Search results for: area flood likelihood model

Authors: Mostafa Aminzadeh, Min Deng

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Modeling loss data is an important part of actuarial science. Actuaries use models to predict future losses and manage financial risk, which can be beneficial for marketing purposes. In the insurance industry, small claims happen frequently while large claims are rare. Traditional distributions such as Normal, Exponential, and inverse-Gaussian are not suitable for describing insurance data, which often show skewness and fat tails. Several authors have studied classical and Bayesian inference for parameters of composite distributions, such as Exponential-Pareto, Weibull-Pareto, and Inverse Gamma-Pareto. These models separate small to moderate losses from large losses using a threshold parameter. This research introduces a computational approach using a nonlinear regression model for loss data that relies on multiple predictors. Simulation studies were conducted to assess the accuracy of the proposed estimation method. The simulations confirmed that the proposed method provides precise estimates for regression parameters. It's important to note that this approach can be applied to datasets if goodness-of-fit tests confirm that the composite distribution under study fits the data well. To demonstrate the computations, a real data set from the insurance industry is analyzed. A Mathematica code uses the Fisher information algorithm as an iteration method to obtain the maximum likelihood estimation (MLE) of regression parameters.

Keywords: maximum likelihood estimation, fisher scoring method, non-linear regression models, composite distributions

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Authors: Davie Hope Moyo

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The environment is a combination of two things: resources and hazards. One of the hazards that is a result of environmental changes is the occurrence of flooding. Floods are one of the disasters that are highly feared by people because they have a huge impact on the human population and their environment. In recent years, flooding disasters in the Nsanje district are increasing in both frequency and magnitude. This study aims to understand the root causes of this phenomenon. To understand the causes of flooding, this study focused on the case of TA Ndamera in the Nsanje district, southern Malawi. People in the Nsanje district face disruption in their day-to-day life because of floods that affect their communities. When floods happen, people lose their property, land, livestock, and even lives. The study was carried out in order to have a better understanding of the root causes of floods. The findings of this study may help the government and other development agencies to put in place mitigation measures that will make Nsanje District resilient to the occurrence of future flood hazards. Data was collected from the area of TA Ndamera in order to assess the causes of flooding in the district. Interviews, transect walks, and researcher observation was done to appreciate the topography of the district and evaluate other factors that are making the people become vulnerable to the impacts of flooding in the district. It was found that flooding in the district is mainly caused by heavy rainfall in the upper shire, settlements along river banks, deforestation, and the topography of the district in general. The research study ends by providing recommendation strategies that need to be put in place to increase the resilience of the communities to future flood hazards. The research recommends the development of indigenous knowledge systems to alert people of incoming floods, construction of evacuation centers to ease pressure on schools, savings, and insurance schemes, construction of dykes, desilting rivers, and afforestation.

Keywords: disaster causes, mitigation, safety measures, Nsanje Malawi

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Authors: Artur Cichowicz

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The last active mine in the Central Rand Goldfields area (50 km x 15 km) ceased operations in 2008. This resulted in the closure of the pumping stations, which previously maintained the underground water level in the mining voids. As a direct consequence of the water being allowed to flood the mine voids, seismic activity has increased directly beneath the populated area of Johannesburg. Monitoring of seismicity in the area has been on-going for over five years using the network of 17 strong ground motion sensors. The objective of the project is to improve strategies for mine closure. The evolution of the seismicity pattern was investigated in detail. Special attention was given to seismic source parameters such as magnitude, scalar seismic moment and static stress drop. Most events are located within historical mine boundaries. The seismicity pattern shows a strong relationship between the presence of the mining void and high levels of seismicity; no seismicity migration patterns were observed outside the areas of old mining. Seven years after the pumping stopped, the evolution of the seismicity has indicated that the area is not yet in equilibrium. The level of seismicity in the area appears to not be decreasing over time since the number of strong events, with Mw magnitudes above 2, is still as high as it was when monitoring began over five years ago. The average rate of seismic deformation is 1.6x1013 Nm/year. Constant seismic deformation was not observed over the last 5 years. The deviation from the average is in the order of 6x10^13 Nm/year, which is a significant deviation. The variation of cumulative seismic moment indicates that a constant deformation rate model is not suitable. Over the most recent five year period, the total cumulative seismic moment released in the Central Rand Basin was 9.0x10^14 Nm. This is equivalent to one earthquake of magnitude 3.9. This is significantly less than what was experienced during the mining operation. Characterization of seismicity triggered by a rising water level in the area can be achieved through the estimation of source parameters. Static stress drop heavily influences ground motion amplitude, which plays an important role in risk assessments of potential seismic hazards in inhabited areas. The observed static stress drop in this study varied from 0.05 MPa to 10 MPa. It was found that large static stress drops could be associated with both small and large events. The temporal evolution of the inter-event time provides an understanding of the physical mechanisms of earthquake interaction. Changes in the characteristics of the inter-event time are produced when a stress change is applied to a group of faults in the region. Results from this study indicate that the fluid-induced source has a shorter inter-event time in comparison to a random distribution. This behaviour corresponds to a clustering of events, in which short recurrence times tend to be close to each other, forming clusters of events.

