基于模糊神经网络的城市排水预测

海绵城市的建设具有重要意义,如何更好地预测城市排水量成为一个关键点。针对城市排水的波动性和非线性特征,提出一种基于模糊神经网络预测的城市水量预测算法。首先,采用灰色模型构建城市排水预测模型,并基于海绵城市的概念建立客观预测回归函数。其次,通过设置断点来构建滑动窗口,并实时分割灰色模型的数据,以获得数据的实时统计特征,通过构造序列误差预测和压缩比之间的函数,并使用误差预测序列来确定分割点,以提高检测过程的鲁棒性。最后,通过城市排水预测模拟实验,表明所提出的算法能够有效提高城市排水的预测精度和预测效率,更好地反映了整体趋势。

Urban drainage prediction based on fuzzy neural network

The construction of sponge city is of great significance. How to better predict urban drainage becomes a key point. Aiming at the volatility and nonlinear characteristics of urban drainage, a city water quantity prediction algorithm based on fuzzy neural network prediction is proposed. Firstly, the gray model is used to construct the urban drainage prediction model, and the objective prediction regression function is established based on the concept of sponge city. Secondly, the sliding window is constructed by setting breakpoints, and the data of the gray model is segmented in real time to obtain real-time statistical features of the data. We construct a function between the sequence error prediction and the compression ratio, and use the error prediction sequence to determine the segmentation point to improve the robustness of the detection process. Finally, through the urban drainage prediction simulation experiment, it is shown that the proposed algorithm can effectively improve the prediction accuracy and prediction efficiency of urban drainage, and better reflect the overall trend.