Rainfall Forecasting Using Projection Pursuit Regression and Neural Networks
Accurate forecasting of rainfall has been one of the most important issues in hydrological research. Due to rainfall forecasting involves a rather complex nonlinear data pattern; there are lots of novel forecasting approaches to improve the forecasting accuracy. This paper proposes a Projection Pursuit Regression and Neural Networks (PPR-NNs) model for forecasting monthly rainfall in summer. First of all, we use the PPR technology to select input feature for NNs. Secondly, the Levenberg-Marquardt algorithm algorithm is used to train the NNs. Subsequently, example of rainfall values in August of Guangxi is used to illustrate the proposed PPR-NNs model. Empirical results indicate that the proposed method is better than the conventional neural network forecasting models which PPR-NNs model provides a promising alternative for forecasting rainfall application.
Projection Pursuit Regression Neural Networks Rainfall Forecasting
Fangqiong Luo Jiansheng Wu
Department of Mathematics and Computer Science Liuzhou Teachers College Liuzhou,Guangxi,China
国际会议
黄山
英文
488-491
2010-05-28（万方平台首次上网日期，不代表论文的发表时间）