Open Access

ARTICLE

Urban Drainage Network Scheduling Strategy Based on Dynamic Regulation: Optimization Model and Theoretical Research

by Xiaoming Fei^*

College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China

* Corresponding Author: Xiaoming Fei. Email:

Intelligent Automation & Soft Computing 2023, 37(2), 1293-1309. https://doi.org/10.32604/iasc.2023.038607

Received 21 December 2022; Accepted 20 February 2023; Issue published 21 June 2023

Abstract

With the acceleration of urbanization in China, the discharge of domestic sewage and industrial wastewater is increasing, and accidents of sewage spilling out and polluting the environment occur from time to time. Problems such as imperfect facilities and backward control methods are common in the urban drainage network systems in China. Efficient drainage not only strengthens infrastructure such as rain and sewage diversion, pollution source monitoring, transportation, drainage and storage but also urgently needs technical means to monitor and optimize production and operation. Aiming at the optimal control of single-stage pumping stations and the coordinated control between two-stage pumping stations, this paper studies the modelling and optimal control of drainage network systems. Based on the Long Short Term Memory (LSTM) water level prediction model of the sewage pumping stations, and then based on the mechanism analysis of drainage pipe network, the factors that may cause the water level change of pumping station are obtained. Grey correlation analysis is carried out on these influencing factors, and the prediction model is established by taking the factors with a high correlation degree as input. The research results show that compared with the traditional prediction model, the LSTM model not only has higher prediction accuracy but also has better inflection point tracking ability.

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Keywords

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