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Development and Evaluation of Hybrid Artificial Neural Network Architectures for Modeling Spatio-Temporal Groundwater Fluctuations in a Complex Aquifer System

Author

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  • Thendiyath Roshni

    (National Institute of Technology Patna)

  • Madan K. Jha

    (Indian Institute of Technology Kharagpur)

  • Ravinesh C. Deo

    (University of Southern Queensland)

  • A. Vandana

    (National Institute of Technology Patna)

Abstract

The proper design, development, and appropriate tuning of the Hybrid Neural Network architecture, mainly for its parsimoniousity and optimal training can help practitioners to generate a robust predictive tool for modeling several important hydrological processes within the water resources sector. In this paper, the Feedforward Artificial Neural Network (FFANN) and the hybrid WANN model have been developed, and later, coupled with the Gamma and M-tests (GT) approach for forecasting spatio-temporal groundwater fluctuations in a complex alluvial aquifer system. The performance of these hybrid models were evaluated using goodness-of-fit criteria. An analysis of the modeling results indicates that the GT coupled with the WANN model was able to provide significantly improved results, with lower values of the root mean square error (RMSE) and higher values of the NSE metric for the 1-week and 3-week lead times. Hence, utilizing this hybrid model, the groundwater level prediction tests were extended for 6-week and 12-week lead times with the GT approach, coupled with the WANN hybrid model only. The results showed that the accuracy of the GT-WANN hybrid model was better for the unconfined aquifer system compared to the leaky confined aquifer system. Furthermore, the present study also examined the interdependence between different model inputs and output variables for the selected study sites by means of the Wavelet Coherent Analysis (WCA). These results indicated that all the model’s input variables have a significant effect on the groundwater level of unconfined aquifers, and confirmed the nature of the aquifers tapped within the present study sites. The study finally concludes that the GT-WANN approach can be a robust predictive tool for modeling spatio-temporal fluctuations of groundwater levels.

Suggested Citation

  • Thendiyath Roshni & Madan K. Jha & Ravinesh C. Deo & A. Vandana, 2019. "Development and Evaluation of Hybrid Artificial Neural Network Architectures for Modeling Spatio-Temporal Groundwater Fluctuations in a Complex Aquifer System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(7), pages 2381-2397, May.
    • Handle: RePEc:spr:waterr:v:33:y:2019:i:7:d:10.1007_s11269-019-02253-4
    DOI: 10.1007/s11269-019-02253-4
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    References listed on IDEAS

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    1. Singh, Kunwar P. & Basant, Ankita & Malik, Amrita & Jain, Gunja, 2009. "Artificial neural network modeling of the river water quality—A case study," Ecological Modelling, Elsevier, vol. 220(6), pages 888-895.
    2. 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.
    3. Hui-cheng Zhou & Yong Peng & Guo-hua Liang, 2008. "The Research of Monthly Discharge Predictor-corrector Model Based on Wavelet Decomposition," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 22(2), pages 217-227, February.
    4. Madan Jha & K. Chikamori & Y. Kamii & Y. Yamasaki, 1999. "Field Investigations for Sustainable Groundwater Utilization in the Konan Basin," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 13(6), pages 443-470, December.
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    3. Saeideh Samani & Meysam Vadiati & Farahnaz Azizi & Efat Zamani & Ozgur Kisi, 2022. "Groundwater Level Simulation Using Soft Computing Methods with Emphasis on Major Meteorological Components," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(10), pages 3627-3647, August.
    4. 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.

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