using the Swedish cadastral system as a case study. Precisely, the aim of this paper is to evaluate the effectiveness of Q methodology towards modelling the diverse psychological perceptions of spatial professionals who are in a widely contested decision of merging the cadastre and land registry components of Land administration using the Swedish cadastral system as a case study. An empirical approach that is prescribed by Q methodology starts with the concourse development, followed by the design of statements and q sort instrument, selection of the participants, the q-sorting exercise, factor extraction by PQMethod and finally narrative development by logic of abduction. The paper uses 36 statements developed from a dominant competing value theory that stands out on its reliability and validity, purposively selects 19 participants to do the Qsorting exercise, proceeds with factor extraction from the diversity using varimax rotation and judgemental rotation provided by PQMethod and effect the narrative construction using the logic abduction. The findings from the diverse perceptions from cadastral professionals in the merger decision of land registry and cadastre components in Sweden’s mapping agency (Lantmäteriet) shows that focus is rather inclined on the perfection of the relationship between the legal expertise and technical spatial expertise. There is much emphasis on tradition, loyalty and communication attributes which concern the organisation’s internal environment rather than innovation and market attributes that reveals customer behavior and needs arising from the changing humankind-land needs. It can be concluded that Q methodology offers effective tools that pursues a psychological approach for the evaluation and gradations of the decisions of strategic change through extracting the local perceptions of spatial expertise.

Keywords: cadastre, factor extraction, land administration merger, land registry, q-methodology, rotation

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Authors: Misuk Kim, Seokho Kang, Jehyuk Lee, Hyunchang Cho, Sungzoon Cho

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In semiconductor manufacturing, virtual metrology (VM) refers to methods to predict properties of a wafer based on machine parameters and sensor data of the production equipment, without performing the (costly) physical measurement of the wafer properties (Wikipedia). Additional benefits include avoidance of human bias and identification of important factors affecting the quality of the process which allow improving the process quality in the future. It is however rare to find VM applied to other areas of manufacturing. In this work, we propose to use VM to copper clad laminate (CCL) manufacturing. CCL is a core element of a printed circuit board (PCB) which is used in smartphones, tablets, digital cameras, and laptop computers. The manufacturing of CCL consists of three processes: Treating, lay-up, and pressing. Treating, the most important process among the three, puts resin on glass cloth, heat up in a drying oven, then produces prepreg for lay-up process. In this process, three important quality factors are inspected: Treated weight (T/W), Minimum Viscosity (M/V), and Gel Time (G/T). They are manually inspected, incurring heavy cost in terms of time and money, which makes it a good candidate for VM application. We developed prediction models of the three quality factors T/W, M/V, and G/T, respectively, with process variables, raw material, and environment variables. The actual process data was obtained from a CCL manufacturer. A variety of variable selection methods and learning algorithms were employed to find the best prediction model. We obtained prediction models of M/V and G/T with a high enough accuracy. They also provided us with information on “important” predictor variables, some of which the process engineers had been already aware and the rest of which they had not. They were quite excited to find new insights that the model revealed and set out to do further analysis on them to gain process control implications. T/W did not turn out to be possible to predict with a reasonable accuracy with given factors. The very fact indicates that the factors currently monitored may not affect T/W, thus an effort has to be made to find other factors which are not currently monitored in order to understand the process better and improve the quality of it. In conclusion, VM application to CCL’s treating process was quite successful. The newly built quality prediction model allowed one to reduce the cost associated with actual metrology as well as reveal some insights on the factors affecting the important quality factors and on the level of our less than perfect understanding of the treating process.

Keywords: copper clad laminate, predictive modeling, quality control, virtual metrology

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Authors: Tamekloe Michael Kossivi, Kenichi Matsui