Keywords: inter-event time, fluid induced seismicity, mine closure, spectral parameters of seismic source

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Authors: Sneha Thakur, Sanjeev Karmakar

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This paper proposed hybrid Particle Swarm Optimization (PSO) – Long-Short Term Memory (LSTM) model for groundwater level prediction. The evaluation of the performance is realized using the parameters: root mean square error (RMSE) and mean absolute error (MAE). Ground water level forecasting will be very effective for planning water harvesting. Proper calculation of water level forecasting can overcome the problem of drought and flood to some extent. The objective of this work is to develop a ground water level forecasting model using deep learning technique integrated with optimization technique PSO by applying 29 years data of Chhattisgarh state, In-dia. It is important to find the precise forecasting in case of ground water level so that various water resource planning and water harvesting can be managed effectively.

Keywords: long short-term memory, particle swarm optimization, prediction, deep learning, groundwater level

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Authors: Hyeon-Kyo Lim, Sang-Hun Byun

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Risk assessment is carried out in most industrial plants for accident prevention, but there exists insufficient data for statistical decision making. It is commonly said that risk can be expressed as a product of consequence and likelihood of a corresponding hazard factor. Eventually, therefore, risk assessment involves human decision making which cannot be objective per se. This study was carried out to comprehend perceptive characteristics of human beings in industrial plants. Subjects were shown a set of illustrations describing scenes of industrial plants, and were asked to assess the risk of each scene with not only linguistic variables but also numeric scores in the aspect of consequence and likelihood. After that, their responses were formulated as fuzzy membership functions, and compared with those of university students who had no experience of industrial works. The results showed that risk level of industrial workers were lower than those of any other groups, which implied that the workers might generally have a tendency to neglect more hazard factors in their work fields.

Keywords: fuzzy, hazard, linguistic variable, risk assessment

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Authors: Mara Getachew Zenebe, Luuk Fleskens, Abdu Obieda Ahmed

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This paper seeks to advance our scientific understanding of optimizing flood utilization in regions impacted by climate change, with a focus on enhancing agricultural productivity through effective irrigation management. The study was conducted as part of a three-year (2021 to 2023) USAID-supported initiative aimed at promoting Economic Growth and Peace in the Gash Agricultural Scheme (GAS), situated in Sudan's water-stressed Eastern region. GAS is the country's largest flood-irrigated scheme, covering 100,800 hectares of cultivable land, with a potential to provide the food security needs of over a quarter of a million agro-pastoral community members. GAS relies on the Gash River, which sources its water from high-intensity rainfall events in the highlands of Ethiopia and Eritrea. However, climate change and variations in these highlands have led to increased variability in the Gash River's flow. The study conducted water balance analyses based on a ten-year dataset of the annual Gash River flow, irrigated area; as well as the evapotranspiration demand of the major sorghum crop. Data collection methods included field measurements, surveys, remote sensing, and CropWat modelling. The water balance assessment revealed that the existing three-year rotation-based irrigation system management, capping cultivated land at 33,000 hectares annually, is excessively risk-averse. While this system reduced conflicts among the agro-pastoral communities by consistently delivering on the land promised to be annually cultivated, it also increased GAS's vulnerability to flood damage due to several reasons. The irrigation efficiency over the past decade was approximately 30%, leaving significant unharnessed floodwater that caused damage to infrastructure and agricultural land. The three-year rotation resulted in inadequate infrastructural maintenance, given the destructive nature of floods. Additionally, it led to infrequent land tillage, allowing the encroachment of mesquite trees hindering major sorghum crop growth. Remote sensing data confirmed that mesquite trees have overtaken 70,000 hectares in the past two decades, rendering them unavailable for agriculture. The water balance analyses suggest shifting to a two-year rotation, covering approximately 50,000 hectares annually while maintaining risk aversion. This shift could boost GAS's annual sorghum production by two-thirds, exceeding 850,000 tons. The scheme's efficiency can be further enhanced through low-cost on-farm interventions. Currently, large irrigation plots that range from 420 to 756 hectares are irrigated with limited water distribution guidance, leading to uneven irrigation. As demonstrated through field trials, implementing internal longitudinal bunds and horizontal deflector bunds can increase adequately irrigated parts of the irrigation plots from 50% to 80% and thus nearly double the sorghum yield to 2 tons per hectare while reducing the irrigation duration from 30 days to a maximum of 17 days. Flow measurements in 2021 and 2022 confirmed that these changes sufficiently meet the sorghum crop's water requirements, even with a conservative 60% field application efficiency assumption. These insights and lessons from the GAS on enhancing agricultural resilience and sustainability in the face of climate change are relevant to flood-based livelihood systems globally.

