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The Study on Urban Water Supply Network Simulation and Monitoring Model

Author: WangYang
Tutor: ZuoZhiHong
School: Agricultural University of Hebei
Course: Agricultural Soil and Water Engineering
Keywords: Water supply network HPSO Model The method of least squares BP neural network Support Vector Machine
CLC: TU991.33
Type: Master's thesis
Year: 2011
Downloads: 43
Quote: 0
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Abstract


With the improvement of the social productive forces , the population continues to increase , as well as the continuous improvement of people's living standards , the speed and scale of urbanization is also growing fast , so that the urban water consumption is constantly increasing . Urban water supply network is an important part of the urban infrastructure . Security of water supply pipe network running a direct impact on the security of urban water supply , also affect the city 's social and economic development . Therefore , to establish and improve the water supply pipe network security management system , to achieve real-time monitoring and simulation of urban water supply networks , is to improve urban water supply management of modern enterprises , reduce the cost of the water supply , the only way to improve the economic efficiency . Unified monitoring of the water supply network system , rational distribution , rationalization and management , to achieve the purpose of energy conservation and to ensure a safe water supply , improve the economic and social benefits of the water supply system has an important significance . The paper is divided into two parts: the friction coefficient C value of the inversion of the water supply network , and to be verified by experiment ; regression model of the three simulation methods in the actual pipe network , and draw based on support vector machine The algorithm is the most accurate . 1 introduces the basic principles of the hybrid particle swarm optimization (HPSO) algorithm and the realization given based on the the HPSO algorithm of friction coefficient inversion method . 2 based on an actual 2.5m × 2.5m micro simplify pipe network , the inversion of the friction coefficient of node values ??calculated to verify . 3 macro regression model of a pipe network simulation , first to each node of the pipe network traffic changes over time based data . Were used based on the method of least squares , BP neural network , support vector machine simulated on the basis of the actual pipe network data . Comparisons : After analysis , support vector machine - based simulation method can better simulate the running state of the pipe network .

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CLC: > Industrial Technology > Building Science > Municipal Engineering > Water supply project ( on the Water Works ) > Water distribution > Water distribution network
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