Monthly stream flow prediction: the power of ensemble machine learning-based decision support models
by Hamit Erdal; Ersin Namli
International Journal of Hydrology Science and Technology (IJHST), Vol. 16, No. 1, 2023

Abstract: Predicting stream flow is a vital milestone in planning and managing water resources, and is important to researchers and hydrologists. Unpredicted stream flow threads cultivated areas, dams and riverside lands. Recently, the increasing popularity of machine learning (ML) methods including ensemble methods in hydrological prediction is noticeable. In this study, five single and three ensembles ML-based 18 prediction models and six performance evaluation measurements are utilised for monthly stream flow prediction. It proved that models developed by stacking and voting ensemble ML methods have higher prediction accuracy. As a conclusion, this paper has presented the promising endeavour of incorporating sentiment regression into stream flow prediction.

Online publication date: Tue, 04-Jul-2023

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