A Novel Approach for Water Resources Management to Accurate River Flow Prediction: Utilization of Linear Programming and Weighted Ensemble Learning

Recent years have seen significant advancements in the field of water resources management (WRM). It is notable that river flow regimes have undergone abrupt shifts, which have led to an increase in turbulence. This study presents a new approach to ensemble machine learning (EML) that utilizes a weighted-based machine learning (ML) framework to create an optimized ensemble model. The study concentrates on the Kashkan River basin in Lorestan Province, Iran. The primary dataset for this study was historical river discharge data obtained from the Iran Water Resources Company (IWRC). Between 2017 and 2018, 2,068 monthly river Debi measurements were examined for analysis. This study utilizes time-series modeling by determining the effect lags and subjecting them to ML and EML models to simulate the stream flow. To enhance predictive accuracy, three ensemble models were developed: Weighted Ensemble Machine Learning (WEML), Linear-Programmed Ensemble Machine Learning (LPEML), and Linear-Programmed Weighted Ensemble Machine Learning (LPWEML). According to the evaluation results, the WEML and LPEML models demonstrated the lowest computational errors, achieving R² values of 0.939 and 0.934, respectively. The efficiency of WEML and LPEML models can be seen through validation approaches. However, the LPWEML performed poorly compared to other MLs. According to these findings, the proposed methodologies are effective in increasing the accuracy of streamflow prediction.