Physical Sciences and Mathematics Commons^™

Groundwater Withdrawal Prediction Using Integrated Multitemporal Remote Sensing Data Sets And Machine Learning, S. Majumdar, Ryan G. Smith, J. J. Butler, V. Lakshmi

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Effective monitoring of groundwater withdrawals is necessary to help mitigate the negative impacts of aquifer depletion. In this study, we develop a holistic approach that combines water balance components with a machine learning model to estimate groundwater withdrawals. We use both multitemporal satellite and modeled data from sensors that measure different components of the water balance and land use at varying spatial and temporal resolutions. These remote sensing products include evapotranspiration, precipitation, and land cover. Due to the inherent complexity of integrating these data sets and subsequently relating them to groundwater withdrawals using physical models, we apply random forests -- …