Keywords: climate change, irrigation management and productivity, variable flood flows, water balance analysis

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Authors: Michael Tilahun Mulugeta

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Pedestrians are the most vulnerable road users as they are more exposed to the risk of collusion. Pedestrian safety at road intersections still remains the most vital and yet unsolved issue in Addis Ababa, Ethiopia. One of the critical points in pedestrian safety is the occurrence of conflict between turning vehicle and pedestrians at un-signalized intersection. However, a better understanding of the factors that affect the likelihood of the conflicts would help provide direction for countermeasures aimed at reducing the number of crashes. This paper has sorted to explore a model to describe the relation between traffic conflicts and influencing factors using Multiple Linear regression methodology. In this research the main focus is to study the interaction of turning (left & right) vehicle with pedestrian at unsignalized intersections. The specific objectives also to determine factors that affect the number of potential conflicts and develop a model of potential conflict.

Keywords: potential, regression analysis, pedestrian, conflicts

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Authors: Gregory Michael McMahon

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Risk management for the purposes of minimizing the damages from the operations of dams has met with opposition emerging from organisations and authorities, and their practitioners. It appears that the cause may be a misunderstanding of risk management arising from exchanges that mix deterministic thinking with risk-centric thinking and that do not separate uncertainty from reliability and accuracy from probability. This paper sets out those misunderstandings that arose from dam operations at Wivenhoe in 2011, using a comparison of outcomes that have been based on the methodology and its rules and those that have been operated by applying misunderstandings of the rules. The paper addresses the performance of one risk-centric Flood Manual for Wivenhoe Dam in achieving a risk management outcome. A mixture of engineering, administrative, and legal factors appear to have combined to reduce the outcomes from the risk approach. These are described. The findings are that a risk-centric Manual may need to assist administrations in the conduct of scenario training regimes, in responding to healthy audit reporting, and in the development of decision-support systems. The principal assistance needed from the Manual, however, is to assist engineering and the law to a good understanding of how risks are managed – do not assume that risk management is understood. The wider findings are that the critical profession for decision-making downstream of the meteorologist is not dam engineering or hydrology, or hydraulics; it is risk management. Risk management will provide the minimum flood damage outcome where actual rainfalls match or exceed forecasts of rainfalls, that therefore risk management will provide the best approach for the likely history of flooding in the life of a dam, and provisions made for worst cases may be state of the art in risk management. The principal conclusion is the need for training in both risk management as a discipline and also in the application of risk management rules to particular dam operational scenarios.

Keywords: risk management, flood control, dam operations, deterministic thinking

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Authors: Bawadi M. A., Abbad J. A., Baras E. A.

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This study was conducted to assess wind energy resources in the area of Al-Adwas Ras-Huwirah Hadhramout Governorate, Yemen, through using statistical calculations, the Weibull model and SPSS program were used in the monthly and the annual to analyze the wind energy resource; the convergence of wind energy; turbine efficiency in the selected area. Wind speed data was obtained from NASA over a period of ten years (2010-2019) and at heights of 50 m above ground level. Probability distributions derived from wind data and their distribution parameters are determined. The density probability function is fitted to the measured probability distributions on an annual basis. This study also involves locating preliminary sites for wind farms using Geographic Information System (GIS) technology. This further leads to maximizing the output energy from the most suitable wind turbines in the proposed site.

Keywords: wind speed analysis, Yemen wind energy, wind power density, Weibull distribution model

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Authors: Petra Dilling

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This study examines the market reaction to the publication of integrated reports for a sample of 316 global companies for the reporting year 2018. Applying event study methodology, we find significant cumulative average abnormal returns (CAARs) after the publication date. To ensure robust estimation resultsthe three-factor model, according to Fama and French, is used as well as a market-adjusted model, a CAPM and a Frama-French model taking GARCH effects into account. We find a significant positive CAAR after the publication day of the integrated report. Our results suggest that investors react to information provided in the integrated report and that they react differently to the annual financial report. Furthermore, our cross-sectional analysis confirms that companies with a significant positive cumulative average abnormal show certain characteristic. It was found that European companies have a higher likelihood to experience a stronger significant positive market reaction to their integrated report publication.

Keywords: integrated report, event methodology, cumulative abnormal return, sustainability, CAPM

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Authors: Ali Heidari, Hany Saleem

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The tailwater requirements are important criteria in the design of the stilling basins as energy dissipation of the spillways. The adequate tailwater level that ensures the hydraulic jump inside the basin should be fulfilled by the river's natural water level and the apron depth downstream of the chute. The requirements of the hydraulic jump should mainly be checked for the design flood, however, the drawn jump condition should not be critical in the discharges lesser than the design flood. The tailwater requirement is not met in Almatti dam, built in 2005 in India, and the jump sweep out from the basin, resulting in significant scour in the apron and end sill of the basin. This paper discusses different hydraulic solutions as sustainable solutions for the rehabilitation program. The deep apron alternative is proposed for the fewer bays of the spillway as the most cost-effective, sustainable solution. The apron level of 15 gates out of 26 gates should decrease by 5.4 m compared to the existing design to ensure a safe hydraulic jump up to the discharge of 10,000 m3/s i.e. 30% of the updated PMF.

