TY - JOUR
T1 - Machine learning algorithms for modeling groundwater level changes in agricultural regions of the U.S.
AU - Sahoo, S.
AU - Russo, T. A.
AU - Elliott, J.
AU - Foster, I.
N1 - Funding Information:
This work was supported by the University of Chicago 1896 Pilot Project, the Center for Robust Decision Making on Climate and Energy Policy (RDCEP), NSF award 0951576, and DOE contract DE-AC02-06CH11357. We would like to thank two anonymous reviewers for comments that greatly improved the manuscript. We also thank Debashish Sahu for his helpful suggestions on the model development. The groundwater level, climate, and streamflow data used in this study are publically available from the sources listed in the article. All data sets are available from the corresponding author on request.
Publisher Copyright:
© 2017. The Authors.
PY - 2017/5/1
Y1 - 2017/5/1
N2 - Climate, groundwater extraction, and surface water flows have complex nonlinear relationships with groundwater level in agricultural regions. To better understand the relative importance of each driver and predict groundwater level change, we develop a new ensemble modeling framework based on spectral analysis, machine learning, and uncertainty analysis, as an alternative to complex and computationally expensive physical models. We apply and evaluate this new approach in the context of two aquifer systems supporting agricultural production in the United States: the High Plains aquifer (HPA) and the Mississippi River Valley alluvial aquifer (MRVA). We select input data sets by using a combination of mutual information, genetic algorithms, and lag analysis, and then use the selected data sets in a Multilayer Perceptron network architecture to simulate seasonal groundwater level change. As expected, model results suggest that irrigation demand has the highest influence on groundwater level change for a majority of the wells. The subset of groundwater observations not used in model training or cross-validation correlates strongly (R > 0.8) with model results for 88 and 83% of the wells in the HPA and MRVA, respectively. In both aquifer systems, the error in the modeled cumulative groundwater level change during testing (2003–2012) was less than 2 m over a majority of the area. We conclude that our modeling framework can serve as an alternative approach to simulating groundwater level change and water availability, especially in regions where subsurface properties are unknown.
AB - Climate, groundwater extraction, and surface water flows have complex nonlinear relationships with groundwater level in agricultural regions. To better understand the relative importance of each driver and predict groundwater level change, we develop a new ensemble modeling framework based on spectral analysis, machine learning, and uncertainty analysis, as an alternative to complex and computationally expensive physical models. We apply and evaluate this new approach in the context of two aquifer systems supporting agricultural production in the United States: the High Plains aquifer (HPA) and the Mississippi River Valley alluvial aquifer (MRVA). We select input data sets by using a combination of mutual information, genetic algorithms, and lag analysis, and then use the selected data sets in a Multilayer Perceptron network architecture to simulate seasonal groundwater level change. As expected, model results suggest that irrigation demand has the highest influence on groundwater level change for a majority of the wells. The subset of groundwater observations not used in model training or cross-validation correlates strongly (R > 0.8) with model results for 88 and 83% of the wells in the HPA and MRVA, respectively. In both aquifer systems, the error in the modeled cumulative groundwater level change during testing (2003–2012) was less than 2 m over a majority of the area. We conclude that our modeling framework can serve as an alternative approach to simulating groundwater level change and water availability, especially in regions where subsurface properties are unknown.
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U2 - 10.1002/2016WR019933
DO - 10.1002/2016WR019933
M3 - Article
AN - SCOPUS:85018903316
SN - 0043-1397
VL - 53
SP - 3878
EP - 3895
JO - Water Resources Research
JF - Water Resources Research
IS - 5
ER -