Abstract
Reservoirs play a crucial role in flood control by storing and regulating the water. Inflow forecasting in real-time is essential for the effective management of reservoir water. The emergence of high spatial resolution weather forecasts and explainable Machine Learning (eXML) algorithms remove the barriers to accurate forecasting and provide a better understanding of Machine Learning (ML) models. In this context, reservoir inflows are forecasted using eXML models and Global Forecasting System (GFS) data: precipitation, minimum and maximum temperature. Popular ML methods like Long Short Term Memory (LSTM), Multilayer Perceptron, Support Vectors Machine and Random Forest are used in this study. Further, these models' explainability is assessed using the Shapley additive explanations method. The ML models are trained and tested using the observed data from 2000 to 2018 in the Tenughat catchment, Damodar river basin, India. Among all the ML models, the LSTM model better predicted the inflows with an NSE value of 0.938. The eXML revealed that inflow and precipitation variables of 1-day lag significantly impact the models’ prediction in all models. The GFS data of 2017–2018 was bias-corrected using the Scaled Distribution Mapping method, and the method improved the GFS data significantly. The inflow forecasting using the LSTM model with the bias-corrected GFS data revealed that the LSTM model performed well up to 3-day lead time (NSE = 0.908, 0.888, 0.876 for 1–3 day lead). Thus, the LSTM model with the GFS forecasts has substantial potential for real-time forecasting of reservoir inflows.
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by KS, AM, and RS. The first draft of the manuscript was written by KS and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Sushanth, K., Mishra, A., Mukhopadhyay, P. et al. Near-real-time forecasting of reservoir inflows using explainable machine learning and short-term weather forecasts. Stoch Environ Res Risk Assess 37, 3945–3965 (2023). https://doi.org/10.1007/s00477-023-02489-y
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DOI: https://doi.org/10.1007/s00477-023-02489-y