Keywords: dam, spillway, stilling basin, Almatti

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Authors: M. A. C. S. Sampath Fernando, James M. Curran, Renate Meyer

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Model assessment, in the Bayesian context, involves evaluation of the goodness-of-fit and the comparison of several alternative candidate models for predictive accuracy and improvements. In posterior predictive checks, the data simulated under the fitted model is compared with the actual data. Predictive model accuracy is estimated using information criteria such as the Akaike information criterion (AIC), the Bayesian information criterion (BIC), the Deviance information criterion (DIC), and the Watanabe-Akaike information criterion (WAIC). The goal of an information criterion is to obtain an unbiased measure of out-of-sample prediction error. Since posterior checks use the data twice; once for model estimation and once for testing, a bias correction which penalises the model complexity is incorporated in these criteria. Cross-validation (CV) is another method used for examining out-of-sample prediction accuracy. Leave-one-out cross-validation (LOO-CV) is the most computationally expensive variant among the other CV methods, as it fits as many models as the number of observations. Importance sampling (IS), truncated importance sampling (TIS) and Pareto-smoothed importance sampling (PSIS) are generally used as approximations to the exact LOO-CV and utilise the existing MCMC results avoiding expensive computational issues. The reciprocals of the predictive densities calculated over posterior draws for each observation are treated as the raw importance weights. These are in turn used to calculate the approximate LOO-CV of the observation as a weighted average of posterior densities. In IS-LOO, the raw weights are directly used. In contrast, the larger weights are replaced by their modified truncated weights in calculating TIS-LOO and PSIS-LOO. Although, information criteria and LOO-CV are unable to reflect the goodness-of-fit in absolute sense, the differences can be used to measure the relative performance of the models of interest. However, the use of these measures is only valid under specific circumstances. This study has developed 11 models using normal, log-normal, gamma, and student’s t distributions to improve the PCR stutter prediction with forensic data. These models are comprised of four with profile-wide variances, four with locus specific variances, and three which are two-component mixture models. The mean stutter ratio in each model is modeled as a locus specific simple linear regression against a feature of the alleles under study known as the longest uninterrupted sequence (LUS). The use of AIC, BIC, DIC, and WAIC in model comparison has some practical limitations. Even though, IS-LOO, TIS-LOO, and PSIS-LOO are considered to be approximations of the exact LOO-CV, the study observed some drastic deviations in the results. However, there are some interesting relationships among the logarithms of pointwise predictive densities (lppd) calculated under WAIC and the LOO approximation methods. The estimated overall lppd is a relative measure that reflects the overall goodness-of-fit of the model. Parallel log-likelihood profiles for the models conditional on equal posterior variances in lppds were observed. This study illustrates the limitations of the information criteria in practical model comparison problems. In addition, the relationships among LOO-CV approximation methods and WAIC with their limitations are discussed. Finally, useful recommendations that may help in practical model comparisons with these methods are provided.

Keywords: cross-validation, importance sampling, information criteria, predictive accuracy

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Authors: Ferhat Demir

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Even if strategic management is highly critical for all types of organizations, only a few maturity models have been proposed in business literature for the area of strategic management activities. This paper updates previous studies and presents a new conceptual model for assessing the maturity of strategic management in any organization. Strategic management maturity model (S-3M) is basically composed of 6 maturity levels with 7 dimensions. The biggest contribution of S-3M is to put innovation into agenda of strategic management. The main objective of this study is to propose a model to align innovation with business strategies. This paper suggests that innovation (breakthrough new products/services and business models) is the only way of creating sustainable growth and strategy studies cannot ignore this aspect. Maturity models should embrace innovation to respond dynamic business environment and rapidly changing customer behaviours.

Keywords: strategic management, innovation, business model, maturity model

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Authors: Wenqi Liu, Reginald Bailey

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This study explores an advanced approach to enhancing B2B sales forecasting by integrating machine learning models with a rule-based decision framework. The methodology begins with the development of a machine learning classification model to predict conversion likelihood, aiming to improve accuracy over traditional methods like logistic regression. The classification model's effectiveness is measured using metrics such as accuracy, precision, recall, and F1 score, alongside a feature importance analysis to identify key predictors. Following this, a machine learning regression model is used to forecast sales value, with the objective of reducing mean absolute error (MAE) compared to linear regression techniques. The regression model's performance is assessed using MAE, root mean square error (RMSE), and R-squared metrics, emphasizing feature contribution to the prediction. To bridge the gap between predictive analytics and decision-making, a rule-based decision model is introduced that prioritizes customers based on predefined thresholds for conversion probability and predicted sales value. This approach significantly enhances customer prioritization and improves overall sales performance by increasing conversion rates and optimizing revenue generation. The findings suggest that this combined framework offers a practical, data-driven solution for sales teams, facilitating more strategic decision-making in B2B environments.

