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Artificial Neural Network Optimized with a Genetic Algorithm for Seasonal Groundwater Table Depth Prediction in Uttar Pradesh, India

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  • Kusum Pandey

    (Department of Soil and Water Engineering, Punjab Agricultural University, Ludhiana 141004, Punjab, India)

  • Shiv Kumar

    (Department of Irrigation and Drainage Engineering, College of Technology, G.B. Pant University of Agriculture & Technology, Pantnagar 263145, Uttarakhand, India)

  • Anurag Malik

    (Punjab Agricultural University, Regional Research Station, Bathinda 151001, Punjab, India)

  • Alban Kuriqi

    (CERIS, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal)

Abstract

Accurate information about groundwater level prediction is crucial for effective planning and management of groundwater resources. In the present study, the Artificial Neural Network (ANN), optimized with a Genetic Algorithm (GA-ANN), was employed for seasonal groundwater table depth (GWTD) prediction in the area between the Ganga and Hindon rivers located in Uttar Pradesh State, India. A total of 18 models for both seasons (nine for the pre-monsoon and nine for the post-monsoon) have been formulated by using groundwater recharge (GW R ), groundwater discharge (GW D ), and previous groundwater level data from a 21-year period (1994–2014). The hybrid GA-ANN models’ predictive ability was evaluated against the traditional GA models based on statistical indicators and visual inspection. The results appraisal indicates that the hybrid GA-ANN models outperformed the GA models for predicting the seasonal GWTD in the study region. Overall, the hybrid GA-ANN-8 model with an 8-9-1 structure (i.e., 8: inputs, 9: neurons in the hidden layer, and 1: output) was nominated optimal for predicting the GWTD during pre- and post-monsoon seasons. Additionally, it was noted that the maximum number of input variables in the hybrid GA-ANN approach improved the prediction accuracy. In conclusion, the proposed hybrid GA-ANN model’s findings could be readily transferable or implemented in other parts of the world, specifically those with similar geology and hydrogeology conditions for sustainable planning and groundwater resources management.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:21:p:8932-:d:435670
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    1. Amarasinghe, Upali A. & Smakhtin, Vladimir., 2014. "Global water demand projections: past, present and future," IWMI Research Reports H046577, International Water Management Institute.
    2. Anurag Malik & Anil Kumar & Sinan Q Salih & Sungwon Kim & Nam Won Kim & Zaher Mundher Yaseen & Vijay P Singh, 2020. "Drought index prediction using advanced fuzzy logic model: Regional case study over Kumaon in India," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-31, May.
    3. Partha Majumder & T.I. Eldho, 2020. "Artificial Neural Network and Grey Wolf Optimizer Based Surrogate Simulation-Optimization Model for Groundwater Remediation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(2), pages 763-783, January.
    4. Anurag Malik & Anil Kumar & Rajesh P. Singh, 2019. "Application of Heuristic Approaches for Prediction of Hydrological Drought Using Multi-scalar Streamflow Drought Index," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(11), pages 3985-4006, September.
    5. Amarasinghe, Upali A. & Smakhtin, Vladimir, 2014. "Global water demand projections: past, present and future," IWMI Reports 201006, International Water Management Institute.
    6. 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.
    7. 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.
    8. 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.
    9. Yicheng Gong & Yongxiang Zhang & Shuangshuang Lan & Huan Wang, 2016. "A Comparative Study of Artificial Neural Networks, Support Vector Machines and Adaptive Neuro Fuzzy Inference System for Forecasting Groundwater Levels near Lake Okeechobee, Florida," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 375-391, January.
    10. Yicheng Gong & Yongxiang Zhang & Shuangshuang Lan & Huan Wang, 2016. "A Comparative Study of Artificial Neural Networks, Support Vector Machines and Adaptive Neuro Fuzzy Inference System for Forecasting Groundwater Levels near Lake Okeechobee, Florida," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 375-391, January.
    11. Shishir Gaur & Sudheer Ch & Didier Graillot & B. Chahar & D. Kumar, 2013. "Application of Artificial Neural Networks and Particle Swarm Optimization for the Management of Groundwater Resources," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(3), pages 927-941, February.
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    1. Priyanka Majumder & Arnab Paul & Pratik Saha & Mrinmoy Majumder & Dayarnab Baidya & Dhritiman Saha, 2023. "Trapezoidal fuzzy BWM-TOPSIS approach and application on water resources," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(3), pages 2648-2669, March.
    2. Vishwakarma, Dinesh Kumar & Pandey, Kusum & Kaur, Arshdeep & Kushwaha, N.L. & Kumar, Rohitashw & Ali, Rawshan & Elbeltagi, Ahmed & Kuriqi, Alban, 2022. "Methods to estimate evapotranspiration in humid and subtropical climate conditions," Agricultural Water Management, Elsevier, vol. 261(C).

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