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Research and application of neural network algorithm based on Water supply and drainage pipeline network

Published: 03 July 2024 Publication History

Abstract

The explosion/leakage problem of the water supply and drainage pipeline network is the main hidden danger problem in the current domestic water supply and drainage system. GIS technology for equipment management and algorithm research in the water supply and drainage pipeline network to solve this problem. This article provides an evaluation method for the leakage problem of underground pipeline networks based on BP neural network algorithm and genetic algorithm, which is based on historical accident type data, time data, and regional feature data of accident points. The data is then processed and transformed into neural network model data, which is then trained and verified by the neural network model. The results have shown that this method can not only make predictions before water pipe leakage or even explosion accidents occur, but also provide accurate and effective guidance for daily pipeline inspections, and improve work efficiency. This solution can be applied to various pipeline networks and has strong application and economic value in avoiding water resource loss.

References

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ZHAO Jiabin. 2023. Study on the Changes of Residual Chlorinein Water Supply Pipeline Based on BP Neural Network. Journal of Jia mu si University (Natural Science Edition). 2023, Vol.41 No.5.
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Pan Ruijun. 2023.Research on leakage location of long-distance water supply network based on neural network algorithm. Water conservancy technical supervision. 2023 (08).
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Du Menghang. 2021.Research on Location of Burst in Water Supply Network Based on BP Neural Network. China:Tianjin University. Master Dissertation
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Wang Boyan. Dai Xiongqi. Lin Feng. Chen Lihai. 2020. Prediction of comprehensive risk rate and analysis of influencing factors for urban water supply network. The 15th Qingdao Water Conference in 2020.
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Guo Zhongyang. Wang Xiyuan. 2020. Principles, Methods, Technologies, and Applications of Data Mining. Beijing,China: Science Press, 2020.
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Igor Livshin. 2021. Java Artificial Neural Network Construction. China: China Machine Press, 2021.
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Song LiHeng. Wang Xiyuan. 2022. Python deep learning starts from scratch. China: Tsinghua University Press, 2022.
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Zhang Kun. 2022. Python Data Analysis and Data Mining Algorithms From Beginner to Machine Learning. China: Tsinghua University Press, 2022.
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Yang Yanying, Zheng Jianchun, Liu Shuangqing, Zhu Wei, Rui Jing, Liu Mengting. 2022. An evaluation method for the operational capacity of underground pipeline networks based on neural network models. Invention patent. Chinese Patent Number: CN202111366186.9, 2021-11-18, 2022-02-11.
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Peng xi. 2011. The Application of Spatial Data Mining in Explosive Tube Information. Southwest water & wasterwater, 2011, Vol.33 No.3.

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    GAIIS '24: Proceedings of the 2024 International Conference on Generative Artificial Intelligence and Information Security
    May 2024
    439 pages
    ISBN:9798400709562
    DOI:10.1145/3665348
    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 the author(s) 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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    Association for Computing Machinery

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    Published: 03 July 2024

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