In this paper, a rainfall runoff pollution prediction method based on grey neural network algorithm is proposed in consideration of the current situation that the accuracy of research results related to rainfall runoff pollution prediction needs to be improved. Meanwhile, the characteristics of rainfall runoff pollution are analyzed from the perspectives of the main sources of rainfall runoff pollution, the types of rainfall runoff pollution, and the initial erosion. The neural network algorithm is optimized and trained according to the sample data to obtain the sample features; the sample data are predicted according to the extracted sample features, and the prediction model is generated by using the feature fusion technology for two groups of prediction results to generate the prediction model and realize the water drop prediction. The pollution concentration of runoff was obtained by the exponential function method. The experimental results show that the predicted values of discharge and pollution concentration are well fitted with the actual values, indicating that the proposed method has high accuracy and feasibility. Finally, from the viewpoint of non-engineering measures and engineering measures, the suggestions for treating runoff pollution and relevant supports for ecological environment protection are given.

中文翻译：

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基于特征融合的城市降雨径流污染预测模型研究

鉴于目前需要提高与降雨径流污染预测相关的研究结果的准确性，提出了一种基于灰色神经网络算法的降雨径流污染预测方法。同时，从降雨径流污染的主要来源，降雨径流污染的类型和初始侵蚀的角度分析了降雨径流污染的特征。根据样本数据对神经网络算法进行优化和训练以获得样本特征；根据提取的样本特征对样本数据进行预测，利用特征融合技术对两组预测结果进行预测，生成预测模型，实现水滴预测。用指数函数法计算了径流的污染浓度。实验结果表明，该方法的排放量和污染浓度的预测值与实际值吻合较好，表明该方法具有较高的准确性和可行性。最后，从非工程措施和工程措施的角度，提出了径流污染治理的建议和生态环境保护的相关支持。