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Reservoir Inflow Prediction: A Comparison between Semi Distributed Numerical and Artificial Neural Network Modelling

Author

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  • Mahesh Shelke

    (Vishwakarma Institute of Information Technology)

  • S. N. Londhe

    (Vishwakarma Institute of Information Technology)

  • P. R. Dixit

    (Vishwakarma Institute of Information Technology)

  • Pravin Kolhe

    (Water Resource Department, Government of Maharashtra)

Abstract

Reservoir inflow is a major component of the reservoir operations management system. It becomes highly essential to predict the accurate reservoir inflow. The lumped models and semi-distributed or fully distributed model implemented to solve a range of specific problems in the prediction of reservoir inflow. The findings in this paper compare a conceptual semi distributed Hydrologic Engineering Centre Hydrologic Modelling System (HEC-HMS) model and an ANN (Artificial Neural Network) based model for the prediction of inflow in the Koyna reservoir catchment, Maharashtra. The performance of the models is assessed using different statistical indicators such as Nash–Sutcliffe Efficiency (NSE), Root Mean Square Error (RMSE), Correlation Coefficient (r) and Mean Absolute Error (MAE). The results confirmed the ability of the semi distributed (rHEC-HMS = 0.92, RMSEHEC-HMS = 129.37 m3/s, MAEHEC-HMS = 21.66 m3/s, NSEHEC-HMS = 0.82 and RSRHEC-HMS = 0.42) and ANN model (rANN = 0.85, RMSEANN = 176.29 m3/s, MAEANN = 14.62 m3/s, NSEANN = 0.69 and RSRANN = 0.55) to capture the effect of the complex hydrological phenomenon, variations of land use and soils of watershed. The study illustrates that the semi distributed HEC-HMS model shows moderately better results compared to ANN model. It may be noted that the ANN predicts the reservoir inflow using only one input i.e., rainfall, whereas the HEC-HMS requires exogenous input parameters and plenty of time for model building compared to ANN. This work will have a significant contribution for planning of reservoir operations within the catchment of Koyna reservoir.

Suggested Citation

  • Mahesh Shelke & S. N. Londhe & P. R. Dixit & Pravin Kolhe, 2023. "Reservoir Inflow Prediction: A Comparison between Semi Distributed Numerical and Artificial Neural Network Modelling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(15), pages 6127-6143, December.
    • Handle: RePEc:spr:waterr:v:37:y:2023:i:15:d:10.1007_s11269-023-03646-2
    DOI: 10.1007/s11269-023-03646-2
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    References listed on IDEAS

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    1. Xiaoli Zhang & Haixia Wang & Anbang Peng & Wenchuan Wang & Baojian Li & Xudong Huang, 2020. "Quantifying the Uncertainties in Data-Driven Models for Reservoir Inflow Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(4), pages 1479-1493, March.
    2. Mohammad Babaei & Ramtin Moeini & Eghbal Ehsanzadeh, 2019. "Artificial Neural Network and Support Vector Machine Models for Inflow Prediction of Dam Reservoir (Case Study: Zayandehroud Dam Reservoir)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(6), pages 2203-2218, April.
    3. Sooyeon Yi & G. Mathias Kondolf & Samuel Sandoval-Solis & Larry Dale, 2022. "Application of Machine Learning-based Energy Use Forecasting for Inter-basin Water Transfer Project," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(14), pages 5675-5694, November.
    4. Laleh Parviz & Kabir Rasouli & Ali Torabi Haghighi, 2023. "Improving Hybrid Models for Precipitation Forecasting by Combining Nonlinear Machine Learning Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(10), pages 3833-3855, August.
    5. Maryam Zare & Mojtaba Pakparvar & Sajad Jamshidi & Omolbanin Bazrafshan & Gholamreza Ghahari, 2021. "Optimizing the Runoff Estimation with HEC-HMS Model Using Spatial Evapotranspiration by the SEBS Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(8), pages 2633-2648, June.
    6. Omid Bozorg-Haddad & Pouria Yari & Mohammad Delpasand & Xuefeng Chu, 2022. "Reservoir operation under influence of the joint uncertainty of inflow and evaporation," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(2), pages 2914-2940, February.
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