Machine-learning-based regional-scale groundwater level prediction using GRACE

• Published in: Hydrogeology journal Vol. 29; no. 3; pp. 1027 - 1042
• Main Authors: Malakar, Pragnaditya, Mukherjee, Abhijit, Bhanja, Soumendra N., Ray, Ranjan Kumar, Sarkar, Sudeshna, Zahid, Anwar
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2021; Springer Nature B.V
• Subjects: Agriculture; Aquatic Pollution; Aquifers; Data; Depth; Earth and Environmental Science; Earth Sciences; Geology; Geophysics/Geodesy; GRACE (experiment); Gravity; Groundwater; Groundwater data; Groundwater levels; Groundwater storage; Hydrogeology; Hydrology/Water Resources; Learning algorithms; Machine learning; Observation wells; Population density; Predictions; Pumping; River basins; Statistical analysis; Statistical methods; Statistics; Support vector machines; Transboundary waters; Waste Water Technology; Water Management; Water Pollution Control; Water Quality/Water Pollution; Wells; Satellite imagery; Groundwater level anomaly prediction; Transboundary aquifer; Groundwater exploration; Machine learning

The rapid decline of groundwater levels (GWL) due to pervasive groundwater abstraction in the densely populated (~1 billion) Indus-Ganges-Brahmaputra-Meghna (IGBM) transboundary river basins of South Asia, necessitates a robust framework of prediction and understanding. While few localized studies e...