The amount of water withdrawn by wells is one of the quantitative variables that can be applied to estimate groundwater resources and further evaluate the human influence on groundwater systems. The accuracy for the c...The amount of water withdrawn by wells is one of the quantitative variables that can be applied to estimate groundwater resources and further evaluate the human influence on groundwater systems. The accuracy for the calculation of the amount of water withdrawal significantly influences the regional groundwater resource evaluation and management. However, the decentralized groundwater pumping, inefficient management, measurement errors and uncertainties have resulted in considerable errors in the groundwater withdrawal estimation. In this study, to improve the estimation of the groundwater withdrawal, an innovative approach was proposed using an inversion method based on a regional groundwater flow numerical model, and this method was then applied in the North China Plain. The principle of the method was matching the simulated water levels with the observation ones by adjusting the amount of groundwater withdrawal. In addition, uncertainty analysis of hydraulic conductivity and specific yield for the estimation of the groundwater withdrawal was conducted. By using the proposed inversion method, the estimated annual average groundwater withdrawal was approximately 24.92×10^9 m^3 in the North China Plain from 2002 to 2008. The inversion method also significantly improved the simulation results for both hydrograph and the flow field. Results of the uncertainty analysis showed that the hydraulic conductivity was more sensitive to the inversion results than the specific yield.展开更多
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
基金supported by the National Basic Research Program of China (No. 2010CB428804)the Public Welfare Industry Special Funds for Scientific Research from Ministry of Land and Resources of China (No. 201211079-4).
文摘The amount of water withdrawn by wells is one of the quantitative variables that can be applied to estimate groundwater resources and further evaluate the human influence on groundwater systems. The accuracy for the calculation of the amount of water withdrawal significantly influences the regional groundwater resource evaluation and management. However, the decentralized groundwater pumping, inefficient management, measurement errors and uncertainties have resulted in considerable errors in the groundwater withdrawal estimation. In this study, to improve the estimation of the groundwater withdrawal, an innovative approach was proposed using an inversion method based on a regional groundwater flow numerical model, and this method was then applied in the North China Plain. The principle of the method was matching the simulated water levels with the observation ones by adjusting the amount of groundwater withdrawal. In addition, uncertainty analysis of hydraulic conductivity and specific yield for the estimation of the groundwater withdrawal was conducted. By using the proposed inversion method, the estimated annual average groundwater withdrawal was approximately 24.92×10^9 m^3 in the North China Plain from 2002 to 2008. The inversion method also significantly improved the simulation results for both hydrograph and the flow field. Results of the uncertainty analysis showed that the hydraulic conductivity was more sensitive to the inversion results than the specific yield.
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.
