基于RBF神经网络马尔可夫模型的降水量预测

Precipitation Predicting Based on Improved RBF Neural Network and Markov Model

降水量预测是制定抗旱防涝对策的重要依据,其预测方法是科学准确预测降水的重要手段。为提高降水量预测的精确度,应用RBF神经网络与马尔可夫相结合,建立R-M降水量预测模型,介绍了它的基本原理及算法,并给出了该模型建立的具体过程,最后将该模型应用于降水量预测工作中,实例验证结果令人满意。

Precipitation predicting is an important base of making countermeasures for drought and flood.The selection of forecasting method is also important for scientific and accurate precipitation predicting.The model of RBF neural network and Markov model is brought out in this paper brings out,and its basic principle as well as algorithm is introduced in detail.The model building-up process is also introduced.Finally,it is applied to predict monthly precipitation,and the result shows that it is practicable to predict.