Volume 15, Issue 1 (4-2022)
ijhe 2022, 15(1): 17-36 |
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Mehrdadi N, Vafaei Mehr D, Nabi Bidhendi G, Hoveidi H. Self-organizing map neural network application in detecting the extent of intentional pollution in the urban water distribution network. ijhe 2022; 15 (1) :17-36
URL: http://ijhe.tums.ac.ir/article-1-6589-en.html
URL: http://ijhe.tums.ac.ir/article-1-6589-en.html
1- Department of Environmental Engineering, School of Environment, College of Engineering, University of Tehran, Tehran, Iran , mehrdadi@ut.ac.ir
2- Department of Environmental Engineering, Aras International Campus, University of Tehran, Jolfa, Iran
3- Department of Environmental Engineering, School of Environment, College of Engineering, University of Tehran, Tehran, Iran
4- Department of Environmental Planning, Management and Education, School of Environment, College of Engineering, University of Tehran, Tehran, Iran
2- Department of Environmental Engineering, Aras International Campus, University of Tehran, Jolfa, Iran
3- Department of Environmental Engineering, School of Environment, College of Engineering, University of Tehran, Tehran, Iran
4- Department of Environmental Planning, Management and Education, School of Environment, College of Engineering, University of Tehran, Tehran, Iran
Abstract: (1231 Views)
Background and Objective: Water distribution networks are prone to terrorist attacks by injecting toxic substances, due to their vastness and availability. The main objective of this paper was detecting the extent of intentional pollution in the urban water distribution network by self-organizing map (SOM).
Materials and Methods: The existing hydraulic condition of the water distribution network covered by reservoir No. 4 in Tehran was modeled as a pilot. Possible injection scenarios of contamination in different parts of the water distribution network were performed using qualitative analysis of the water distribution network, using the EPANET analyzer engine and coding in R software environment. Artificial neural network of SOM was used to find the contamination range for the injection of arsenic at different times and places in the distribution network.
Results: The concentration of contamination at a certain point decreased over time and a high correlation was observed between time and concentration. The extent of contamination depended on the consumption of subscribers and consequently, the time of contaminat injection. The results of the artificial neural network model showed that the method developed in this research was 91% accurate and was able to determine the extent of contamination in the water distribution network at high speed.
Conclusion: SOM can be used as a complement to the water quality monitoring and pollution detection system in the urban water distribution network to determine the extent of pollution when detecting potential pollution in the shortest possible time, and as an alternative to quantitative-qualitative modeling of the water network.
Materials and Methods: The existing hydraulic condition of the water distribution network covered by reservoir No. 4 in Tehran was modeled as a pilot. Possible injection scenarios of contamination in different parts of the water distribution network were performed using qualitative analysis of the water distribution network, using the EPANET analyzer engine and coding in R software environment. Artificial neural network of SOM was used to find the contamination range for the injection of arsenic at different times and places in the distribution network.
Results: The concentration of contamination at a certain point decreased over time and a high correlation was observed between time and concentration. The extent of contamination depended on the consumption of subscribers and consequently, the time of contaminat injection. The results of the artificial neural network model showed that the method developed in this research was 91% accurate and was able to determine the extent of contamination in the water distribution network at high speed.
Conclusion: SOM can be used as a complement to the water quality monitoring and pollution detection system in the urban water distribution network to determine the extent of pollution when detecting potential pollution in the shortest possible time, and as an alternative to quantitative-qualitative modeling of the water network.
Keywords: Water distribution network, Hydraulic analysis, Artificial intelligence, Self-organizing map
Type of Study: Research |
Subject:
WATER
Received: 2021/10/23 | Accepted: 2022/04/17 | Published: 2022/06/18
Received: 2021/10/23 | Accepted: 2022/04/17 | Published: 2022/06/18
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