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Groundwater Withdrawal Prediction Using Integrated Multitemporal Remote Sensing Data Sets and Machine Learning
Author(s) -
Majumdar S.,
Smith R.,
Butler J. J.,
Lakshmi V.
Publication year - 2020
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2020wr028059
Subject(s) - groundwater , aquifer , scale (ratio) , remote sensing , environmental science , key (lock) , hydrology (agriculture) , computer science , geology , cartography , geotechnical engineering , geography , computer security
Key Points Groundwater withdrawals are not actively monitored in most places of the world at a scale necessary to implement sustainable solutions Various multitemporal remote sensing data are integrated into a machine learning framework to effectively predict groundwater withdrawals The results over the High Plains Aquifer, Kansas, USA, show that this approach is applicable to similar regions having sparse in situ data