RIVER ICE CONDITIONS FORECAST BY ARTIFICIAL NEURAL NETWORKS

摘要：

Ice condition forecasts are very important for preventing ice disasters. Because of the complexity of ice conditions, traditional methods could hardly give accurate prediction in the ice condition forecast, especially for the meandering rivers as the Yellow River, while the artificial neural networks (ANNs) have obvious advantage over other traditional methods for forecasting ice condition. An ANN model based on feed-forward back-propagation (FFBP) and improved by Levenberg-Marquardt algorithm is applied to forecast the ice condition. The study is applied to forecasting ice condition of the Yellow River in the Inner Mongolia Region. The forecast results in the winter of 2004-2005 are in good agreement with the measured ones. Simulation also shows that the ANN model is superior to the MLR model and GM (0,1) model.

关键词： neural networks river ice forecasting levenberg-marquardt algorithm

作者: Tao Wang Kailin Yang Yongxin Guo

作者单位: China Insti.of Water Res.and Hydropower Res., Beijing 100038, China

会议类型: 国际会议

会议名称: 第16届亚太地区国际水利学大会暨第3届水工水力学国际研讨会(16th IAHR-APD Congress and 3rd Symoposium of IAHR-ISHS)

会议地点: 南京

会议语种:英文

页码: 1918-1923

在线出版日期: 2008-10-20（万方平台首次上网日期，不代表论文的发表时间）