Deep Learning Based Modeling of Groundwater Storage Change

作     者：Mohd Anul Haq Abdul Khadar Jilani P.Prabu 

作者机构：College of Computer and Information Sciences Majmaah University Almajmaah11952Saudi Arabia CHRIST(Deemed to be University)BangaloreIndia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第70卷第3期

页      面：4599-4617页

核心收录：

学科分类：081803[工学-地质工程] 08[工学] 0818[工学-地质资源与地质工程] 0815[工学-水利工程] 0706[理学-大气科学] 

基　　金：The authors extend their appreciation to the deputyship for Research&Innovation Ministry of Education in Saudi Arabia for funding this research work through the project number(IFP-2020-14) 

主　　题：LSTM forecasting time series tensorflow keras modeling 

摘      要：The understanding of water resource changes and a proper projection of their future availability are necessary elements of sustainable water *** GWS change and future water resource availability are crucial,especially under changing climatic *** methods for in situ groundwater well measurement are a significant challenge due to data *** present investigation utilized the Long Short Term Memory(LSTM)networks to monitor and forecast Terrestrial Water Storage Change(TWSC)and Ground Water Storage Change(GWSC)based on Gravity Recovery and Climate Experiment(GRACE)datasets from 2003-2025 for five basins of Saudi *** attempt has been made to assess the effects of rainfall,water used,and net budget modeling of *** of GRACE-derived TWSC and GWSC estimates indicates that all five basins show depletion of water from 2003-2020 with a rate ranging from−5.88±1.2 mm/year to−14.12±1.2 mm/year and−3.5±1.5 to−10.7±1.5,*** based on the developed LSTM model indicates that the investigated basins are likely to experience serious water depletion at rates ranging from−7.78±1.2 to−15.6±1.2 for TWSC and−4.97±1.5 to−12.21±1.5 for GWSC from *** interesting observation was a minor increase in rainfall during the study period for three basins.