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Risk Based Analysis for Contamination Event Selection and Optimal Sensor Placement for Intermittent Water Distribution Network Security

机译:基于风险的污染事件选择和间歇水分配网络安全传感器的最佳布置分析

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Water distribution networks are vulnerable to various contamination events that may be accidental or purposeful. Sensors are required for online monitoring of water quality to safeguard human health. Since sensors are costly, their numbers must be limited that makes sensor locations crucial in the water monitoring system. This paper aims at location of sensors in intermittent water distribution system which are more prone to accidental contamination due to contaminants ingress into the pipe lines because of low pressures during non supply hours. Considering deployment of limited number of sensors, the novelty of the paper is to propose a methodology for selection of contamination events with associated risk to be used in design of sensor network. Integrated risk assessment model is used to identify risk prone areas that may lead to possible contamination events. A Genetic Algorithm based methodology is suggested for optimal location of water quality sensors to maximize the detection likelihood of the contamination events within the acceptable time from the risk prone areas to improve network security. A comparison of sensor network design is made by considering contamination events occurring with: (i) equal probability at all the nodes; (ii) equal probability at risk prone nodes; and (iii) probability of occurrences based on quantified risk, to show that identification of risk prone areas and selection of contamination events results in reduction of computational work and more sensible placement of sensors.
机译:供水网络容易受到各种偶然或有目的的污染事件的影响。在线监测水质需要传感器,以保护人类健康。由于传感器价格昂贵,因此必须限制其数量,这使得传感器位置在水监控系统中至关重要。本文针对间歇水分配系统中的传感器的位置,由于在非供水时间内的低压,污染物进入管道时更容易造成意外污染。考虑到部署有限数量的传感器,本文的新颖之处在于提出一种用于选择污染事件及其相关风险的方法,以用于传感器网络的设计。综合风险评估模型用于识别可能导致污染事件的易发风险区域。提出了一种基于遗传算法的方法,用于水质传感器的最佳位置,以在容易发生风险的区域的可接受时间内,最大化污染事件的检测可能性,以提高网络安全性。通过考虑以下情况下发生的污染事件进行传感器网络设计的比较:(i)在所有节点上的概率相等; (ii)易受威胁节点的概率相等; (iii)基于量化风险的发生概率,以表明识别易发风险区域和选择污染事件会导致计算工作量减少和传感器的更合理放置。

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