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Risk evaluation of urban water distribution network pipes using neural network

Published: 06 November 2018 Publication History

Abstract

To identify the high-risk areas of urban water supply network, a neural network model is established based on the accident records collected by Beijing grid management. Impacts of buildings, roads around the accident site, and the time information of the accident were included in the model. In total 13 factors were used as input parameters, and the risk coefficient of the pipes was specified as the output. A risk amplification rate (RAR) was introduced to characterize the accident risk of the regional pipes. The results show that the model is able to effectively predict high risk regions.

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  • (2020)A Decision Tree Approach to the Risk Evaluation of Urban Water Distribution Network PipesSafety10.3390/safety60300366:3(36)Online publication date: 22-Jul-2020

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    cover image ACM Conferences
    Safety and Resilience'18: Proceedings of the 4th ACM SIGSPATIAL International Workshop on Safety and Resilience
    November 2018
    129 pages
    ISBN:9781450360449
    DOI:10.1145/3284103
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 06 November 2018

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    Author Tags

    1. Neural network
    2. risk assessment
    3. water supply network

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    Safety and Resilience'18 Paper Acceptance Rate 22 of 38 submissions, 58%;
    Overall Acceptance Rate 22 of 38 submissions, 58%

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    • (2020)A Decision Tree Approach to the Risk Evaluation of Urban Water Distribution Network PipesSafety10.3390/safety60300366:3(36)Online publication date: 22-Jul-2020

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