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

Author

Listed:
  • 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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    References listed on IDEAS

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    2. Shivam Saw & Prasoon Kumar Singh & Jaydev Kumar Mahato & Rohit Patel & Deep Shikha, 2025. "A modeling approach for the suitability evaluation and human health risk assessment of heavy metals dispersion in groundwater resources," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 27(3), pages 7871-7895, March.
    3. Yankun Liu & Mingliang Du & Xiaofei Ma & Shuting Hu & Ziyun Tuo, 2025. "Groundwater Level Prediction Using a Hybrid TCN–Transformer–LSTM Model and Multi-Source Data Fusion: A Case Study of the Kuitun River Basin, Xinjiang," Sustainability, MDPI, vol. 17(19), pages 1-17, September.
    4. 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).
    5. Wang, Sheng-Wei & Chen, Yen-Yu & Hsu, Shu-Han & Kao, Yu-Hsuan & Kimura, Masaomi & Chang, Li-chiu & Pan, Tzi-Wen & Ni, Chuen-Fa, 2025. "A case study on the application of a data-driven (XGBoost) approach on the environmental and socio-economic perspectives of agricultural groundwater management," Agricultural Water Management, Elsevier, vol. 318(C).

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