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Using Artificial Neural Network Approach for Simultaneous Forecasting of Weekly Groundwater Levels at Multiple Sites

Author

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  • S. Mohanty
  • Madan Jha
  • S. Raul
  • R. Panda
  • K. Sudheer

Abstract

Reliable forecast of groundwater level is necessary for its sustainable use and for planning land and water management strategies. This paper deals with an application of artificial neural network (ANN) approach to the weekly forecasting of groundwater levels in multiple wells located over a river basin. Gradient descent with momentum and adaptive learning rate backpropagation (GDX) algorithm was employed to predict groundwater levels 1 week ahead at 18 sites over the study area. Based on the domain knowledge and pertinent statistical analysis, appropriate set of inputs for the ANN model was selected. This consisted of weekly rainfall, pan evaporation, river stage, water level in the surface drain, pumping rates of 18 sites and groundwater levels of 18 sites in the previous week, which led to 40 input nodes and 18 output nodes. During training of the ANN model, the optimum number of hidden neurons was found to be 40 and the model performance was found satisfactory (RMSE = 0.2397 m, r = 0.9861, and NSE = 0.9722). During testing of the model, the values of statistical indicators RMSE, r and NSE were 0.4118 m, 0.9715 and 0.9288, respectively. Using the same inputs, the developed ANN model was further used for forecasting groundwater levels 2, 3 and 4 weeks ahead in 18 tubewells. The model performance was better while forecasting groundwater levels at shorter lead times (up to 2 weeks) than that for larger lead times. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • S. Mohanty & Madan Jha & S. Raul & R. Panda & K. Sudheer, 2015. "Using Artificial Neural Network Approach for Simultaneous Forecasting of Weekly Groundwater Levels at Multiple Sites," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(15), pages 5521-5532, December.
    • Handle: RePEc:spr:waterr:v:29:y:2015:i:15:p:5521-5532
    DOI: 10.1007/s11269-015-1132-6
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    References listed on IDEAS

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    1. Purna Nayak & Y. Rao & K. Sudheer, 2006. "Groundwater Level Forecasting in a Shallow Aquifer Using Artificial Neural Network Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 20(1), pages 77-90, February.
    2. 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.
    3. Zhenfang He & Yaonan Zhang & Qingchun Guo & Xueru Zhao, 2014. "Comparative Study of Artificial Neural Networks and Wavelet Artificial Neural Networks for Groundwater Depth Data Forecasting with Various Curve Fractal Dimensions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(15), pages 5297-5317, December.
    4. 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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    1. Afshin Khoshand, 2021. "Application of artificial intelligence in groundwater ecosystem protection: a case study of Semnan/Sorkheh plain, Iran," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(11), pages 16617-16631, November.
    2. Kusum Pandey & Shiv Kumar & Anurag Malik & Alban Kuriqi, 2020. "Artificial Neural Network Optimized with a Genetic Algorithm for Seasonal Groundwater Table Depth Prediction in Uttar Pradesh, India," Sustainability, MDPI, vol. 12(21), pages 1-24, October.
    3. Bing-Chen Jhong & Jhih-Huang Wang & Gwo-Fong Lin, 2016. "Improving the Long Lead-Time Inundation Forecasts Using Effective Typhoon Characteristics," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(12), pages 4247-4271, September.
    4. Akram Seifi & Mohammad Ehteram & Vijay P. Singh & Amir Mosavi, 2020. "Modeling and Uncertainty Analysis of Groundwater Level Using Six Evolutionary Optimization Algorithms Hybridized with ANFIS, SVM, and ANN," Sustainability, MDPI, vol. 12(10), pages 1-42, May.
    5. 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.
    6. Ozgur Kisi & Meysam Alizamir & Mohammad Zounemat-Kermani, 2017. "Modeling groundwater fluctuations by three different evolutionary neural network techniques using hydroclimatic data," 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. 87(1), pages 367-381, May.
    7. 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.
    8. Godahewa, Rakshitha & Bergmeir, Christoph & Webb, Geoffrey I. & Montero-Manso, Pablo, 2023. "An accurate and fully-automated ensemble model for weekly time series forecasting," International Journal of Forecasting, Elsevier, vol. 39(2), pages 641-658.
    9. Mohammad Naderianfar & Jamshid Piri & Ozgur Kisi, 2017. "Pre-processing data to predict groundwater levels using the fuzzy standardized evapotranspiration and precipitation index (SEPI)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(14), pages 4433-4448, November.
    10. Haijiao Yu & Xiaohu Wen & Qi Feng & Ravinesh C. Deo & Jianhua Si & Min Wu, 2018. "Comparative Study of Hybrid-Wavelet Artificial Intelligence Models for Monthly Groundwater Depth Forecasting in Extreme Arid Regions, Northwest China," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(1), pages 301-323, January.
    11. Wen-Ping Tsai & Yen-Ming Chiang & Jun-Lin Huang & Fi-John Chang, 2016. "Exploring the Mechanism of Surface and Ground Water through Data-Driven Techniques with Sensitivity Analysis for Water Resources Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(13), pages 4789-4806, October.
    12. Siriporn Supratid & Thannob Aribarg & Seree Supharatid, 2017. "An Integration of Stationary Wavelet Transform and Nonlinear Autoregressive Neural Network with Exogenous Input for Baseline and Future Forecasting of Reservoir Inflow," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(12), pages 4023-4043, September.
    13. Ao, Chang & Zeng, Wenzhi & Wu, Lifeng & Qian, Long & Srivastava, Amit Kumar & Gaiser, Thomas, 2021. "Time-delayed machine learning models for estimating groundwater depth in the Hetao Irrigation District, China," Agricultural Water Management, Elsevier, vol. 255(C).

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