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Irrigation access is one of the essential rural development investment options that can significantly improve smallholder farmers’ agriculture productivity. Investment in irrigation infrastructural development to supply adequate water could improve food security, growth in income for farmers, poverty alleviation, and improve business and livelihood. This paper assesses smallholder farmers’ constraints and the needs to adopt small-scale irrigation for crops production in the South Tongu District of Ghana. The data collection involved database search, questionnaire survey, interview, and field work. The structured questionnaire survey was administered from September to November 2020 among 120 respondents in six purposively sampled irrigation communities in the District. The questions focused on small-scale irrigation development constraints and needs. As a result, we found that the respondents relied mainly on rainfall for agriculture production. They did not have adequate irrigation access. Even though the District is blessed with open arable lands and rich water sources for rice and vegetable production on a massive scale, water sources like the Lower Volta River, Tordzi River, and Avu Lagoon were not close enough to the respondents. The respondents faced inadequate credit support (100%), unreliable rainfall (76%), insufficient water supply (54%), and unreliable water delivery challenges on their farms (53%). Physical constraints for the respondents to adopt irrigation included flood (77%), drought (93%), inadequate irrigation technology (59%), and insufficient technical know-how (65%). Farmers were interested in investing in irrigation infrastructural development to enhance productivity on their farms only if they own the farmlands. External support from donors on irrigation systems did not allow smallholder farmers to control irrigation facilities.

Keywords: constraints, food security, needs, smallholder farmers, small-scale irrigation

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Authors: Ammar Alali, Mahmoud Abughaban

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Cost reduction and drilling optimization is the goal of many drilling operators. Historically, stuck pipe incidents were a major segment of non-productive time (NPT) associated costs. Traditionally, stuck pipe problems are part of the operations and solved post-sticking. However, the real key to savings and success is in predicting the stuck pipe incidents and avoiding the conditions leading to its occurrences. Previous attempts in stuck-pipe predictions have neglected the local geology of the problem. The proposed predictive tool utilizes geophysical data processing techniques and Machine Learning (ML) algorithms to predict drilling activities events in real-time using surface drilling data with minimum computational power. The method combines two types of analysis: (1) real-time prediction, and (2) cause analysis. Real-time prediction aggregates the input data, including historical drilling surface data, geological formation tops, and petrophysical data, from wells within the same field. The input data are then flattened per the geological formation and stacked per stuck-pipe incidents. The algorithm uses two physical methods (stacking and flattening) to filter any noise in the signature and create a robust pre-determined pilot that adheres to the local geology. Once the drilling operation starts, the Wellsite Information Transfer Standard Markup Language (WITSML) live surface data are fed into a matrix and aggregated in a similar frequency as the pre-determined signature. Then, the matrix is correlated with the pre-determined stuck-pipe signature for this field, in real-time. The correlation used is a machine learning Correlation-based Feature Selection (CFS) algorithm, which selects relevant features from the class and identifying redundant features. The correlation output is interpreted as a probability curve of stuck pipe incidents prediction in real-time. Once this probability passes a fixed-threshold defined by the user, the other component, cause analysis, alerts the user of the expected incident based on set pre-determined signatures. A set of recommendations will be provided to reduce the associated risk. The validation process involved feeding of historical drilling data as live-stream, mimicking actual drilling conditions, of an onshore oil field. Pre-determined signatures were created for three problematic geological formations in this field prior. Three wells were processed as case studies, and the stuck-pipe incidents were predicted successfully, with an accuracy of 76%. This accuracy of detection could have resulted in around 50% reduction in NPT, equivalent to 9% cost saving in comparison with offset wells. The prediction of stuck pipe problem requires a method to capture geological, geophysical and drilling data, and recognize the indicators of this issue at a field and geological formation level. This paper illustrates the efficiency and the robustness of the proposed cross-disciplinary approach in its ability to produce such signatures and predicting this NPT event.

Keywords: drilling optimization, hazard prediction, machine learning, stuck pipe

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Authors: Subhamita Chakraborty, Shubhabrata Datta, Sujay Kumar Mukherjea, Partha Protim Chattopadhyay

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Manufacturing of multiphase high strength steel in hot strip mill have drawn significant attention due to the possibility of forming low temperature transformation product of austenite under continuous cooling condition. In such endeavor, reliable prediction of temperature profile of hot strip coil is essential in order to accesses the evolution of microstructure at different location of hot strip coil, on the basis of corresponding Continuous Cooling Transformation (CCT) diagram. Temperature distribution profile of the hot strip coil has been determined by using finite volume method (FVM) vis-à-vis finite difference method (FDM). It has been demonstrated that FVM offer greater computational reliability in estimation of contact pressure distribution and hence the temperature distribution for curved and irregular profiles, owing to the flexibility in selection of grid geometry and discrete point position, Moreover, use of finite volume concept allows enforcing the conservation of mass, momentum and energy, leading to enhanced accuracy of prediction.

