Despite the high efficiency of remote sensing methods for rapid and large-scale detection of subsidence phenomena,this technique has limitations such as atmospheric impact and temporal and spatial decorrelation that a...Despite the high efficiency of remote sensing methods for rapid and large-scale detection of subsidence phenomena,this technique has limitations such as atmospheric impact and temporal and spatial decorrelation that affect the accuracy of the results.This paper proposes a method based on an artificial neural network to improve the results of monitoring land subsidence due to groundwater overexploitation by radar interferometry in the Aliabad plain(Central Iran).In this regard,vertical ground deformations were monitored over 18 months using the Sentinel-1A SAR images.To model the land subsidence by a multilayer perceptron(MLP)artificial neural network,four parameters,including groundwater level,alluvial thickness,elastic modulus,and transmissivity have been applied.The model's generalizability was assessed using data derived for 144 days.According to the results,the neural network estimates the land subsidence at each ground point with an accuracy of 6.8 mm.A comparison between the predicted and actual values indicated a significant agreement.The MLP model can be used to improve the results of subsidence detection in the study area or other areas with similar characteristics.展开更多
In recent years, the land subsidence due to different phenomena such as excessive withdrawal of groundwater resources has lead to significant damages to farmlands, residential buildings, roads and transmission lines. ...In recent years, the land subsidence due to different phenomena such as excessive withdrawal of groundwater resources has lead to significant damages to farmlands, residential buildings, roads and transmission lines. Inattention to the land subsidence results in the ruining of buildings which in turn causes the migration of people and imposes financial and social costs on the government. In this paper a case study is performed for Marvdasht plain and rural regions of Fars province, Iran. All affecting parameters and their related damages are considered in the study. The variation of groundwater level and the conditions of rainfall and drought processes in recent years are investigated. The effects of faults and the seismic vulnerability of the regions are also studied by seismic methods. Such factor as groundwater level drop, thickness of clay layers, variation in the thickness of layers at the hillsides and the coincidence of the direction of created cracks and fissures with direction of available faults in the studied area have been investigated as the main factors affecting the soil settlement. At the end of the research, appropriate solutions for the land subsidence prevention and consequently the reduction of the related damages are presented.展开更多
Due to the subtropical climate the average annual precipitation in Vietnam is high. Nevertheless it is observed that groundwater levels in the capital Hanoi have decreased dramatically. As a consequence more and more ...Due to the subtropical climate the average annual precipitation in Vietnam is high. Nevertheless it is observed that groundwater levels in the capital Hanoi have decreased dramatically. As a consequence more and more settlements of buildings have been registered since the beginning of the new millennium. Reason for this tremendous impact is the increasing demand of areas and the extensive surface sealing in the course of the industrial development. This paper describes the “state of the art” and the development of sustainable solutions to maintain and even increase the declined groundwater levels in Hanoi.展开更多
文摘Despite the high efficiency of remote sensing methods for rapid and large-scale detection of subsidence phenomena,this technique has limitations such as atmospheric impact and temporal and spatial decorrelation that affect the accuracy of the results.This paper proposes a method based on an artificial neural network to improve the results of monitoring land subsidence due to groundwater overexploitation by radar interferometry in the Aliabad plain(Central Iran).In this regard,vertical ground deformations were monitored over 18 months using the Sentinel-1A SAR images.To model the land subsidence by a multilayer perceptron(MLP)artificial neural network,four parameters,including groundwater level,alluvial thickness,elastic modulus,and transmissivity have been applied.The model's generalizability was assessed using data derived for 144 days.According to the results,the neural network estimates the land subsidence at each ground point with an accuracy of 6.8 mm.A comparison between the predicted and actual values indicated a significant agreement.The MLP model can be used to improve the results of subsidence detection in the study area or other areas with similar characteristics.
文摘In recent years, the land subsidence due to different phenomena such as excessive withdrawal of groundwater resources has lead to significant damages to farmlands, residential buildings, roads and transmission lines. Inattention to the land subsidence results in the ruining of buildings which in turn causes the migration of people and imposes financial and social costs on the government. In this paper a case study is performed for Marvdasht plain and rural regions of Fars province, Iran. All affecting parameters and their related damages are considered in the study. The variation of groundwater level and the conditions of rainfall and drought processes in recent years are investigated. The effects of faults and the seismic vulnerability of the regions are also studied by seismic methods. Such factor as groundwater level drop, thickness of clay layers, variation in the thickness of layers at the hillsides and the coincidence of the direction of created cracks and fissures with direction of available faults in the studied area have been investigated as the main factors affecting the soil settlement. At the end of the research, appropriate solutions for the land subsidence prevention and consequently the reduction of the related damages are presented.
文摘Due to the subtropical climate the average annual precipitation in Vietnam is high. Nevertheless it is observed that groundwater levels in the capital Hanoi have decreased dramatically. As a consequence more and more settlements of buildings have been registered since the beginning of the new millennium. Reason for this tremendous impact is the increasing demand of areas and the extensive surface sealing in the course of the industrial development. This paper describes the “state of the art” and the development of sustainable solutions to maintain and even increase the declined groundwater levels in Hanoi.