Keywords: sales forecasting, machine learning, rule-based decision model, customer prioritization, predictive analytics

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Authors: Alper Taner, Yesim Benal Oztekin, Huseyin Duran

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In this study, an Artificial Neural Network (ANN) was developed in order to classify barley varieties. For this purpose, physical properties of barley varieties were determined and ANN techniques were used. The physical properties of 8 barley varieties grown in Turkey, namely thousand kernel weight, geometric mean diameter, sphericity, kernel volume, surface area, bulk density, true density, porosity and colour parameters of grain, were determined and it was found that these properties were statistically significant with respect to varieties. As ANN model, three models, N-l, N-2 and N-3 were constructed. The performances of these models were compared. It was determined that the best-fit model was N-1. In the N-1 model, the structure of the model was designed to be 11 input layers, 2 hidden layers and 1 output layer. Thousand kernel weight, geometric mean diameter, sphericity, kernel volume, surface area, bulk density, true density, porosity and colour parameters of grain were used as input parameter; and varieties as output parameter. R2, Root Mean Square Error and Mean Error for the N-l model were found as 99.99%, 0.00074 and 0.009%, respectively. All results obtained by the N-l model were observed to have been quite consistent with real data. By this model, it would be possible to construct automation systems for classification and cleaning in flourmills.

Keywords: physical properties, artificial neural networks, barley, classification

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Authors: Eun-Taek Sin, Hyun-Ju Jang, Chang Geun Song, Yong-Sik Han

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Due to the climate change, the coastal urban area repeatedly suffers from the loss of property and life by flooding. There are three main causes of inland submergence. First, when heavy rain with high intensity occurs, the water quantity in inland cannot be drained into rivers by increase in impervious surface of the land development and defect of the pump, storm sewer. Second, river inundation occurs then water surface level surpasses the top of levee. Finally, Coastal inundation occurs due to rising sea water. However, previous studies ignored the complex mechanism of flooding, and showed discrepancy and inadequacy due to linear summation of each analysis result. In this study, inland flooding and river inundation were analyzed together by HDM-2D model. Petrov-Galerkin stabilizing method and flux-blocking algorithm were applied to simulate the inland flooding. In addition, sink/source terms with exponentially growth rate attribute were added to the shallow water equations to include the inland flooding analysis module. The applications of developed model gave satisfactory results, and provided accurate prediction in comprehensive flooding analysis. The applications of developed model gave satisfactory results, and provided accurate prediction in comprehensive flooding analysis. To consider the coastal surge, another module was developed by adding seawater to the existing Inland Flooding-River Inundation binding module for comprehensive flooding analysis. Based on the combined modules, the Coastal Inundation – Inland & River Flow Interface was simulated by inputting the flow rate and depth data in artificial flume. Accordingly, it was able to analyze the flood patterns of coastal cities over time. This study is expected to help identify the complex causes of flooding in coastal areas where complex flooding occurs, and assist in analyzing damage to coastal cities. Acknowledgements—This research was supported by a grant ‘Development of the Evaluation Technology for Complex Causes of Inundation Vulnerability and the Response Plans in Coastal Urban Areas for Adaptation to Climate Change’ [MPSS-NH-2015-77] from the Natural Hazard Mitigation Research Group, Ministry of Public Safety and Security of Korea.

Keywords: flooding analysis, river inundation, inland flooding, 2D hydrodynamic model

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Authors: Champika S. Kariyawasam

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The distribution of gorse (Ulex europaeus) plants in South Australia has been modelled using 126 presence-only location data as a function of seven climate parameters. The predicted range of U. europaeus is mainly along the Mount Lofty Ranges in the Adelaide Hills and on Kangaroo Island. Annual precipitation and yearly average aridity index appeared to be the highest contributing variables to the final model formulation. The Jackknife procedure was employed to identify the contribution of different variables to gorse model outputs and response curves were used to predict changes with changing environmental variables. Based on this analysis, it was revealed that the combined effect of one or more variables could make a completely different impact to the original variables on their own to the model prediction. This work also demonstrates the need for a careful approach when selecting environmental variables for projecting correlative models to climatically distinct area. Maxent acts as a robust model when projecting the fitted species distribution model to another area with changing climatic conditions, whereas the generalized linear model, bioclim, and domain models to be less robust in this regard. These findings are important not only for predicting and managing invasive alien gorse in South Australia and Sri Lanka but also in other countries of the invasive range.

Keywords: invasive species, Maxent, species distribution modelling, Ulex europaeus

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Authors: Gerald Todd

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Recent advances in random epistemologies and ubiquitous theory have paved the way for web services. Given the current status of linear-time communication, cyberinformaticians compellingly desire the exploration of link-level acknowledgements. In order to realize this purpose, we concentrate our efforts on disconfirming that DHTs and model checking are mostly incompatible.