Keywords: simulation, modeling, thermal analysis, coil cooling, contact pressure, finite volume method

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Authors: L. Nahimana

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The eastern edge of the East African Rift in Burundi shows many mass movement phenomena corresponding to landslides, mudflow, debris flow, spectacular erosion (mega-gully), flash floods and alluvial deposits. These phenomena usually occur during the rainy season. Their extent and consecutive damages vary widely. To manage these phenomena, it is necessary to adopt a methodological approach of their mapping with a structured database. The elements for this database are: three-dimensional extent of the phenomenon, natural causes and conditions (geological lithology, slope, weathering depth and products, rainfall patterns, natural environment) and the anthropogenic factors corresponding to the various human activities. The extent of the area provides information about the possibilities and opportunities for mitigation technique. The lithological nature allows understanding the influence of the nature of the rock and its structure on the intensity of the weathering of rocks, as well as the geotechnical properties of the weathering products. The slope influences the land stability. The intensity of annual, monthly and daily rainfall helps to understand the conditions of water saturation of the terrains. Certain natural circumstances such as the presence of streams and rivers promote foot slope erosion and thus the occurrence and activity of mass movements. The construction of some infrastructures such as new roads and agglomerations deeply modify the flow of surface and underground water followed by mass movements. Using geospatial data selected on the East African Rift in Burundi, it is presented case of mass movements illustrating the nature, importance, various factors and the extent of the damages. An analysis of these elements for each hazard can guide the options for mitigation of the phenomenon and its consequences.

Keywords: mass movement, landslide, mudflow, debris flow, spectacular erosion, mega-gully, flash flood, alluvial deposit, East African rift, Burundi

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Authors: Samuel O. Akande

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The human-induced climate change is likely to have severe consequences on forest ecosystems in Nigeria. Recent discussions and emphasis on issues concerning the environment justify the need for this research which examined deforestation monitoring in Oban Forest, Nigeria using Remote Sensing techniques. The Landsat images from TM (1986), ETM+ (2001) and OLI (2015) sensors were obtained from Landsat online archive and processed using Erdas Imagine 2014 and ArcGIS 10.3 to obtain the land use/land cover and Normalized Differential Vegetative Index (NDVI) values. Ground control points of deforested areas were collected for validation. It was observed that the forest cover decreased in area by about 689.14 km² between 1986 and 2015. The NDVI was used to determine the vegetation health of the forest and its implications on agricultural sustainability. The result showed that the total percentage of the healthy forest cover has reduced to about 45.9% from 1986 to 2015. The results obtained from analysed questionnaires shown that there was a positive correlation between the causes and effects of deforestation in the study area. The coefficient of determination value was calculated as R² ≥ 0.7, to ascertain the level of anthropogenic activities, such as fuelwood harvesting, intensive farming, and logging, urbanization, and engineering construction activities, responsible for deforestation in the study area. Similarly, temperature and rainfall data were obtained from Nigerian Meteorological Agency (NIMET) for the period of 1986 to 2015 in the study area. It was observed that there was a significant increase in temperature while rainfall decreased over the study area. Responses from the administered questionnaires also showed that futile destruction of forest ecosystem in Oban forest could be reduced to its barest minimum if fuelwood harvesting is disallowed. Thus, the projected impacts of climate change on Nigeria’s forest ecosystems and environmental stability is better imagined than experienced.

Keywords: deforestation, ecosystems, normalized differential vegetative index, sustainability

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Authors: Claudineia Brazil, Elison Eduardo Jardim Bierhals, Luciane Teresa Salvi, Rafael Haag

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Renewable energy sources are dependent on climatic variability, so for adequate energy planning, observations of the meteorological variables are required, preferably representing long-period series. Despite the scientific and technological advances that meteorological measurement systems have undergone in the last decades, there is still a considerable lack of meteorological observations that form series of long periods. The reanalysis is a system of assimilation of data prepared using general atmospheric circulation models, based on the combination of data collected at surface stations, ocean buoys, satellites and radiosondes, allowing the production of long period data, for a wide gamma. The third generation of reanalysis data emerged in 2010, among them is the Climate Forecast System Reanalysis (CFSR) developed by the National Centers for Environmental Prediction (NCEP), these data have a spatial resolution of 0.50 x 0.50. In order to overcome these difficulties, it aims to evaluate the performance of solar radiation estimation through alternative data bases, such as data from Reanalysis and from meteorological satellites that satisfactorily meet the absence of observations of solar radiation at global and/or regional level. The results of the analysis of the solar radiation data indicated that the reanalysis data of the CFSR model presented a good performance in relation to the observed data, with determination coefficient around 0.90. Therefore, it is concluded that these data have the potential to be used as an alternative source in locations with no seasons or long series of solar radiation, important for the evaluation of solar energy potential.

