论文信息 - Rainfall Forecasting Using Projection Pursuit Regression and Neural Networks

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.

Fangqiong Luo | Jiansheng Wu

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