Water supply network pollution source identification by random forest algorithm

Luka Grbčić; Ivana Lučin; Lado Kranjčević; Siniša Družeta

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Water supply network pollution source identification by random forest algorithm

机译：随机林算法供水网络污染源鉴定

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摘要

A novel approach for identifying the source of contamination in a water supply network based on the random forest classifying algorithm is presented in this paper. The proposed method is tested on two different water distribution benchmark networks with different sensor placements. For each considered network, a considerable amount of contamination scenarios with randomly selected contamination parameters were simulated and water quality time series of network sensors were obtained. Pollution scenarios were defined by randomly generated pollution source location, pollution starting time, duration of injection and the chemical intensity of the pollutant. Sensor layout's influence, demand uncertainty and imperfect sensor measurements were also investigated to verify the robustness of the method. The proposed approach shows high accuracy in localizing the potential sources of pollution, thus greatly reducing the complexity of the water supply network contamination detection problem.

机译：本文介绍了一种基于随机森林分类算法的供水网络中污染源的一种新方法。该方法在具有不同传感器放置的两种不同的水分配基准网络上进行测试。对于每个考虑的网络，模拟了具有随机选择的污染参数的相当大量的污染场景，并获得了网络传感器的水质时间序列。通过随机产生的污染源位置，污染开始时间，注射持续时间以及污染物的化学强度来定义污染情况。传感器布局的影响，还研究了需求不确定性和不完美的传感器测量，以验证该方法的鲁棒性。所提出的方法在本地化潜在的污染源方面表现出高精度，从而大大降低了供水网络污染检测问题的复杂性。

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来源
《Journal of Hydroinformatics》 |2020年第6期|1521-1535|共15页
作者
Luka Grbčić; Ivana Lučin; Lado Kranjčević; Siniša Družeta;
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作者单位

University of Rijeka|University of Rijeka;

University of Rijeka;

University of Rijeka|University of Rijeka;

University of Rijeka|University of Rijeka;

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收录信息美国《科学引文索引》(SCI);
原文格式 PDF
正文语种 eng
中图分类
关键词
machine learning; pipe network pollution; pollution source identification; random forest algorithm; water supply system contamination;

机译：机器学习;管道网络污染;污染源鉴定;随机森林算法;供水系统污染;

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4. Identification and spatial patterns of coastal water pollution sources based on GIS and chemometric approach [J] . ZHOU, Feng, GUO, 环境科学学报：英文版 . 2007,第007期
5. Pollution source localization in an urban water supply network based on dynamic water demand [J] . Yan Xuesong, Zhu Zhixin, Li Tian Environmental Science and Pollution Research . 2019,第18期

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6. Molecular identification of fecal pollution sources in water supplies by host-specific fecal DNA markers and Terminal Restriction Fragment Length Polymorphism profiles of 16S rRNA gene [J] . Ju-Yong Jeong, Kyung-Ik Gil, Kyong-Hee Lee, The journal of microbiology . 2008,第6期

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9. SOURCES OF POLLUTION IN THE DRAINAGE BASIN CONTRIBUTING TO THE EUTROPHICATION OF HOOVER RESERVOIR, A WATER-SUPPLY IMPOUNDMENT FOR THE CITY OF COLUMBUS, OHIO. [D] . BIRCH, THOMAS JOSEPH. 1969

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Water supply network pollution source identification by random forest algorithm

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