TY - JOUR
AU - 
AB - Computers, Materials & Continua Tech Science Press DOI:10.32604/cmc.2022.020495 Article 1,* 1 2 Mohd Anul Haq , Abdul Khadar Jilani and P. Prabu College of Computer and Information Sciences Majmaah University Almajmaah, 11952, Saudi Arabia CHRIST (Deemed to be University), Bangalore, India Corresponding Author: Mohd Anul Haq. Email: m.anul@mu.edu.sa Received: 26 May 2021; Accepted: 03 July 2021 Abstract: The understanding of water resource changes and a proper projec- tion of their future availability are necessary elements of sustainable water planning. Monitoring GWS change and future water resource availability are crucial, especially under changing climatic conditions. Traditional methods for in situ groundwater well measurement are a significant challenge due to data unavailability. The present investigation utilized the Long Short Term Memory (LSTM) networks to monitor and forecast Terrestrial Water Stor- age Change (TWSC) and Ground Water Storage Change (GWSC) based on Gravity Recovery and Climate Experiment (GRACE) datasets from 2003– 2025 for vfi e basins of Saudi Arabia. An attempt has been made to assess the effects of rainfall, water used, and net budget modeling of groundwater. Analysis 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 
TI - Deep Learning Based Modeling of Groundwater Storage Change
JF - Computers, Materials & Continua
DO - 10.32604/cmc.2022.020495
DA - 2022-01-01
UR - https://www.deepdyve.com/lp/unpaywall/deep-learning-based-modeling-of-groundwater-storage-change-9T8Z9T8zJP
DP - DeepDyve
ER -