Keywords: climate, reanalysis, renewable energy, solar radiation

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Authors: Barbara Roosen, Oswald Devisch

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Spatial projects are often contested. Despite participatory trajectories in official spatial development processes, citizens engage often by their power to say no. Participatory mapping helps to produce more legible and democratic ways of decision-making. It has proven its value in producing a multitude of knowledges and views, for individuals and community groups and local stakeholders to imagine desired and undesired futures and to give them the rhetorical power to present their views throughout the development process. From this perspective, mapping works as a social process in which individuals and groups share their knowledge, learn from each other and negotiate their relationship with each other as well as with space and power. In this way, these processes eventually aim to activate communities to intervene in cooperation in real problems. However, these are fragile and bumpy processes, sometimes leading to (local) conflict and intractable situations. Heterogeneous subjectivities and knowledge that become visible during the mapping process and which are contested by members of the community, is often the first trigger. This paper discusses a participatory mapping project conducted in a residential subdivision in Flanders to provide a deeper understanding of how or under which conditions the mapping process could moderate discordant situations amongst inhabitants, local organisations and local authorities, towards a more constructive outcome. In our opinion, this implies a thorough documentation and presentation of the different steps of the mapping process to design and moderate an open and transparent dialogue. The mapping project ‘Make the Neighbourhood’, is set up in the aftermath of a socio-spatial design intervention in the neighbourhood that led to polarization within the community. To start negotiation between the diverse claims that came to the fore, we co-create a desired future map of the neighbourhood together with local organisations and inhabitants as a way to engage them in the development of a new spatial development plan for the area. This mapping initiative set up a new ‘common’ goal or concern, as a first step to bridge the gap that we experienced between different sociocultural groups, bottom-up and top-down initiatives and between professionals and non-professionals. An atlas of elements (materials), an atlas of actors with different roles and an atlas of ways of cooperation and organisation form the work and building material of the future neighbourhood map, assembled in two co-creation sessions. Firstly, we will consider how the mapping procedures articulate the plurality of claims and agendas. Secondly, we will elaborate upon how social relations and spatialities are negotiated and reproduced during the different steps of the map making. Thirdly, we will reflect on the role of the rules, format, and structure of the mapping process in moderating negotiations between much divided claims. To conclude, we will discuss the challenges of visualizing the different steps of mapping process as a strategy to moderate tense negotiations in a more constructive direction in the context of spatial development processes.

Keywords: conflict, documentation, participatory mapping, residential subdivision

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Authors: Pedro B. Antunes, Paulo J. Ramísio

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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

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Authors: Amin Salighehdar, Ziwen Ye, Mingzhe Liu, Ionut Florescu, Alan F. Blumberg

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Storm surge is an abnormal water level caused by a storm. Accurate prediction of a storm surge is a challenging problem. Researchers developed various ensemble modeling techniques to combine several individual forecasts to produce an overall presumably better forecast. There exist some simple ensemble modeling techniques in literature. For instance, Model Output Statistics (MOS), and running mean-bias removal are widely used techniques in storm surge prediction domain. However, these methods have some drawbacks. For instance, MOS is based on multiple linear regression and it needs a long period of training data. To overcome the shortcomings of these simple methods, researchers propose some advanced methods. For instance, ENSURF (Ensemble SURge Forecast) is a multi-model application for sea level forecast. This application creates a better forecast of sea level using a combination of several instances of the Bayesian Model Averaging (BMA). An ensemble dressing method is based on identifying best member forecast and using it for prediction. Our contribution in this paper can be summarized as follows. First, we investigate whether the ensemble models perform better than any single forecast. Therefore, we need to identify the single best forecast. We present a methodology based on a simple Bayesian selection method to select the best single forecast. Second, we present several new and simple ways to construct ensemble models. We use correlation and standard deviation as weights in combining different forecast models. Third, we use these ensembles and compare with several existing models in literature to forecast storm surge level. We then investigate whether developing a complex ensemble model is indeed needed. To achieve this goal, we use a simple average (one of the simplest and widely used ensemble model) as benchmark. Predicting the peak level of Surge during a storm as well as the precise time at which this peak level takes place is crucial, thus we develop a statistical platform to compare the performance of various ensemble methods. This statistical analysis is based on root mean square error of the ensemble forecast during the testing period and on the magnitude and timing of the forecasted peak surge compared to the actual time and peak. In this work, we analyze four hurricanes: hurricanes Irene and Lee in 2011, hurricane Sandy in 2012, and hurricane Joaquin in 2015. Since hurricane Irene developed at the end of August 2011 and hurricane Lee started just after Irene at the beginning of September 2011, in this study we consider them as a single contiguous hurricane event. The data set used for this study is generated by the New York Harbor Observing and Prediction System (NYHOPS). We find that even the simplest possible way of creating an ensemble produces results superior to any single forecast. We also show that the ensemble models we propose generally have better performance compared to the simple average ensemble technique.

