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

Sahoo, S.; Russo, T. A.; Elliott, J.; Foster, I.

doi:10.1002/2016WR019933

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

Journal Article · Sat May 13 00:00:00 EDT 2017 · Water Resources Research

DOI:https://doi.org/10.1002/2016WR019933· OSTI ID:1357813

^[1];

^[2];

^[3]

Department of Geosciences, Pennsylvania State University, University Park Pennsylvania USA
Computation Institute, University of Chicago, Chicago Illinois USA, Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, Illinois USA
Computation Institute, University of Chicago, Chicago Illinois USA, Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, Illinois USA, Department of Computer Science, University of Chicago, Chicago Illinois USA

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.

View Journal Article

Cite

Export

Save

Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

Grant/Contract Number:: AC02-06CH11357; 0951576

OSTI ID:: 1357813

Alternate ID(s):: OSTI ID: 1357814; OSTI ID: 1398994

Journal Information:: Water Resources Research, Journal Name: Water Resources Research; ISSN 0043-1397

Publisher:: American Geophysical Union (AGU)Copyright Statement

Country of Publication:: United States

Language:: English

Citation Metrics:

Cited by: 182 works

Citation information provided by
Web of Science

References (73)

Estimating epistemic and aleatory uncertainties during hydrologic modeling: An information theoretic approach: ESTIMATING EPISTEMIC AND ALEATORY UNCERTAINTIES Gong, Wei; Gupta, Hoshin V.; Yang, Dawen Water Resources Research, Vol. 49, Issue 4 https://doi.org/10.1002/wrcr.20161	journal	April 2013
Application of a neural network technique to rainfall-runoff modelling Shamseldin, Assad Y. Journal of Hydrology, Vol. 199, Issue 3-4 https://doi.org/10.1016/S0022-1694(96)03330-6	journal	December 1997
The parallel system for integrating impact models and sectors (pSIMS) Elliott, Joshua; Kelly, David; Chryssanthacopoulos, James Environmental Modelling & Software, Vol. 62 https://doi.org/10.1016/j.envsoft.2014.04.008	journal	December 2014
Using of hybrid fuzzy models to predict spatiotemporal groundwater quality parameters Jalalkamali, Amir Earth Science Informatics, Vol. 8, Issue 4 https://doi.org/10.1007/s12145-015-0222-6	journal	April 2015
Assessment of the impact of anthropogenic activities on the groundwater hydrology and chemistry in Tarsus coastal plain (Mersin, SE Turkey) using fuzzy clustering, multivariate statistics and GIS techniques Güler, Cüneyt; Kurt, Mehmet Ali; Alpaslan, Musa Journal of Hydrology, Vol. 414-415 https://doi.org/10.1016/j.jhydrol.2011.11.021	journal	January 2012
Prediction of monthly regional groundwater levels through hybrid soft-computing techniques Chang, Fi-John; Chang, Li-Chiu; Huang, Chien-Wei Journal of Hydrology, Vol. 541 https://doi.org/10.1016/j.jhydrol.2016.08.006	journal	October 2016
Efficacy of neural network and genetic algorithm techniques in simulating spatio-temporal fluctuations of groundwater: NEURAL NETWORK AND GENETIC ALGORITHM FOR GROUNDWATER LEVEL SIMULATION Jha, Madan K.; Sahoo, Sasmita Hydrological Processes, Vol. 29, Issue 5 https://doi.org/10.1002/hyp.10166	journal	March 2014
Comment on “Quantifying renewable groundwater stress with GRACE” by Alexandra S. Richey et al.: Comment Sahoo, Sasmita; Russo, Tess; Lall, Upmanu Water Resources Research, Vol. 52, Issue 5 https://doi.org/10.1002/2015WR018085	journal	May 2016
Elements of Information Theory Cover, Thomas M.; Thomas, Joy A. https://doi.org/10.1002/047174882X	book	April 2005
Comparative Study of SVMs and ANNs in Aquifer Water Level Prediction Behzad, Mohsen; Asghari, Keyvan; Coppola, Emery A. Journal of Computing in Civil Engineering, Vol. 24, Issue 5 https://doi.org/10.1061/(ASCE)CP.1943-5487.0000043	journal	September 2010
