神经网络综合模型预测龙江河流域汛期旱涝

应用龙江河流域金城江站水文资料、500hPa月平均环流指数、海温、太阳黑子和单站气象要素等资料,应用差值资料通过方差周期、多元线性回归和逐步回归分析得到的预测值,再经过神经网络综合模型进行分析,最后进行预测试验。结果表明,神经网络综合模型在龙江河流域旱涝天气预测中效果显著,可应用于业务预测。

Application the Integrated Model of Neural Network in Drought and Flood Predicting for the Longjiang River Basin

According to the annual warning water levels from the Jinchengjiang Guaging Station on the Longjiang River, with 500 hPa circulation index, circulation characteristic amount, local climate and etc., analysis can be made with variance cycle, multiple linear regression, stepwise regression procedure. Then, the predicted values can be taken while error values be received to make analysis and test through the integrated model of neural network. The results show that the model has achieved a remarkable effect in weather forecasting, which can be used for drought and flood predicting.