Keywords: Bayesian learning, ensemble model, statistical analysis, storm surge prediction

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Authors: Lazhar Belkhiri, Ammar Tiri, Lotfi Mouni

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Water resources present to the public order of the world a very important problem for the protection and management of water quality given the complexity of water quality data sets. In this study, the water quality index (WQI) and irrigation water quality index (IWQI) were calculated in order to evaluate the surface water quality for drinking and irrigation purposes based on nine hydrochemical parameters. In order to separate the variables that are the most responsible for the spatial differentiation, the discriminant analysis (DA) was applied. The results show that the surface water quality for drinking is poor quality and very poor quality based on WQI values, however, the values of IWQI reflect that this water is acceptable for irrigation with a restriction for sensitive plants. Consequently, the discriminant analysis DA method has shown that the following parameters pH, potassium, chloride, sulfate, and bicarbonate are significant discrimination between the different stations with the spatial variation of the surface water quality, therefore, the results obtained in this study provide very useful information to decision-makers

Keywords: surface water quality, drinking and irrigation purposes, water quality index, discriminant analysis

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Authors: Sagwata A. Manyike, Hulisani Magada

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The South African National Biodiversity Institute is in the process of developing a guideline which sets out how biodiversity can be incorporated into land use (zoning) schemes. South Africa promulgated its Spatial Planning and Land Use Management Act in 2015 and the act seeks, amongst other things, to bridge the gap between spatial planning and land use management within the country. In addition, the act requires local governments to develop wall-to-wall land use schemes for their entire jurisdictions as they had previously only developed them for their urban areas. At the same time, South Africa has a rich history of systematic conservation planning whereby Critical Biodiversity Areas and Ecological Support Areas have been spatially delineated at a scale appropriate for spatial planning and land use management at the scale of local government. South Africa is also in the process of spatially delineating ecological infrastructure which is defined as naturally occurring ecosystems which provide valuable services to people such as water and climate regulation, soil formation, disaster risk reduction, etc. The Biodiversity and Land Use Project, which is funded by the Global Environmental Facility through the United Nations Development Programme is seeking to explore ways in which biodiversity information and ecological infrastructure can be incorporated into the spatial planning and land use management systems of local governments. Towards this end, the Biodiversity and Land Use Project have developed a guideline which sets out how local governments can integrate biodiversity into their land-use schemes as a way of not only ensuring sustainable development but also as a way helping them prepare for climate change. In addition, by incorporating biodiversity into land-use schemes, the project is exploring new ways of protecting biodiversity through land use schemes. The Guideline for Incorporating Biodiversity into Land Use Schemes was developed as a response to the fact that the National Land Use Scheme Guidelines only indicates that local governments needed to incorporate biodiversity without explaining how this could be achieved. The Natioanl Guideline also failed to specify which biodiversity-related layers are compatible with which land uses or what the benefits of incorporating biodiversity into the schemes will be for that local government. The guideline, therefore, sets out an argument for why biodiversity is important in land management processes and proceeds to provide a step by step guideline for how schemes can integrate priority biodiversity layers. This guideline will further be added as an addendum to the National Land Use Guidelines. Although the planning act calls for local government to have wall to wall schemes within 5 years of its enactment, many municipalities will not meet this deadline and so this guideline will support them in the development of their new schemes.

