Pipe network siltation risk prediction modeling method based on PNN neural network and SWMM technology

Abstract

The invention provides a pipe network siltation risk prediction modeling method based on a PNN neural network and an SWMM technology. The method comprises the following steps: A, collecting pipe network drainage parameters, and carrying out the preprocessing of the drainage parameters based on an SWMM model; b, constructing historical data including parameters influencing the pipe network sedimentation condition and reflecting the pipe network sedimentation condition based on the preprocessing result in the step A; and C, inputting historical data into the PNN neural network to obtain a siltation risk prediction model. The pipe network siltation risk prediction modeling method based on the PNN neural network and the SWMM technology has the advantages that the pipe network siltation risk prediction modeling method is based on the PNN neural network and the SWMM technology; through the combination of the PNN neural network and the SWMM technology, more accurate historical data are constructed for model training, the problems of singleness and hysteresis of an existing siltation judgment method are solved, a management and maintenance unit can conveniently make a detailed desilting maintenance scheme, and the pipe network maintenance efficiency is improved.

Description

technical field [0001] The invention relates to the technical field of calculation and prediction of drainage pipe network siltation risk, in particular to a pipe network siltation risk prediction modeling method based on PNN neural network and SWMM technology. Background technique [0002] Sewerage systems play an important role in keeping cities functioning. Due to various reasons such as planning, design, construction, operation and maintenance, etc., the drainage pipe network often has problems such as pipe siltation and blockage, resulting in low flow capacity of the pipes, which seriously affects the normal operation of the drainage pipes and induces rain. Problems and disasters such as stagnant water and urban waterlogging have brought great inconvenience to urban traffic and the normal life of citizens. [0003] However, at present, there is no systematic predictive analysis method for judging the siltation of the pipe network. Usually, CCTV (closed-circuit televisi...

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Problems solved by technology

[0004] The technical problem to be solved by the present invention is to provide a method for obtaining training data based on SWMM technology and using it to train a PNN neural network to obtain a prediction model capable of predicting the risk of pipe network silting, so as to overcome the hysteresis of silting monitoring in the prior art question

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