Abstract

Flood forecasting is very important research topic in disaster prevention and reduction. The characteristics of flood involve a rather complex systematic dynamic under the influence of different meteorological factors including linear and non-linear patterns. Recently there are many novel forecasting methods of improving the forecasting accuracy. This paper explores the potential and effect of the semiparametric regression to modelize flood water-level and to forecast the inundation of Mekong Delta in Vietnam. The semi-parametric regression technique is a combination of a parametric regression approach and a non-parametric regression concept. In the process of model building, three altered linear regression models are applied for the parametric component. They are stepwise multiple linear regression, partial least squares solution and multirecursive regression method. They are used to capture flood’s linear characteristics. The nonparametric part is solved by a modified estimation of a smooth function. Furthermore, some justified nonlinear regression models based on artificial neural network are also able to obtain flood’s non-linear characteristics. They help us to smooth the model's non-parametric constituent easily and quickly. The last element is the model's error. Then the semiparametric regression is used for ensemble model based on the principle component analysis technique. Flood water-level forecasting, with a lead time of one and more days, has been made by using a selected sequence of past water-level values and some relevant factors observed at a specific location. Time-series analytical method is utilized to build the model. Obtained empirical results indicate that the prediction by using the amended semi-parametric regression ensemble model is generally better than those obtained by using the other models presented in this study in terms of the same evaluation measurements. Our findings reveal that the estimation power of the modern statistical model is reliable and auspicious. The proposed model here can be used as a promising alternative forecasting tool for flood to achieve better forecasting accuracy and to optimize prediction quality further.