Keywords: LAN, cyberinformatics, model checking, communication

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Authors: Pronob Kumar Mozumder, M. Abdur Rob Mollah

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This research was conducted in two coastal locations of Bangladesh from February, 2013 to January, 2014.The objective of this research was to assess the potential vulnerabilities of climate change on local ecosystem and people and to identify and recommend local level adaptation strategies to climate change. Focus group discussions, participatory rural appraisal, interviewing local elderly people were conducted. Perceptions about climate change indicate that local people are experiencing impacts of climate change. According to local people, temperature, cyclone, rain, water-logging, siltation, salinity, erosion, and flash flood are increasing. Vulnerability assessment revealed that local people are variously affected by abnormal climate related disasters. This is jeopardizing their livelihoods, risking their lives, health, and their assets. This prevailing climatic situation in the area is also impacting their environmental conditions, biodiversity and natural resources, and their economic activities. The existing adaptation includes using traditional boat and mobile phone while fishing and making house on high land and lower height. Proposed adaptation for fishing boat are using more than 60 feet length with good timber, putting at least 3 longitudinal bar along upper side, using enough vertical side bars. The homestead measures include use of cross bracing of wall frame, roof tying with extra-post by ropes and plantation of timber tree against wind.

Keywords: community level vulnerabilities, climate change, Cox’s Bazar-Teknaf Coastal Area, Bangladesh

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Authors: Dinnah Feye H. Andal, Ann Laurice V. Salonga

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The paper investigates the flooding event that was experienced by Marikina City residents during the onslaught of Tropical Storm Ondoy on September 26, 2009 and during the heavy downpour caused by the southwest monsoon (Habagat) on August 1-8, 2012. Typhoon Ketsana, locally known as Tropical Storm Ondoy, devastated the whole of Marikina City, displacing a lot of people from their homes and damages properties as well, as flood rose at a very short period of time. Meanwhile, the massive amount of rain water brought by the southwest monsoon lasted for a week that also caused flooding to different parts of Metro Manila including Marikina City. This paper examines how the respondents’ experiences of the flooding caused by Tropical Storm Ondoy informed the coping strategies that the households in Barangay Tumana and Provident Village employed during the flooding brought by the southwest monsoon rains. Specifically, the research compares the coping strategies to flood hazards between residents of Barangay Tumana and Provident Village before, during and after the flooding caused by the southwest monsoon rains. Both study sites have relatively low elevation and are located along rivers and creeks which make them highly susceptible to flood. Interviews with affected residents were undertaken to understand how a household's coping strategies contribute to the development of community coping strategies at the respective neighborhood level. Based from the findings, income levels, local politics, religion and social relations between and among neighbors affect the way household and community coping strategies differ in the two case study sites.

Keywords: community coping strategies, Habagat, Marikina, Ondoy

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Authors: S. M. Anowarul Haque, Md. Asiful Islam

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Electrical load forecasting is considered to be one of the most indispensable parts of a modern-day electrical power system. To ensure a reliable and efficient supply of electric energy, special emphasis should have been put on the predictive feature of electricity supply. Artificial Neural Network-based approaches have emerged to be a significant area of interest for electric load forecasting research. This paper proposed an Artificial Neural Network model based on the particle swarm optimization algorithm for improved electric load forecasting for Mymensingh, Bangladesh. The forecasting model is developed and simulated on the MATLAB environment with a large number of training datasets. The model is trained based on eight input parameters including historical load and weather data. The predicted load data are then compared with an available dataset for validation. The proposed neural network model is proved to be more reliable in terms of day-wise load forecasting for Mymensingh, Bangladesh.

Keywords: load forecasting, artificial neural network, particle swarm optimization

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Authors: Lamyaa Gamal El-Deen Taha, Ashraf Sharawi

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Planet image is a new data source from planet lab. This research is concerned with the assessment of Planet image for land cover mapping. Two pixel based classifiers and one subpixel based classifier were compared. Firstly, rectification of Planet image was performed. Secondly, a comparison between minimum distance, maximum likelihood and neural network classifications for classification of Planet image was performed. Thirdly, the overall accuracy of classification and kappa coefficient were calculated. Results indicate that neural network classification is best followed by maximum likelihood classifier then minimum distance classification for land cover mapping.

Keywords: planet image, land cover mapping, rectification, neural network classification, multilayer perceptron, soft classifiers, hard classifiers

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Authors: K. S. Agrawal

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The performance of jet ejector was studied in detail by different authors. Several authors have studied mass transfer characteristics like interfacial area, mass transfer coefficients etc. In this paper, we have made an attempt to develop PDE model by considering bubble properties and apply Lattice-Boltzmann technique for PDE model. We may present the results for the interfacial area which we have obtained from our numerical simulation. Later the results are compared with previous work.

