Mining activities often cause dramatic changes in landscapes, particularly in the dump sites and its surrounding environment. Land rehabilitation is the process of renovating damaged land to some extent of its origina...Mining activities often cause dramatic changes in landscapes, particularly in the dump sites and its surrounding environment. Land rehabilitation is the process of renovating damaged land to some extent of its original shape and aims to minimize and mitigate the environmental effects to allow new land uses. The success of different rehabilitation strategy and newly suggested urban and architecture modeling depends on the landscape characterization (topography of the study area and its derivatives such as slope and aspects, geological and geomorphologic nature of the study area). The aim of this study is to demonstrate the utility of different methodologies based on geomatics techniques (Photogrammetry, Remote Sensing, Global Positioning System (GPS) and three dimensional Geographic Information System (GIS)) for highlighting landscape characterization which is needed for rehabilitation of Mahis area. Photogrammetric adjustment procedures were used to create digital elevation model and Orth-Photo model for the study area using aerial images. Remote sensing data were used for land classification to provide vital information for rehabilitation planning. GPS field observations were used to build spatial network for the study area based on ground control point collections. Finally, realistic representation of the study area with three dimensional GIS was prepared for the study area considering ease and flexible updating of the geo-spatial database.展开更多
Flash floods(FFs)are amongst the most devastating hazards in arid regions in response to climate change and can cause the loss of agricultural land,human lives and infrastructure.One of the major challenges is the hig...Flash floods(FFs)are amongst the most devastating hazards in arid regions in response to climate change and can cause the loss of agricultural land,human lives and infrastructure.One of the major challenges is the high-intensity rainfall events affecting low-lying areas that are vulnerable to FF.Several works in this field have been conducted using ensemble machine learning models and geohydrological models.However,the current advancement of eXtreme deep learning,which is named eXtreme deep factorisation machine(xDeepFM),for FF susceptibility mapping(FSM)is lacking in the literature.The current study introduces a new model and employs a previously unapplied approach to enhance FSM for capturing the severity of floods.The proposed approach has three main objectives:(i)During-and after-flood effects are assessed through flood detection techniques using Sentinel-1 data.(ii)Flood inventory is updated using remote sensing-based methods.The derived flood effects are implemented in the next step.(iii)An FSM map is generated using an xDeepFM model.Therefore,this study aims to apply xDeepFM to estimate susceptible areas using 13 factors in the emirates of Fujairah,UAE.The performance metrics show a recall of 0.9488),an F1-score of 0.9107),precision of(0.8756)and an overall accuracy of 90.41%.The accuracy of the applied xDeepFM model is compared with that of traditional machine learning models,specifically the deep neural network(78%),support vector machine(85.4%)and random forest(88.75%).Random forest achieves high accuracy,which is due to its strong performance that depends on factors contribution,dataset size and quality,and available computational resources.Comparatively,the xDeepFM model works efficiently for complicated prediction problems having high non-collinearity and huge datasets.The obtained map denotes that the narrow basins,lowland coastal areas and riverbank areas up to 5 km(Fujairah)are highly prone to FF,whilst the alluvial plains in Al Dhaid and hilly regions in Fujairah show low probability.The coastal city areas are bounded by high-rise steep hills and the Gulf of Oman,which can elevate the water levels during heavy rainfall.Four major synchronised influencing factors,namely,rainfall,elevation,drainage density,distance from drainage and geomorphology,account for nearly 50%of the total factors contributing to a very high flood susceptibility.This study offers a platform for planners and decision makers to take timely actions on potential areas in mitigating the effects of FF.展开更多
文摘Mining activities often cause dramatic changes in landscapes, particularly in the dump sites and its surrounding environment. Land rehabilitation is the process of renovating damaged land to some extent of its original shape and aims to minimize and mitigate the environmental effects to allow new land uses. The success of different rehabilitation strategy and newly suggested urban and architecture modeling depends on the landscape characterization (topography of the study area and its derivatives such as slope and aspects, geological and geomorphologic nature of the study area). The aim of this study is to demonstrate the utility of different methodologies based on geomatics techniques (Photogrammetry, Remote Sensing, Global Positioning System (GPS) and three dimensional Geographic Information System (GIS)) for highlighting landscape characterization which is needed for rehabilitation of Mahis area. Photogrammetric adjustment procedures were used to create digital elevation model and Orth-Photo model for the study area using aerial images. Remote sensing data were used for land classification to provide vital information for rehabilitation planning. GPS field observations were used to build spatial network for the study area based on ground control point collections. Finally, realistic representation of the study area with three dimensional GIS was prepared for the study area considering ease and flexible updating of the geo-spatial database.
基金the University of Sharjah and Fujairah Research Centre(Grant No.1902041134-P)that helped to facilitate this research.
文摘Flash floods(FFs)are amongst the most devastating hazards in arid regions in response to climate change and can cause the loss of agricultural land,human lives and infrastructure.One of the major challenges is the high-intensity rainfall events affecting low-lying areas that are vulnerable to FF.Several works in this field have been conducted using ensemble machine learning models and geohydrological models.However,the current advancement of eXtreme deep learning,which is named eXtreme deep factorisation machine(xDeepFM),for FF susceptibility mapping(FSM)is lacking in the literature.The current study introduces a new model and employs a previously unapplied approach to enhance FSM for capturing the severity of floods.The proposed approach has three main objectives:(i)During-and after-flood effects are assessed through flood detection techniques using Sentinel-1 data.(ii)Flood inventory is updated using remote sensing-based methods.The derived flood effects are implemented in the next step.(iii)An FSM map is generated using an xDeepFM model.Therefore,this study aims to apply xDeepFM to estimate susceptible areas using 13 factors in the emirates of Fujairah,UAE.The performance metrics show a recall of 0.9488),an F1-score of 0.9107),precision of(0.8756)and an overall accuracy of 90.41%.The accuracy of the applied xDeepFM model is compared with that of traditional machine learning models,specifically the deep neural network(78%),support vector machine(85.4%)and random forest(88.75%).Random forest achieves high accuracy,which is due to its strong performance that depends on factors contribution,dataset size and quality,and available computational resources.Comparatively,the xDeepFM model works efficiently for complicated prediction problems having high non-collinearity and huge datasets.The obtained map denotes that the narrow basins,lowland coastal areas and riverbank areas up to 5 km(Fujairah)are highly prone to FF,whilst the alluvial plains in Al Dhaid and hilly regions in Fujairah show low probability.The coastal city areas are bounded by high-rise steep hills and the Gulf of Oman,which can elevate the water levels during heavy rainfall.Four major synchronised influencing factors,namely,rainfall,elevation,drainage density,distance from drainage and geomorphology,account for nearly 50%of the total factors contributing to a very high flood susceptibility.This study offers a platform for planners and decision makers to take timely actions on potential areas in mitigating the effects of FF.
