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The Use of NARX Neural Networks to Forecast Daily Groundwater Levels

Author

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  • Sandra M. Guzman

    (Mississippi State University)

  • Joel O. Paz

    (Mississippi State University)

  • Mary Love M. Tagert

    (Mississippi State University)

Abstract

The lack of information to manage groundwater for irrigation is one of the biggest concerns for farmers and stakeholders in agricultural areas of Mississippi. In this study, we present a novel implementation of a nonlinear autoregressive with exogenous inputs (NARX) network to simulate daily groundwater levels at a local scale in the Mississippi River Valley Alluvial (MRVA) aquifer, located in the southeastern United States. The NARX network was trained using the Levenberg-Marquardt (LM) and Bayesian Regularization (BR) algorithms, and the results were compared to identify an optimal architecture for the forecasting of daily groundwater levels over time. The training algorithms were implemented using different hidden node combinations and delays (5, 25, 50, 75, and 100) until the optimal network was found. Eight years of daily historical input time series including precipitation and groundwater levels were used to forecast groundwater levels up to three months ahead. The comparison between LM and BR showed that NARX-BR is superior in forecasting daily levels based on the Mean Squared Error (MSE), coefficient of determination (R2), and Nash-Sutcliffe coefficient of efficiency. The results showed that BR with two hidden nodes and 100 time delays provided the most accurate prediction of groundwater levels with an error of ± 0.00119 m. This innovative study is the first of its kind and will provide significant contributions for the implementation of data-based models (DBMs) in the prediction and management of groundwater for agricultural use.

Suggested Citation

  • Sandra M. Guzman & Joel O. Paz & Mary Love M. Tagert, 2017. "The Use of NARX Neural Networks to Forecast Daily Groundwater Levels," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(5), pages 1591-1603, March.
    • Handle: RePEc:spr:waterr:v:31:y:2017:i:5:d:10.1007_s11269-017-1598-5
    DOI: 10.1007/s11269-017-1598-5
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    References listed on IDEAS

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    1. Sheelabhadra Mohanty & Madan Jha & Ashwani Kumar & K. Sudheer, 2010. "Artificial Neural Network Modeling for Groundwater Level Forecasting in a River Island of Eastern India," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(9), pages 1845-1865, July.
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    6. Samad Emamgholizadeh & Khadije Moslemi & Gholamhosein Karami, 2014. "Prediction the Groundwater Level of Bastam Plain (Iran) by Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(15), pages 5433-5446, December.
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    4. Georgios N. Kouziokas & Alexander Chatzigeorgiou & Konstantinos Perakis, 2018. "Multilayer Feed Forward Models in Groundwater Level Forecasting Using Meteorological Data in Public Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(15), pages 5041-5052, December.
    5. Jielong Wang & Yi Chen, 2022. "Using NARX neural network to forecast droughts and floods over Yangtze River Basin," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 110(1), pages 225-246, January.
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    8. Andreas Wunsch & Tanja Liesch & Stefan Broda, 2022. "Deep learning shows declining groundwater levels in Germany until 2100 due to climate change," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    9. Sangita Dey & Arabin Kumar Dey & Rajesh Kumar Mall, 2021. "Modeling Long-term Groundwater Levels By Exploring Deep Bidirectional Long Short-Term Memory using Hydro-climatic Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(10), pages 3395-3410, August.
    10. Dilip Kumar Roy & Sujit Kumar Biswas & Kowshik Kumar Saha & Khandakar Faisal Ibn Murad, 2021. "Groundwater Level Forecast Via a Discrete Space-State Modelling Approach as a Surrogate to Complex Groundwater Simulation Modelling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(6), pages 1653-1672, April.
    11. Jielong Wang & Yi Chen, 2022. "The applicability of using NARX neural network to forecast GRACE terrestrial water storage anomalies," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 110(3), pages 1997-2016, February.
    12. Salma Hamad Almuhaini & Nahid Sultana, 2023. "Forecasting Long-Term Electricity Consumption in Saudi Arabia Based on Statistical and Machine Learning Algorithms to Enhance Electric Power Supply Management," Energies, MDPI, vol. 16(4), pages 1-28, February.
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    14. Qi Liu & Yi Liu & Jie Niu & Dongwei Gui & Bill X. Hu, 2022. "Prediction of the Irrigation Area Carrying Capacity in the Tarim River Basin under Climate Change," Agriculture, MDPI, vol. 12(5), pages 1-14, April.

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