Comparison of genetic programming with neuro-fuzzy systems for predicting short-term water table depth fluctuations Shiri, Jalal; Kişi, Özgur Computers & Geosciences, Vol. 37, Issue 10 https://doi.org/10.1016/j.cageo.2010.11.010	journal	October 2011
A methodology to asess relations between climatic variability and variations in hydrologic time series in the southwestern United States Hanson, R. T.; Newhouse, M. W.; Dettinger, M. D. Journal of Hydrology, Vol. 287, Issue 1-4 https://doi.org/10.1016/j.jhydrol.2003.10.006	journal	February 2004
Groundwater availability of the Mississippi embayment Clark, Brian R.; Hart, Rheannon M.; Gurdak, Jason J. https://doi.org/10.3133/pp1785	report	January 2011
Artificial neural network modeling of water table depth fluctuations Coulibaly, Paulin; Anctil, François; Aravena, Ramon Water Resources Research, Vol. 37, Issue 4 https://doi.org/10.1029/2000WR900368	journal	April 2001
Climate change impacts on groundwater recharge- uncertainty, shortcomings, and the way forward? Holman, I. P. Hydrogeology Journal, Vol. 14, Issue 5 https://doi.org/10.1007/s10040-005-0467-0	journal	November 2005
A numerical modelling and neural network approach to estimate the impact of groundwater abstractions on river flows Parkin, G.; Birkinshaw, S. J.; Younger, P. L. Journal of Hydrology, Vol. 339, Issue 1-2 https://doi.org/10.1016/j.jhydrol.2007.01.041	journal	June 2007
Optimal reservoir operation considering the water quality issues: A stochastic conflict resolution approach: OPTIMAL RESERVOIR OPERATION Kerachian, Reza; Karamouz, Mohammad Water Resources Research, Vol. 42, Issue 12 https://doi.org/10.1029/2005WR004575	journal	December 2006
Optimising the mutual information of ecological data clusters using evolutionary algorithms Maier, H. R.; Zecchin, A. C.; Radbone, L. Mathematical and Computer Modelling, Vol. 44, Issue 5-6 https://doi.org/10.1016/j.mcm.2006.01.004	journal	September 2006
Groundwater App to Determine Flow Direction and Gradient Morrison, Derek; Munster, Clyde Groundwater, Vol. 53, Issue 2 https://doi.org/10.1111/gwat.12211	journal	June 2014
Climate Variability Controls on Unsaturated Water and Chemical Movement, High Plains Aquifer, USA Gurdak, Jason J.; Hanson, Randall T.; McMahon, Peter B. Vadose Zone Journal, Vol. 6, Issue 3 https://doi.org/10.2136/vzj2006.0087	journal	January 2007
Designing an optimal multivariate geostatistical groundwater quality monitoring network using factorial kriging and genetic algorithms Yeh, Ming-Sheng; Lin, Yu-Pin; Chang, Liang-Cheng Environmental Geology, Vol. 50, Issue 1 https://doi.org/10.1007/s00254-006-0190-8	journal	March 2006
MODFLOW-2005 : the U.S. Geological Survey modular ground-water model--the ground-water flow process (Techniques and Methods 6-A16) Harbaugh, Arlen W. Book 6: Modeling techniques, Section A. Ground-water https://doi.org/10.3133/tm6A16	report	January 2005
Application of Optimal Control and Fuzzy Theory for Dynamic Groundwater Remediation Design Chu, Hone-Jay; Chang, Liang-Cheng Water Resources Management, Vol. 23, Issue 4 https://doi.org/10.1007/s11269-008-9293-1	journal	July 2008
Groundwater-level prediction using multiple linear regression and artificial neural network techniques: a comparative assessment Sahoo, Sasmita; Jha, Madan K. Hydrogeology Journal, Vol. 21, Issue 8 https://doi.org/10.1007/s10040-013-1029-5	journal	October 2013
Assessment of Agricultural Water Management in Punjab, India, Using Bayesian Methods Russo, Tess A.; Devineni, Naresh; Lall, Upmanu Sustainability of Integrated Water Resources Management https://doi.org/10.1007/978-3-319-12194-9_9	book	January 2015
A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer Yoon, Heesung; Jun, Seong-Chun; Hyun, Yunjung Journal of Hydrology, Vol. 396, Issue 1-2 https://doi.org/10.1016/j.jhydrol.2010.11.002	journal	January 2011
Optimal groundwater remediation design using an Adaptive Neural Network Genetic Algorithm: OPTIMAL GROUNDWATER REMEDIATION DESIGN Yan, Shengquan; Minsker, Barbara Water Resources Research, Vol. 42, Issue 5 https://doi.org/10.1029/2005WR004303	journal	May 2006
