Abstract

Discharge prediction methods play crucial role in providing early warnings and helping local people and government agencies to prepare well before flood or managing available water for various purposes. The ability to predict future river flows helps people anticipate and plan for upcoming flooding, preventing deaths and decreasing property destruction. Different hydrological models supporting these predictions have different characteristics, driven by available data and the research area. This study applied two different types of Machine learning techniques to the Tikarpara station present in the lower end of the Mahanadi river basin India. The two Machine learning techniques include Multi-layer perception (MLP) and support vector regression (SVR) MLP has shown great deal of accuracy as compared to SVR across the cases used in the study; based on available data and the study area, MLP showed the best applicability, compared to SVR techniques. MLP out performed SVR model with r2 = 0.75 and lowest RMSE = 0.58.MLP can be used as a promising tool for forecasting monthly discharge at the selected station.

Keywords

MLP, SVR, Forecasting, Time series,

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