Hydrology Forecasting Model of Upper-lower Bound Estimation Based on Projection Pursuit Regression

Abstract

Due to its simple structure and easy calculation, upper-lower bound estimation method has been applied in more and more researches related to probabilistic hydrology forecast. Upper and lower bounds are usually estimated by artificial neural network method, namely forecast intervals. This thesis proposes an upper- lower bound interval estimation forecasting method based on projection pursuit regression model. Coverage ratio, forecasting interval width, and symmetry shall be set as forecast criterions for upper-lower bound forecast of runoff. This thesis selected the flow data monitored in Yichang Station which is on the upstream of the Yangtze River as research object. It introduced wavelet analysis method to conduct denoising process on input data. Then, this thesis compared the results respectively forecasted by projection pursuit upper-lower bound estimation model after wavelet denoising process, projection pursuit upper-lower bound estimation model, and upper-lower estimation model of neural network model. The results showed that: projection pursuit interval forecasting model after wavelet denoising process has similar effect to BP neural network model and they both have better effect than interval forecasting model only with projection pursuit regression model.