Artificial Neural Network Approach for Predicting Transient Water Levels in a Multilayered Groundwater System under Variable State, Pumping, and Climate Conditions Coppola, Emery; Szidarovszky, Ferenc; Poulton, Mary Journal of Hydrologic Engineering, Vol. 8, Issue 6 https://doi.org/10.1061/(ASCE)1084-0699(2003)8:6(348)	journal	November 2003
An Algorithm for Least-Squares Estimation of Nonlinear Parameters Marquardt, Donald W. Journal of the Society for Industrial and Applied Mathematics, Vol. 11, Issue 2 https://doi.org/10.1137/0111030	journal	June 1963
Potential application of wavelet neural network ensemble to forecast streamflow for flood management Kasiviswanathan, K. S.; He, Jianxun; Sudheer, K. P. Journal of Hydrology, Vol. 536 https://doi.org/10.1016/j.jhydrol.2016.02.044	journal	May 2016
Modeling daily discharge responses of a large karstic aquifer using soft computing methods: Artificial neural network and neuro-fuzzy Kurtulus, Bedri; Razack, Moumtaz Journal of Hydrology, Vol. 381, Issue 1-2 https://doi.org/10.1016/j.jhydrol.2009.11.029	journal	February 2010
Peak groundwater depletion in the High Plains Aquifer, projections from 1930 to 2110 Steward, David R.; Allen, Andrew J. Agricultural Water Management, Vol. 170 https://doi.org/10.1016/j.agwat.2015.10.003	journal	May 2016
Wavelet-entropy data pre-processing approach for ANN-based groundwater level modeling Nourani, Vahid; Alami, Mohammad Taghi; Vousoughi, Farnaz Daneshvar Journal of Hydrology, Vol. 524 https://doi.org/10.1016/j.jhydrol.2015.02.048	journal	May 2015
Seasonal to interannual rainfall probabilistic forecasts for improved water supply management: Part 1 — A strategy for system predictor identification Sharma, A. Journal of Hydrology, Vol. 239, Issue 1-4 https://doi.org/10.1016/S0022-1694(00)00346-2	journal	December 2000
A wavelet neural network conjunction model for groundwater level forecasting Adamowski, Jan; Chan, Hiu Fung Journal of Hydrology, Vol. 407, Issue 1-4 https://doi.org/10.1016/j.jhydrol.2011.06.013	journal	September 2011
Predicting water table depths in space and time using a regionalised time series model Knotters, Martin; Bierkens, Marc F. P. Geoderma, Vol. 103, Issue 1-2 https://doi.org/10.1016/S0016-7061(01)00069-6	journal	September 2001
Multilayer feedforward networks are universal approximators Hornik, Kurt; Stinchcombe, Maxwell; White, Halbert Neural Networks, Vol. 2, Issue 5 https://doi.org/10.1016/0893-6080(89)90020-8	journal	January 1989
A method for the solution of certain non-linear problems in least squares Levenberg, Kenneth Quarterly of Applied Mathematics, Vol. 2, Issue 2 https://doi.org/10.1090/qam/10666	journal	January 1944
Artificial neural networks as rainfall-runoff models Minns, A. W.; Hall, M. J. Hydrological Sciences Journal, Vol. 41, Issue 3 https://doi.org/10.1080/02626669609491511	journal	June 1996
Simulation/Optimization Modeling for Robust Pumping Strategy Design Kalwij, Ineke M.; Peralta, Richard C. Ground Water, Vol. 44, Issue 4 https://doi.org/10.1111/j.1745-6584.2006.00176.x	journal	May 2006
Simulation of Ground-Water Flow and Effects of Ground-Water Irrigation on Base Flow in the Elkhorn and Loup River Basins, Nebraska Peterson, Steven M.; Stanton, Jennifer S.; Saunders, Amanda T. https://doi.org/10.3133/sir20085143	report	January 2008
Relations between climatic variability and hydrologic time series from four alluvial basins across the southwestern United States Hanson, R. T.; Dettinger, M. D.; Newhouse, M. W. Hydrogeology Journal, Vol. 14, Issue 7 https://doi.org/10.1007/s10040-006-0067-7	journal	August 2006
Dry matter and qualitative characteristics of alfalfa as affected by harvest times and soil water content Testa, G.; Gresta, F.; Cosentino, S. L. European Journal of Agronomy, Vol. 34, Issue 3 https://doi.org/10.1016/j.eja.2010.12.001	journal	April 2011
