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The Application of Gray Model and BP Artificial Neural Network in Predicting Drought in the Liaoning Province

机译:灰色模型与BP人工神经网络在辽宁省预测干旱中的应用

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Precipitation prediction is the core of a regional drought prediction. Due to the great randomness and uncertainty in the precipitation process, this study combined the grey model and BP artificial neural network. The residual errors of precipitation were modified by the BP artificial neural network after the precipitation were modeled and predicted by the grey model, then the grey-BP neural network combination model was established for predicting the precipitation in the studied area. The results showed that the prediction accuracy of the combination model was the highest by integrating the advantages of the grey model and BP artificial neural network. The prediction error of the combination model was much lower than the grey model's and only slightly lower than the BP neural network model's.
机译:降水预测是区域干旱预测的核心。由于降水过程中的随机性和不确定性,这项研究结合了灰色模型和BP人工神经网络。通过灰色模型进行沉淀并预测沉淀后,通过BP人工神经网络改变沉淀的残余误差,建立了灰色BP神经网络组合模型,用于预测研究中的降水。结果表明,通过整合灰色模型和BP人工神经网络的优点,组合模型的预测精度最高。组合模型的预测误差远低于灰色模型,仅略低于BP神经网络模型。

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