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A novel approach to extreme rainfall prediction based on data mining

机译:基于数据挖掘的极端降雨预测的一种新型方法

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A novel extreme rainfall prediction model combined with data mining is proposed in this paper. Because of the special nature of hydrological data, our model uses the clustering method to group the next year's average extreme rainfall and then establish a hybrid extreme rainfall prediction model based on building neural networks for each group. Furthermore discriminant analysis is employed to classify the sample's coming year's average extreme rainfall and corresponding BP neural network is chosen to obtain the prediction. We also take the impact of discrimination error into account in our computation of the average extreme precipitation predictive value owing to the discrimination error. Experimental results validate our method and the prediction accuracy is satisfactory.
机译:本文提出了一种新的极端降雨预测模型与数据挖掘相结合。 由于水文数据的特殊性,我们的模型使用聚类方法对明年的平均极端降雨进行分组,然后基于每个群体建立神经网络的混合极端降雨预测模型。 此外,采用判别分析来分类样本的来年的平均极端降雨,并选择相应的BP神经网络以获得预测。 由于辨别误差,我们还考虑了歧视错误的影响。 实验结果验证了我们的方法,预测精度是令人满意的。

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