Realistic forecasting of groundwater level, based on the eigenstructure of aquifer dynamics Bidwell, V. J. Mathematics and Computers in Simulation, Vol. 69, Issue 1-2 https://doi.org/10.1016/j.matcom.2005.02.023	journal	June 2005
Depletion and response of deep groundwater to climate-induced pumping variability Russo, Tess A.; Lall, Upmanu Nature Geoscience, Vol. 10, Issue 2 https://doi.org/10.1038/ngeo2883	journal	January 2017
Beneath the surface of global change: Impacts of climate change on groundwater Green, Timothy R.; Taniguchi, Makoto; Kooi, Henk Journal of Hydrology, Vol. 405, Issue 3-4 https://doi.org/10.1016/j.jhydrol.2011.05.002	journal	August 2011
Singular-spectrum analysis: A toolkit for short, noisy chaotic signals Vautard, Robert; Yiou, Pascal; Ghil, Michael Physica D: Nonlinear Phenomena, Vol. 58, Issue 1-4 https://doi.org/10.1016/0167-2789(92)90103-T	journal	September 1992
Methods to improve neural network performance in daily flows prediction Wu, C. L.; Chau, K. W.; Li, Y. S. Journal of Hydrology, Vol. 372, Issue 1-4 https://doi.org/10.1016/j.jhydrol.2009.03.038	journal	June 2009
Groundwater level response in U.S. principal aquifers to ENSO, NAO, PDO, and AMO Kuss, Amber Jean M.; Gurdak, Jason J. Journal of Hydrology, Vol. 519 https://doi.org/10.1016/j.jhydrol.2014.09.069	journal	November 2014
Water Level Declines in the High Plains Aquifer: Predevelopment to Resource Senescence: Ground Water xx, no. x: xx-xx Haacker, Erin M. K.; Kendall, Anthony D.; Hyndman, David W. Groundwater, Vol. 54, Issue 2 https://doi.org/10.1111/gwat.12350	journal	May 2015
Novel Maximum-Margin Training Algorithms for Supervised Neural Networks Ludwig, Oswaldo; Nunes, Urbano IEEE Transactions on Neural Networks, Vol. 21, Issue 6 https://doi.org/10.1109/TNN.2010.2046423	journal	June 2010
Water-level and storage changes in the High Plains aquifer, predevelopment to 2011 and 2009-11 McGuire, Virginia L. https://doi.org/10.3133/sir20125291	report	January 2013
Ground water and climate change Taylor, Richard G.; Scanlon, Bridget; Döll, Petra Nature Climate Change, Vol. 3, Issue 4 https://doi.org/10.1038/nclimate1744	journal	November 2012
Global depletion of groundwater resources: GLOBAL GROUNDWATER DEPLETION Wada, Yoshihide; van Beek, Ludovicus P. H.; van Kempen, Cheryl M. Geophysical Research Letters, Vol. 37, Issue 20 https://doi.org/10.1029/2010GL044571	journal	October 2010
Comparative study of monthly inflow prediction methods for the Three Gorges Reservoir Wang, Yun; Guo, Shenglian; Chen, Hua Stochastic Environmental Research and Risk Assessment, Vol. 28, Issue 3 https://doi.org/10.1007/s00477-013-0772-4	journal	July 2013
An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data Olden, Julian D.; Joy, Michael K.; Death, Russell G. Ecological Modelling, Vol. 178, Issue 3-4 https://doi.org/10.1016/j.ecolmodel.2004.03.013	journal	November 2004
Water-level changes in the High Plains aquifer, predevelopment to 2002, 1980 to 2002, and 2001 to 2002 McGuire, Virginia L. https://doi.org/10.3133/fs20043026	report	January 2004
Artificial neural networks as an alternative approach to groundwater numerical modelling and environmental design Nikolos, Ioannis K.; Stergiadi, Maria; Papadopoulou, Maria P. Hydrological Processes, Vol. 22, Issue 17 https://doi.org/10.1002/hyp.6916	journal	August 2008
Extraction of information content from stochastic disaggregation and bias corrected downscaled precipitation variables for crop simulation Mishra, Ashok K.; Ines, Amor V. M.; Singh, Vijay P. Stochastic Environmental Research and Risk Assessment, Vol. 27, Issue 2 https://doi.org/10.1007/s00477-012-0667-9	journal	November 2012
Flow and Storage in Groundwater Systems Alley, W. M. Science, Vol. 296, Issue 5575 https://doi.org/10.1126/science.1067123	journal	June 2002
A novel approach to parameter uncertainty analysis of hydrological models using neural networks Shrestha, D. L.; Kayastha, N.; Solomatine, D. P. Hydrology and Earth System Sciences, Vol. 13, Issue 7 https://doi.org/10.5194/hess-13-1235-2009	journal	January 2009
