A Dynamic Forecasting Method for Drainage Flow of Urban Rainwater System Outlet

Abstract

The invention discloses a method for dynamically predicting the drainage flow of an urban rainwater system drainage outlet. In step (1), the rainstorm and flood management model is used to simulate the rainfall-runoff, and the drainage flow process lines of multiple sets of drainage pipe network outlets are used as training samples. Step (2), set up RBF neural network and train, carry out the optimization of network hidden layer node and center width Spread in the training process; Step (3), set up NARX neural network and train; Step (4), will finish training The NARX neural network and the RBF neural network are coupled to obtain the coupling network, and then predict, calculate the mean square error of the coupling network and the sample, return the flow value with the smallest mean square error as the optimized coupling point, and randomly select the rainfall data to input the coupling network. Obtain the predicted drainage flow hydrograph. The invention organically combines the advantages and characteristics of different neural networks, the prediction result is in good agreement with SWMM simulation, the mean square error of the curve is 0.000458, and has good prediction accuracy.

Description

technical field [0001] The invention relates to the technical field of urban rainwater resources management and drainage, in particular to a dynamic prediction method of drainage flow based on coupled radial basis neural network, nonlinear autoregressive model and numerical simulation. Background technique [0002] Storm Flood Management Model (SWMM) is a dynamic precipitation-runoff simulation model, mainly including runoff module, confluence module and water quality module, etc. It is mostly used to simulate a single precipitation event or long-term water quantity and water quality simulation in a city. The model can track and simulate the water quality and quantity of runoff produced by each sub-basin at any time at different time steps, as well as the flow, water depth and water quality of water in each pipeline and channel. SWMM model is widely used in urban drainage simulation. [0003] The radial basis function (RBF) neural network belongs to the type of forward neur...

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Problems solved by technology

[0006] In order to solve the above-mentioned problem in the prior art of "only using the method of neural network" that "it is difficult to realize the high-precision forecasting requirement of the dynamic drainage process", the present invention proposes a method to measure the drainage flow rate of the drainage outlet of the urban rainwater system. The dynamic prediction method realizes the outfall flow prediction of the non-linear dynamic drainage system based on the NARX-RBF coupling neural network

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