Abstract
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 -- a state of the art machine learning algorithm -- to overcome such limitations. Here, we predict groundwater withdrawals per unit area over a highly monitored portion of the High Plains aquifer in the central United States at 5 km resolution for the Years 2002-2019. Our modeled withdrawals had high accuracy on both training and testing data sets (R2 ≈ 0.99 and R2 ≈ 0.93, respectively) during leave-one-out (year) cross validation with low mean absolute error (MAE) ≈ 4.31 mm and root-mean-square error (RMSE) ≈ 13.50 mm for the year 2014. Moreover, we found that even for the extreme drought year of 2012, we have a satisfactory test score (R2 ≈ 0.84) with MAE ≈ 9.72 mm and RMSE ≈ 24.17 mm. Therefore, the proposed machine learning approach should be applicable to similar regions for proactive water management practices.
Recommended Citation
S. Majumdar et al., "Groundwater Withdrawal Prediction using Integrated Multitemporal Remote Sensing Data Sets and Machine Learning," Water Resources Research, vol. 56, no. 11, Wiley, Nov 2020.
The definitive version is available at https://doi.org/10.1029/2020WR028059
Department(s)
Geosciences and Geological and Petroleum Engineering
Research Center/Lab(s)
Center for High Performance Computing Research
Keywords and Phrases
Estimation and forecasting; Geospatial; Groundwater hydrology; Machine learning; Remote sensing; Time series analysis
International Standard Serial Number (ISSN)
0043-1397; 1944-7973
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2020 American Geophysical Union, All rights reserved.
Publication Date
01 Nov 2020
