A New Hybrid Water Balance and Machine Learning Approach for Groundwater
Withdrawal Prediction using Integrated Multi-Temporal Remote Sensing
Datasets
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 multi-temporal satellite and modeled data from sensors that measure
different components of the water balance 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 datasets (R≈
0.99 and R≈ 0.93, respectively) during leave-one-out (year)
cross-validation with low Mean Absolute Error (MAE) ≈ 4.26 mm and Root
Mean Square Error (RMSE) ≈ 13.57 mm for the year 2014. Moreover, we
found that even for the extreme drought year of 2012, we have a
satisfactory test score (R≈ 0.79) with MAE ≈ 10.34 mm and RMSE ≈ 27.04
mm. Therefore, the proposed hybrid water balance and machine learning
approach can be applied to similar regions for proactive water
management practices.