| Summary: | The rainstorm is believed to contribute flood disasters in upstream catchments, resulting in further consequences in downstream area due to rise of river water levels. Forecasting for flood water level has been challenging, presenting complex task due to its nonlinearities and dependencies. This study proposes a support vector machine regression model, regarded as a powerful machine learningbased technique to forecast flood water levels in downstream area for different lead times. As a case study, Kelantan River in Malaysia has been selected to validate the proposed model. Four water level stations in river basin upstream were identified as input variables. A river water level in downstream area was selected as output of flood forecasting model. A comparison with several benchmarking models, including radial basis function (RBF) and nonlinear autoregressive with exogenous input (NARX) neural network was performed. The results demonstrated that in terms of RMSE error, NARX model was better for the proposed models. However, support vector regression (SVR) demonstrated a more consistent performance, indicated by the highest coefficient of determination value in twelve-hour period ahead of forecasting time. The findings of this study signified that SVR was more capable of addressing the long-term flood forecasting problems.
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