Keywords: biodiversity, climate change, land use schemes, local government

Procedia PDF Downloads 152

Authors: A. O. Abulibdeh

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The aim of this study is to investigate and compare the changes in the Land Surface Temperature (LST) as a function of urbanization, particularly land use/land cover changes, in three cities in the UAE, mainly Abu Dhabi, Dubai, and Al Ain. The scale of this assessment will be at the macro- and micro-levels. At the macro-level, a comparative assessment will take place to compare between the four cities in the UAE. At the micro-level, the study will compare between the effects of different land use/land cover on the LST. This will provide a clear and quantitative city-specific information related to the relationship between urbanization and local spatial intra-urban LST variation in three cities in the UAE. The main objectives of this study are 1) to investigate the development of LST on the macro- and micro-level between and in three cities in the UAE over two decades time period, 2) to examine the impact of different types of land use/land cover on the spatial distribution of LST. Because these three cities are facing harsh arid climate, it is hypothesized that (1) urbanization is affecting and connected to the spatial changes in LST; (2) different land use/land cover have different impact on the LST; and (3) changes in spatial configuration of land use and vegetation concentration over time would control urban microclimate on a city scale and control macroclimate on the country scale. This study will be carried out over a 20-year period (1996-2016) and throughout the whole year. The study will compare between two distinct periods with different thermal characteristics which are the cool/cold period from November to March and warm/hot period between April and October. The best practice research method for this topic is to use remote sensing data to target different aspects of natural and anthropogenic systems impacts. The project will follow classical remote sensing and mapping techniques to investigate the impact of urbanization, mainly changes in land use/land cover, on LST. The investigation in this study will be performed in two stages. Stage one remote sensing data will be used to investigate the impact of urbanization on LST on a macroclimate level where the LST and Urban Heat Island (UHI) will be compared in the three cities using data from the past two decades. Stage two will investigate the impact on microclimate scale by investigating the LST and UHI using a particular land use/land cover type. In both stages, an LST and urban land cover maps will be generated over the study area. The outcome of this study should represent an important contribution to recent urban climate studies, particularly in the UAE. Based on the aim and objectives of this study, the expected outcomes are as follow: i) to determine the increase or decrease of LST as a result of urbanization in these four cities, ii) to determine the effect of different land uses/land covers on increasing or decreasing the LST.

Keywords: land use/land cover, global warming, land surface temperature, remote sensing

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Authors: Ondrej Slach, Jan Machacek, Jan Zenka, Lucie Hyllova, Petr Rumpel

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The aim of the paper is to identify and asses different trajectories of two fashionable urban policies –creative and smart cities– in specific post-socialistic context. Drawing on the case of Czechia, we employ the concept of policy mobility research. More specifically, we employ a discourse analysis in order to identify the so-called 'infrastructure' of both policies (such as principal actors, journals, conferences, events), with the special focus on 'agents of transfer' in a multiscale perspective. The preliminary results indicate faster and more aggressive spatial penetration of smart cities policy compared to creative cities policy in Czechia. Further, it seems that existed translation and implementation of smart cities policy into the national and urban context resulted in deliberated fragmented policy of smart cities in Czechia (pure technocratic view), which might be a threat for the future development of social sustainability, especially in cities that are facing increasing social polarisation. Last but not least, due to the fast spatial penetration of the concept and policies of smart cities, it seems that creative cities policy has almost been crowded out of the Czech urban agenda.

Keywords: policy mobility, smart cities, creative cities, Czechia

Procedia PDF Downloads 151

Authors: Chatrabhuti Pipop, Visessri Supattra, Charinpanitkul Tawatchai

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Durian is one of the top agricultural products exported by Thailand. There is the massive market potential for the durian industry. While the global demand for Thai durians, especially the demand from China, is very high, Thailand's durian supply is far from satisfying strong demand. Poor agricultural practices result in low yields and poor quality of fruit. Most irrigation systems currently used by the farmers are fixed schedule or fixed rates that ignore actual weather conditions and crop water requirements. In addition, the technologies emerging are too difficult and complex and prices are too high for the farmers to adopt and afford. Many farmers leave the durian trees to grow naturally. With improper irrigation and nutrient management system, durians are vulnerable to a variety of issues, including stunted growth, not flowering, diseases, and death. Technical development or research for durian is much needed to support the wellbeing of the farmers and the economic development of the country. However, there are a limited number of studies or development projects for durian because durian is a perennial crop requiring a long time to obtain the results to report. This study, therefore, aims to address the problem of durian production by developing an autonomous and precision irrigation system. The system is designed and equipped with an industrial programmable controller, a weather station, and a digital flow meter. Daily water requirements are computed based on weather data such as rainfall and evapotranspiration for daily irrigation with variable flow rates. A prediction model is also designed as a part of the system to enhance the irrigation schedule. Before the system was installed in the field, a simulation model was built and tested in a laboratory setting to ensure its accuracy. Water consumption was measured daily before and after the experiment for further analysis. With this system, the crop water requirement is precisely estimated and optimized based on the data from the weather station. Durian will be irrigated at the right amount and at the right time, offering the opportunity for higher yield and higher income to the farmers.