Selection of input variables for data driven models: An average shifted histogram partial mutual information estimator approach Fernando, T. M. K. G.; Maier, H. R.; Dandy, G. C. Journal of Hydrology, Vol. 367, Issue 3-4 https://doi.org/10.1016/j.jhydrol.2008.10.019	journal	April 2009
Water-level changes and change in water in storage in the High Plains aquifer, predevelopment to 2013 and 2011-13 McGuire, Virginia L. https://doi.org/10.3133/sir20145218	report	January 2014
On the statistical forecasting of groundwater levels in unconfined aquifer systems Sahoo, Sasmita; Jha, Madan K. Environmental Earth Sciences, Vol. 73, Issue 7 https://doi.org/10.1007/s12665-014-3608-8	journal	September 2014
Improved methods for satellite-based groundwater storage estimates: A decade of monitoring the high plains aquifer from space and ground observations: IMPROVING GROUNDWATER STORAGE ESTIMATES Breña-Naranjo, Jose Agustin; Kendall, Anthony D.; Hyndman, David W. Geophysical Research Letters, Vol. 41, Issue 17 https://doi.org/10.1002/2014GL061213	journal	September 2014
Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications Maier, Holger R.; Dandy, Graeme C. Environmental Modelling & Software, Vol. 15, Issue 1 https://doi.org/10.1016/S1364-8152(99)00007-9	journal	January 2000
Estimated use of water in the United States in 2010 Maupin, Molly A.; Kenny, Joan F.; Hutson, Susan S. https://doi.org/10.3133/cir1405	report	January 2014
Software expedites singular‐spectrum analysis of noisy time series Dettinger, Michael D.; Ghil, Michael; Strong, Christopher M. Eos, Transactions American Geophysical Union, Vol. 76, Issue 2 https://doi.org/10.1029/EO076i002p00012	journal	January 1995
Optimal planning of a dynamic pump-treat-inject groundwater remediation system Chang, Liang-Cheng; Chu, Hone-Jay; Hsiao, Chin-Tsai Journal of Hydrology, Vol. 342, Issue 3-4 https://doi.org/10.1016/j.jhydrol.2007.05.030	journal	September 2007
The DSSAT cropping system model Jones, J. W.; Hoogenboom, G.; Porter, C. H. European Journal of Agronomy, Vol. 18, Issue 3-4 https://doi.org/10.1016/S1161-0301(02)00107-7	journal	January 2003
A Technique for Maximizing Details in Numerical Weather Map Analysis Barnes, Stanley L. Journal of Applied Meteorology, Vol. 3, Issue 4 https://doi.org/10.1175/1520-0450(1964)003<0396:ATFMDI>2.0.CO;2	journal	August 1964
An evaluation framework for input variable selection algorithms for environmental data-driven models Galelli, Stefano; Humphrey, Greer B.; Maier, Holger R. Environmental Modelling & Software, Vol. 62 https://doi.org/10.1016/j.envsoft.2014.08.015	journal	December 2014
Bootstrap rank-ordered conditional mutual information (broCMI): A nonlinear input variable selection method for water resources modeling: BOOTSTRAP RANK-ORDERED INPUT VARIABLE SELECTION Quilty, John; Adamowski, Jan; Khalil, Bahaa Water Resources Research, Vol. 52, Issue 3 https://doi.org/10.1002/2015WR016959	journal	March 2016

Similar Records

Accounting for uncertainty in complex alluvial aquifer modeling by Bayesian multi-model approach

Journal Article · Thu Jul 15 00:00:00 EDT 2021 · Journal of Hydrology · OSTI ID:1357813

Yin, Jina; Tsai, Frank T.-C.; Kao, Shih-Chieh

Quantifying the Impact of Lagged Hydrological Responses on the Effectiveness of Groundwater Conservation

Journal Article · Fri Jul 01 00:00:00 EDT 2022 · Water Resources Research · OSTI ID:1357813

Glose, Thomas J.; Zipper, Sam C.; Hyndman, David W.; +3 more

Physical Controls on Irrigation Return Flow Contributions to Stream Flow in Irrigated Alluvial Valleys

Journal Article · Wed Apr 27 00:00:00 EDT 2022 · Frontiers in Water · OSTI ID:1357813

Ferencz, Stephen B.; Tidwell, Vincent C.

Related Subjects

60 APPLIED LIFE SCIENCES
97 MATHEMATICS AND COMPUTING
58 GEOSCIENCES

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

Citation Formats

References (73)

Similar Records

Related Subjects