Evaluation of Seasonally Classified Inputs for the Prediction of Daily Groundwater Levels: NARX Networks Vs Support Vector Machines

Farmers and stakeholders who use groundwater for irrigation need efficient and cost-effective tools to support sustainable crop production. The variation of groundwater levels at local scale and its continuous use for agriculture intensifies the demand for reliable groundwater information. However, groundwater levels are very dynamic and difficult to predict under traditional modeling approaches, and manual monitoring of wells is costly and time-consuming. Simplified but powerful machine learning models represent a practical alternative to support groundwater management decisions at the farm scale. The predictive capacity of a nonlinear autoregressive with exogenous inputs (NARX) artificial neural network (ANN) and a support vector regression (SVR) trained with a radial basis function (RBF) algorithm were evaluated for an irrigation well located in a highly productive agricultural region in the southeastern USA. We used separately multiple years of daily historical time series classified by summer (withdrawal) and winter (recharge) seasons and evaluated the impacts of this division in the models’ predictive capability. Results showed that SVR had a better modeling performance based on the mean squared error (MSE) and prediction trend for both seasons. In addition, our study suggests that the prediction of daily levels with input time series classified by seasons provides higher accuracy than using the entire withdrawal and recharge periods as a whole. Results also indicate that the recharge season becomes a linear problem, which substantially reduces the SVR modeling computational requirements. The application of our proposed modeling approach in the management of groundwater sources for irrigation provides important information, at short time scale, for the estimation of groundwater variability at local scale.

[1]  Sandra M. Guzmán,et al.  The Use of NARX Neural Networks to Forecast Daily Groundwater Levels , 2017, Water Resources Management.

[2]  K. Davary,et al.  Application of NN-ARX Model to Predict Groundwater Levels in the Neishaboor Plain, Iran , 2013, Water Resources Management.

[3]  J. Paz,et al.  Evaluation of Various Methods for Estimating Global Solar Radiation in the Southeastern United States , 2012 .

[4]  B. Bobée,et al.  Artificial neural network modeling of water table depth fluctuations , 2001 .

[5]  Dimitri P. Solomatine,et al.  Model Induction with Support Vector Machines: Introduction and Applications , 2001 .

[6]  Mohammad Karamouz,et al.  Monthly Water Resources and Irrigation Planning: Case Study of Conjunctive Use of Surface and Groundwater Resources , 2004 .

[7]  Andrew E. Mercer,et al.  An integrated SVR and crop model to estimate the impacts of irrigation on daily groundwater levels , 2018 .

[8]  P. C. Nayak,et al.  Groundwater Level Forecasting in a Shallow Aquifer Using Artificial Neural Network Approach , 2006 .

[9]  R. Reedy,et al.  Groundwater depletion and sustainability of irrigation in the US High Plains and Central Valley , 2012, Proceedings of the National Academy of Sciences.

[10]  Holger R. Maier,et al.  Neural networks for the prediction and forecasting of water resource variables: a review of modelling issues and applications , 2000, Environ. Model. Softw..

[11]  D. Basak,et al.  Support Vector Regression , 2008 .

[12]  Paresh Chandra Deka,et al.  Support vector machine applications in the field of hydrology: A review , 2014, Appl. Soft Comput..

[13]  Mac McKee,et al.  Multi-time scale stream flow predictions: The support vector machines approach , 2006 .

[14]  A. Romanelli,et al.  Assessing groundwater pollution hazard changes under different socio-economic and environmental scenarios in an agricultural watershed. , 2015, The Science of the total environment.

[15]  Adam P. Piotrowski,et al.  A comparison of methods to avoid overfitting in neural networks training in the case of catchment runoff modelling , 2013 .

[16]  A. Mercer,et al.  Identification of recharge zones in the Lower Mississippi River alluvial aquifer using high-resolution precipitation estimates , 2015 .

[17]  MohammadSajjad Khan,et al.  Application of Support Vector Machine in Lake Water Level Prediction , 2006 .

[18]  Nancy L. Barber,et al.  Estimated withdrawals from principal aquifers in the United States, 2000 , 2005 .

[19]  M. Janga Reddy,et al.  Ensemble prediction of regional droughts using climate inputs and the SVM–copula approach , 2014 .

[20]  Mitigating a Commons Dilemma: Agricultural Water Use in the Mississippi Delta , 2017 .

[21]  I-Fan Chang,et al.  Support vector regression for real-time flood stage forecasting , 2006 .

[22]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[23]  Vladimir Vapnik,et al.  Support-vector networks , 2004, Machine Learning.

[24]  Dawei Han,et al.  Identification of Support Vector Machines for Runoff Modelling , 2004 .

[25]  Andrew E. Mercer,et al.  Artificial Neural Networks and Support Vector Machines: Contrast Study for Groundwater Level Prediction. , 2015 .

[26]  Jan Adamowski,et al.  Comparison of machine learning models for predicting fluoride contamination in groundwater , 2017, Stochastic Environmental Research and Risk Assessment.

[27]  Paulin Coulibaly,et al.  Groundwater level forecasting using artificial neural networks , 2005 .

[28]  Yunqian Ma,et al.  Practical selection of SVM parameters and noise estimation for SVM regression , 2004, Neural Networks.

[29]  Ping Li,et al.  Application and comparison of two prediction models for groundwater levels: a case study in Western Jilin Province, China. , 2009 .

[30]  Martin F. Lambert,et al.  Bayesian training of artificial neural networks used for water resources modeling , 2005 .

[31]  Y. Ouyang,et al.  Evaluating the impacts of crop rotations on groundwater storage and recharge in an agricultural watershed , 2016 .

[32]  Rahim Barzegar,et al.  Forecasting of groundwater level fluctuations using ensemble hybrid multi-wavelet neural network-based models. , 2017, The Science of the total environment.

[33]  K. P. Sudheer,et al.  A neuro-fuzzy computing technique for modeling hydrological time series , 2004 .

[34]  K. Lee,et al.  A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer , 2011 .

[35]  Maziar Palhang,et al.  Generalization performance of support vector machines and neural networks in runoff modeling , 2009, Expert Syst. Appl..

[36]  Majid Sartaj,et al.  Predicting Nitrate Concentration and Its Spatial Distribution in Groundwater Resources Using Support Vector Machines (SVMs) Model , 2015, Environmental Modeling & Assessment.