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Title: Machine learning algorithms for modeling groundwater level changes in agricultural regions of the U.S.

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 lessmore » than 2 m over a majority of the area. Here, 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.« less
Authors:
ORCiD logo [1] ; ORCiD logo [1] ; ORCiD logo [2] ; ORCiD logo [2]
  1. Pennsylvania State Univ., University Park, PA (United States)
  2. Univ. of Chicago, Chicago, IL (United States); Argonne National Lab. (ANL), Lemont, IL (United States)
Publication Date:
Grant/Contract Number:
AC02-06CH11357; 0951576
Type:
Published Article
Journal Name:
Water Resources Research
Additional Journal Information:
Journal Volume: 53; Journal Issue: 5; Journal ID: ISSN 0043-1397
Publisher:
American Geophysical Union (AGU)
Research Org:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org:
USDOE Office of Science (SC)
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; 97 MATHEMATICS AND COMPUTING; 58 GEOSCIENCES
OSTI Identifier:
1357813
Alternate Identifier(s):
OSTI ID: 1357814; OSTI ID: 1398994

Sahoo, S., Russo, T. A., Elliott, J., and Foster, I.. Machine learning algorithms for modeling groundwater level changes in agricultural regions of the U.S.. United States: N. p., Web. doi:10.1002/2016WR019933.
Sahoo, S., Russo, T. A., Elliott, J., & Foster, I.. Machine learning algorithms for modeling groundwater level changes in agricultural regions of the U.S.. United States. doi:10.1002/2016WR019933.
Sahoo, S., Russo, T. A., Elliott, J., and Foster, I.. 2017. "Machine learning algorithms for modeling groundwater level changes in agricultural regions of the U.S.". United States. doi:10.1002/2016WR019933.
@article{osti_1357813,
title = {Machine learning algorithms for modeling groundwater level changes in agricultural regions of the U.S.},
author = {Sahoo, S. and Russo, T. A. and Elliott, J. and Foster, I.},
abstractNote = {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. Here, 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.},
doi = {10.1002/2016WR019933},
journal = {Water Resources Research},
number = 5,
volume = 53,
place = {United States},
year = {2017},
month = {5}
}