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

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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.

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The recent techniques of profound learning based on deep learning (Long Short Term Memory Technique, Transformers, Attention Mechanism) may be implemented for improvised results.

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This research did not receive any specific grant from funding agencies in the public, commercial, or not-for profit sectors.

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Authors and Affiliations

  1. Vishwakarma Institute of Information Technology, Pune, India

    Mahesh Shelke

  2. Department of Civil Engineering, Vishwakarma Institute of Information Technology, Pune, India

    S. N. Londhe & P. R. Dixit

  3. Water Resource Department, Government of Maharashtra, Pune, India

    Pravin Kolhe

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  1. Mahesh Shelke
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  2. S. N. Londhe
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  3. P. R. Dixit
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  4. Pravin Kolhe
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Shelke, M., Londhe, S.N., Dixit, P.R. et al. Reservoir Inflow Prediction: A Comparison between Semi Distributed Numerical and Artificial Neural Network Modelling. Water Resour Manage 37, 6127–6143 (2023). https://doi.org/10.1007/s11269-023-03646-2

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