Keywords: jet ejector, mass transfer characteristics, numerical simulation, Lattice-Boltzmann technique

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Authors: Sagar Shiwakoti, Narendra Man Shakya

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The lumped conceptual hydrological model HBV is applied to the Karnali River Basin to estimate runoff at several gauging stations and to analyze the changes in catchment hydrology and future flood magnitude due to climate change. The performance of the model is analyzed to assess its suitability to simulate streamflow in snow fed mountainous catchments. Due to the structural complexity, the model shows difficulties in modeling low and high flows accurately at the same time. It is observed that the low flows were generally underestimated and the peaks were correctly estimated except for some sharp peaks due to isolated precipitation events. In this study, attempt has been made to evaluate the importance of snow melt discharge in the runoff regime of the basin. Quantification of contribution of snowmelt to annual, summer and winter runoff has been done. The contribution is highest at the beginning of the hot months as the accumulated snow begins to melt. Examination of this contribution under conditions of increased temperatures indicate that global warming leading to increase in average basin temperature will significantly lead to higher contributions to runoff from snowmelt. Forcing the model with the output of HadCM3 GCM and the A1B scenario downscaled to the station level show significant changes to catchment hydrology in the 2040s. It is observed that the increase in runoff is most extreme in June - July. A shift in the hydrological regime is also observed.

Keywords: hydrological modeling, HBV light, rainfall runoff modeling, snow melt, climate change

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Authors: G. Venkata Rao, P. Kalpana, R. Srinivasa Rao

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Adequate and reliable estimates of aquifer parameters are of utmost importance for proper management of vital groundwater resources. At present scenario the ground water is polluted because of industrial waste disposed over the land and the contaminants are transported in the aquifer from one area to another area which is depending on the characteristics of the aquifer and contaminants. To know the contaminant transport, the accurate estimation of aquifer properties is highly needed. Conventionally, these properties are estimated through pumping tests carried out on water wells. The occurrence and movement of ground water in the aquifer are characteristically defined by the aquifer parameters. The pumping (aquifer) test is the standard technique for estimating various hydraulic properties of aquifer systems, viz, transmissivity (T), hydraulic conductivity (K), storage coefficient (S) etc., for which the graphical method is widely used. The study area for conducting pumping test is Pydibheemavaram Industrial area near the coastal belt of Srikulam, AP, India. The main objective of the present work is to estimate the aquifer properties for developing contaminant transport model for the study area.

Keywords: aquifer, contaminant transport, hydraulic conductivity, industrial waste, pumping test

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Authors: Ubaidillah Zuhdi, Shunsuke Mori, Kazuhisa Kamegai

Abstract:

The purpose of this study is to forecast the influences of Information and Communication Technology (ICT) on the structural changes of Japanese economies based on Leontief Input-Output (IO) coefficients. This study establishes a statistical analysis to predict the future interrelationships among industries. We employ the Constrained Multivariate Regression (CMR) model to analyze the historical changes of input-output coefficients. Statistical significance of the model is then tested by Likelihood Ratio Test (LRT). In our model, ICT is represented by two explanatory variables, i.e. computers (including main parts and accessories) and telecommunications equipment. A previous study, which analyzed the influences of these variables on the structural changes of Japanese industrial sectors from 1985-2005, concluded that these variables had significant influences on the changes in the business circumstances of Japanese commerce, business services and office supplies, and personal services sectors. The projected future Japanese economic structure based on the above forecast generates the differentiated direct and indirect outcomes of ICT penetration.

Keywords: forecast, ICT, industrial structural changes, statistical analysis

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Authors: Haoyang Liang, Dandong Ge

Abstract:

With the rapid development of Chinese cities, traffic congestion has become more and more serious. At the same time, there are many closed old residential area in Chinese cities, which seriously affect the connectivity of urban roads and reduce the density of urban road networks. After reopening the restricted old residential area, the internal roads in the original residential area were transformed into urban roads, which was of great help to alleviate traffic congestion. This paper uses the spatial syntactic theory to analyze the urban road network and compares the roads with the integration and connectivity degree to evaluate whether the opening of the roads in the residential areas can improve the urban traffic. Based on the road network system within the first loop road in Kunming, the Space Syntax evaluation model is established for status analysis. And comparative analysis method will be used to compare the change of the model before and after the road openness of the old urban residential district within the first-ring road in Kunming. Then it will pick out the areas which indicate a significant difference for the small dimensions model analysis. According to the analyzed results and traffic situation, the evaluation of road openness in the old urban residential district will be proposed to improve the urban residential districts.

Keywords: Space Syntax, Kunming, urban renovation, traffic jam

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Authors: Y. T. Tsai, Jin H. Huang

Abstract:

The sound pressure level (SPL) of the moving-coil loudspeaker (MCL) is often simulated and analyzed using the lumped parameter model. However, the SPL of a MCL cannot be simulated precisely in the high frequency region, because the value of cone effective area is changed due to the geometry variation in different mode shapes, it is also related to affect the acoustic radiation mass and resistance. Herein, the paper presents the inverse method which has a high ability to measure the value of cone effective area in various frequency points, also can estimate the MCL electroacoustic parameters simultaneously. The proposed inverse method comprises the direct problem, adjoint problem, and sensitivity problem in collaboration with nonlinear conjugate gradient method. Estimated values from the inverse method are validated experimentally which compared with the measured SPL curve result. Results presented in this paper not only improve the accuracy of lumped parameter model but also provide the valuable information on loudspeaker cone design.

