Comparison of regression models, grey models, and supervised learning models for forecasting flood stage caused by typhoon events

In this paper, four simple dynamic prediction methods and two supervised learning techniques including a linear regression model, a quadratic regression model, an original grey prediction model, a modified grey prediction model, a back-propagation neural network model, and an epsilon-SVM regression model were investigated for the forecasting of flood stage one hour ahead for early warning of flooding hazards. Quantitative evaluations were carried out between the predicted values by using the six forecasting models and the measured values obtained in the field. The comparisons confirm the ability of the simple grey prediction model to forecast flood stage by using only three observations of water stage with reasonable accuracy for the study cases, especially for study areas with scanty hydrological data.