Research on Urban Rainfall Runoff Pollution Prediction Model Based on Feature Fusion

Abstract EN

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.