Improving Short-range Reservoir Inflow Forecasts with Machine Learning Model Combination

M. Rajesh, Sachdeva Anishka, Pansari Satyam Viksit, Srivastav Arohi and S. Rehana ()
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M. Rajesh: Hydroclimatic Research Group, Lab for Spatial Informatics, International Institute of Information Technology
Sachdeva Anishka: Hydroclimatic Research Group, Lab for Spatial Informatics, International Institute of Information Technology
Pansari Satyam Viksit: Hydroclimatic Research Group, Lab for Spatial Informatics, International Institute of Information Technology
Srivastav Arohi: Hydroclimatic Research Group, Lab for Spatial Informatics, International Institute of Information Technology
S. Rehana: Hydroclimatic Research Group, Lab for Spatial Informatics, International Institute of Information Technology

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2023, vol. 37, issue 1, No 5, 75-90

Abstract: Abstract This paper presents a simple and effective framework to combine various data-driven machine learning (ML) algorithms for short-range reservoir inflow forecasting, including the large-scale climate phenomenon indices addressing forecasting uncertainty. Random Forest (RF), Gradient Boosting Regressor (GBR), K-Nearest Neighbors Regressor (KNN), and Long Short-Term Memory (LSTM) were employed for predicting daily reservoir inflows considering various climate phenomenon indices (e.g., Arctic Oscillation, North Atlantic Oscillation, and Southern Oscillation Index) and hydroclimatic variables (e.g., precipitation), accounting for time-lag effects. After training the individual ML algorithm, a framework was developed to create an ensemble model using a robust weighted voting regressor (VR) method to quantify forecasting uncertainty and to improve model performances. The results of the study reveal that, for 2-day forecasts, the LSTM approach has the greatest influence on prediction accuracy, followed comparably by each model. However, none of the four models seem to be noticeably superior to the VR method, regardless of the prediction lead time. The developed framework was examined on a tropical reservoir, Bhadra reservoir Tunga-Bhadra River, located in India.

Keywords: El Niño–Southern Oscillation (ENSO); Gradient Boosting; Long Short-Term Memory (LSTM); Machine Learning; Reservoir operation; Uncertainty (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11269-022-03356-1

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