Keywords: Durian, precision irrigation, precision agriculture, smart farm

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Authors: Tea Poklepović, Zdravka Aljinović, Branka Marasović

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Due to the importance of yield curve and its estimation it is inevitable to have valid methods for yield curve forecasting in cases when there are scarce issues of securities and/or week trade on a secondary market. Therefore in this paper, after the estimation of weekly yield curves on Croatian financial market from October 2011 to August 2012 using Nelson-Siegel and Svensson models, yield curves are forecasted using Vector auto-regressive model and Neural networks. In general, it can be concluded that both forecasting methods have good prediction abilities where forecasting of yield curves based on Nelson Siegel estimation model give better results in sense of lower Mean Squared Error than forecasting based on Svensson model Also, in this case Neural networks provide slightly better results. Finally, it can be concluded that most appropriate way of yield curve prediction is neural networks using Nelson-Siegel estimation of yield curves.

Keywords: Nelson-Siegel Model, neural networks, Svensson Model, vector autoregressive model, yield curve

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Authors: Abrha Ybeyn Gebremedhn, Yitea Seneshaw Getahun, Alebachew Shumye Moges, Fikrey Tesfay

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Rainfed agriculture in Ethiopia has failed to produce enough food, to achieve the increasing demand for food. Pinpointing the appropriate site for rainwater harvesting (RWH) have a substantial contribution to increasing the available water and enhancing agricultural productivity. The current study related to the identification of the potential RWH sites was conducted at the Chacha watershed central highlands of Ethiopia which is endowed with rugged topography. The Geographic Information System with Analytical Hierarchy Process was used to generate the different maps for identifying appropriate sites for RWH. In this study, 11 factors that determine the RWH locations including slope, soil texture, runoff depth, land cover type, annual average rainfall, drainage density, lineament intensity, hydrologic soil group, antecedent moisture content, and distance to the roads were considered. The overall analyzed result shows that 10.50%, 71.10%, 17.90%, and 0.50% of the areas were found under highly, moderately, marginally suitable, and unsuitable areas for RWH, respectively. The RWH site selection was found highly dependent on a slope, soil texture, and runoff depth; moderately dependent on drainage density, annual average rainfall, and land use land cover; but less dependent on the other factors. The highly suitable areas for rainwater harvesting expansion are lands having a flat topography with a soil textural class of high-water holding capacity that can produce high runoff depth. The application of this study could be a baseline for planners and decision-makers and support any strategy adoption for appropriate RWH site selection.

Keywords: runoff depth, antecedent moisture condition, AHP, weighted overlay, water resource

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Authors: Prateeksha Mahamallik, Anjali Pal

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In the present study, Co(II)-adsolubilized surfactant modified alumina (SMA) was prepared, and methylene blue (MB) degradation was carried out on Co-SMA surface by visible light photo-Fenton process. The entire reaction proceeded on solid surface as MB was embedded on Co-SMA surface. The reaction followed zero order kinetics. Response surface methodology (RSM) and artificial neural network (ANN) were used for modeling the decolorization of MB by photo-Fenton process as a function of dose of Co-SMA (10, 20 and 30 g/L), initial concentration of MB (10, 20 and 30 mg/L), concentration of H2O2 (174.4, 348.8 and 523.2 mM) and reaction time (30, 45 and 60 min). The prediction capabilities of both the methodologies (RSM and ANN) were compared on the basis of correlation coefficient (R2), root mean square error (RMSE), standard error of prediction (SEP), relative percent deviation (RPD). Due to lower value of RMSE (1.27), SEP (2.06) and RPD (1.17) and higher value of R2 (0.9966), ANN was proved to be more accurate than RSM in order to predict decolorization efficiency.

Keywords: adsolubilization, artificial neural network, methylene blue, photo-fenton process, response surface methodology

Procedia PDF Downloads 241