Keywords: inverse problem, cone effective area, loudspeaker, nonlinear conjugate gradient method

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Authors: Abduladim Maitieg

Abstract:

The oil manufacture is one of the main productive activities in Libya and has a massive infrastructure, including offshore drilling and exploration and wide oil export platform sites that located in coastal area. There is a threat to marine and coastal area of oil spills is greatest in those sites with a high spills comes from urban and industry, parallel to that, monitoring oil spills and risk emergency strategy is weakness, An approach for estimating a coastal resources vulnerability to oil spills is presented based on abundance, environmental and Scio-economic importance, distance to oil spill resources and oil risk likelihood. As many as 10 coastal resources were selected for oil spill assessment at the coast. This study aims to evaluate, determine and establish vulnerable coastal resource maps and estimating the rate of oil spill comes for different oil spill resources in Misratah marine environment. In the study area there are two type of oil spill resources, major oil resources come from offshore oil industries which are 96 km from the Coast and Loading/Uploading oil platform. However, the miner oil resources come from urban sewage pipes and fish ports. In order to analyse the collected database, the Geographic information system software has been used to identify oil spill location, to map oil tracks in front of study area, and developing seasonal vulnerable costal resources maps. This work shows that there is a differential distribution of the degree of vulnerability to oil spills along the coastline, with values ranging from high vulnerability and low vulnerability, and highlights the link between oil spill movement and coastal resources vulnerability. The results of assessment found most of costal freshwater spring sites are highly vulnerable to oil spill due to their location on the intertidal zone and their close to proximity to oil spills recourses such as Zreag coast. Furthermore, the Saltmarsh coastline is highly vulnerable to oil spill risk due to characterisation as it contains a nesting area of sea turtles and feeding places for migratory birds and the . Oil will reach the coast in winter season according to oil spill movement. Coastal tourist beaches in the north coast are considered as highly vulnerable to oil spill due to location and closeness to oil spill resources.

Keywords: coastal recourses vulnerability, oil spill trajectory, gnome software, Misratah coast- Libya, GIS

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Authors: Michelle Eliane Hernández-García, Angélica Lozano

Abstract:

The traffic accidents study usually contemplates the relation between factors such as the type of vehicle, its operation, and the road infrastructure. Traffic accidents can be explained by different factors, which have a greater or lower relevance. Two zones are studied, a mixed industrial zone and the extended zone of it. The first zone has mainly residential (57%), and industrial (23%) land uses. Trucks are mainly on the roads where industries are located. Four sensors give information about traffic and speed on the main roads. The extended zone (which includes the first zone) has mainly residential (47%) and mixed residential (43%) land use, and just 3% of industrial use. The traffic mix is composed mainly of non-trucks. 39 traffic and speed sensors are located on main roads. The traffic mix in a mixed land use zone, could be related to traffic accidents. To understand this relation, it is required to identify the elements of the traffic mix which are linked to traffic accidents. Models that attempt to explain what factors are related to traffic accidents have faced multiple methodological problems for obtaining robust databases. Poisson regression models are used to explain the accidents. The objective of the Poisson analysis is to estimate a vector to provide an estimate of the natural logarithm of the mean number of accidents per period; this estimate is achieved by standard maximum likelihood procedures. For the estimation of the relation between traffic accidents and the traffic mix, the database is integrated of eight variables, with 17,520 observations and six vectors. In the model, the dependent variable is the occurrence or non-occurrence of accidents, and the vectors that seek to explain it, correspond to the vehicle classes: C1, C2, C3, C4, C5, and C6, respectively, standing for car, microbus, and van, bus, unitary trucks (2 to 6 axles), articulated trucks (3 to 6 axles) and bi-articulated trucks (5 to 9 axles); in addition, there is a vector for the average speed of the traffic mix. A Poisson model is applied, using a logarithmic link function and a Poisson family. For the first zone, the Poisson model shows a positive relation among traffic accidents and C6, average speed, C3, C2, and C1 (in a decreasing order). The analysis of the coefficient shows a high relation with bi-articulated truck and bus (C6 and the C3), indicating an important participation of freight trucks. For the expanded zone, the Poisson model shows a positive relation among traffic accidents and speed average, biarticulated truck (C6), and microbus and vans (C2). The coefficients obtained in both Poisson models shows a higher relation among freight trucks and traffic accidents in the first industrial zone than in the expanded zone.

Keywords: freight transport, industrial zone, traffic accidents, traffic mix, trucks

Procedia PDF Downloads 126