Application of Artificial Neural Networks to Forecasting Ice Conditions of the Yellow River in the Inner Mongolia Reach

Abstract

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 such as the Yellow River, while the artificial neural networks (ANNs) have an obvious advantage over other traditional methods for forecasting ice conditions. An ANN model based on feed-forward back-propagation and improved by the Levenberg-Marquardt algorithm is applied to forecast the ice conditions 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 multiple linear regression model and the GM (0,1) model.