基于朴素贝叶斯算法的流域降水预测方法

A precipitation forecasting method for a river basin based on naive Bayes algorithm

为了在降水成因尚不明确的情况下有效利用相关历史资料提高降水预报水平,提出了基于朴素贝叶斯算法的流域降水预测方法。以东江流域为例,通过构造不同降水数据特征集预测流域内降水情况,并与传统时间序列方法和BP神经网络方法进行预测准确率对比验证,结果表明,基于朴素贝叶斯算法的降水预测方法取得了比传统时间序列方法和BP神经网络方法更好的降水预测效果。

In order to effectively use available historical observation data for precipitation forecasting in the case of an uncertain cause of precipitation, a precipitation forecasting method was developed based on the naive Bayes algorithm. Using the Dongjiang Basin as an example, a rich set of features was constructed based on the basin’ s precipitation data and meteorological knowledge. The forecasting accuracy of the proposed method was compared with those of the traditional time series method and the BP neural network method. The result shows that the proposed method outperformed both the traditional time series method and the BP neural network method.