the coastal areas geomorphological characteristic is greatly active, where land accretion and erosion is a continuous phenomenon with different degrees. The temporal change in coastline location is an important sign of coastal development and it can provide quantitative and descriptive information on beach dynamics. To investigate coastal variability trends, when field-based surveys are inaccessible or absent, historical remote sensing data and GIS analysis can be effective techniques. This study focuses on the United Arab Emirates (UAE) coastline that stretches for more than 1,000 km to cover the entire coastal area of the country. Multitemporal Landsat satellite images cover more than 45 years had been used to assess the coastal land dynamics by first delineate land areas from existing water bodies accurately using GIS and remote sensing approaches. Then, quantify coastline variability by measuring erosion and accretion rates, using the statistical operations within Digital Shoreline Analysis Software (DSAS) that include, Net Shoreline Movement(NSM), Linear Regression Rate (LRR), and End Point Rate (EPR). Finally, compare and analyse these rates with the associate causes for a better understanding of the coast area dynamic nature. The study results can be a feasible management tool to help coastal planners and policymakers to consider the identified coastal dynamic trends before starting any urban development.
Traffic accidents impose significant problems in our daily life due to the huge social, environmental, and economic expenses associated with them. The rapid development in data science, geographic data collection, and processing methods encourage researchers to evaluate, delineate traffic accident hotspots, and to effectively predict and estimate traffic accidents. In this study, Kaggle traffic accidents dataset that covers United Kingdom for the time period between 2012-2014 will be investigated. Our methodology consists of three main techniques. First, Morans I method of spatial autocorrelation, and Getis-Ord Gi* statistics will be used to examine and relate traffic accidents dataset in terms of spatial and temporal features. Second, weighted features will be used as inputs for Deep Feedforward Neural Network (DFFNN). Finally, the performance of the proposed DFFNN will be evaluated based on its accuracy, misclassification rate, precision, prevalence, histogram of errors, and confusion matrix. These evaluation metrics are then used as a comparison basis against the performance of Support Vector Machine (SVM). The results will focus on using spatial statistics techniques to effectively weight different features according to their contribution to traffic accidents. Consequently, the output of the DFFNN asserts the likelihood of accident occurrence given a certain location. Furthermore, it would be beneficial to investigate whether these accidents exhibit certain timely patterns, such as certain days or months where accidents potentially occur more frequently. The proposed method can be effectively used by different authorities to implement an improved planning and management approaches for traffic accident reduction. Moreover, it can identify and locate road risk segments where immediate action should be considered.
Urbanization is a spatiotemporal process that has significant role in economic, social, and environmental structures. Spatiotemporal analysis for urban growth is vital for city management planning. With highly recognized financial and social developing trends, Dubai City, UAE appears as one of most challenging cities in terms of research and preparation toward a smart city aspect. Integrated technologies of remote sensing and geographic information system (GIS) facilitate urban growth detection and its relation to population distribution. In this study Multi-temporal, medium-resolution Landsat images were used to detect and analyze the urbanization trend in Dubai over the last three decades . Moreover, the influence of urbanization on the aspects of smart city tendency was investigated. The study methodology consisted of three parts. First, classification algorithms along with change detection, segmentation, and extraction of urban areas were used to obtain land Use/land Cover (LULC) maps. Second, Shannon's entropy was used to investigate Dubai's growth toward compact or sprawl city based on two city centers and a major highway. Finally, CA-Markov, associated with the digital elevation model and road map of Dubai, was used to simulate the urban change for 2030, 2050, and 2100. With more than 90% overall accuracy, the statistical analysis for LULC percentages and Shannons entropy values indicated that Dubai experienced a considerable increase in urban fabric while maintaining a compact growth. CA-Markov model estimated 3% urban growth by 2030, which would result in potential loss of green areas and open spaces. This study could be used in improving planning and management methods to achieve sustainable urban growth.
A clear understanding of the spatial distribution of earthquake events facilitates the prediction of seismicity and vulnerability among researchers in the social, physical, environmental, and demographic aspects. Generally, there are few studies on seismic risk assessment in United Arab Emirates (UAE) within the geographic information system (GIS) platform. Former researches and recent news events have demonstrated that the eastern part of the country experiences jolts of 3-5 magnitude, specifically near Fujairah city and surrounding towns. This study builds on previous research on the seismic hazard that extracted the eastern part of the UAE as the most hazard-prone zone. Therefore, this study develops an integrated analytical hierarchical process (AHP) and machine learning (ML) for risk mapping considering eight geospatial parameters—distance from shoreline, schools, hospitals, roads, residences, streams, confined area, and confined area slope. Experts’ opinions and literature reviews were the basis of the AHP ranking and weighting system. To validate the AHP system, support vector machine (SVM), decision tree (DT), and random forest (RF) classifiers were applied to the datasets. The datasets were split into 60 : 40 ratio for training and testing. Results show that SVM has the highest accuracy of 79.6% compared to DT and RF with a “predicted high” precision of 87.5% attained from the model. Risk maps from both AHP and ML approaches were developed and compared. Risk analysis was categorised into 5 classes “very high,” “high,” “moderate,” “low,” and “very low.” Both approaches modelled relatable spatial patterns as risk-prone zones. AHP approach concluded 3.6% as “very high” risk zone, whereas only 0.3% of total area was identified from ML. The total area for the “very high” (20 km2) and “high” (114 km2) risk was estimated from ML approach.
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