Wavelet Neural Networks Model Used for Runoff Forecast Based on Fuzzy C-means Clustering

摘要

Considering various seasons differs greatly in runoff distribution, a new runoff forecasting method based on fuzzy clustering analysis on forecasting factor set is presented in this paper. Firstly, the historical runoff data are classified as four categories by fuzzy C-means clustering. Then partial forecasting models between the factor set and measured data are respectively established by using wavelet neural network model. A network model categorized recognizer is adopted, which can automatically search a compatible partial forecasting model. Comparison between simple wavelet neural model and integrated forecasting model proposed in this paper is made by illustration. The results demonstrate that the proposed integrated model is of higher forecasting accuracy than the simple one.