优化水资源利用战略和应对流行病变化和污染事件的社会技术框架

L. Kadinski, Brent Vizanko, E. Berglund, A. Ostfeld
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引用次数: 0

摘要

配水系统是向消费者提供高质量饮用水的关键基础设施。供水系统(WDS)中的污染事件是可能导致人口痛苦的紧急情况,需要负责任的公用事业经理快速响应。虽然在水处理设施定期监测水质参数,但监测水务系统本身的水质仍然是一项挑战。已经开发了各种模型来探索污染事件中相关利益相关者的反应和相互作用,包括基于代理的建模。此外,最近的研究表明,在2019冠状病毒病大流行期间,水需求发生了重大变化,这些变化可能影响供水基础设施的运营和管理。在本研究中,开发了一个基于主体的建模框架,在考虑大流行需求情景的情况下,探索污染事件中水消费者和公用事业管理者的社会动态和反应。此外,对污染事件的创新响应和恢复方法进行了探索,以便在水质恶化后恢复水网。利用图论算法将移动传感器设备放置在特定网段进行水质监测,并根据危害状态对配网系统进行聚类。贝叶斯信念网络(BBN)是根据COVID-19大流行期间收集的有关风险认知和社会距离行为的调查数据开发的。基于主体的模型(ABM)是根据BBN的输出和2019冠状病毒病大流行期间收集的用水数据开发的。ABM与水利基础设施的水力模拟相结合,以评估水力性能的变化。该模型可用于探索大流行对供水系统管理、设计和运营的长期和短期影响;制定和优化如何应对因大流行而导致的周围供水系统变化的策略;并调查弹性供水设施如何应对额外的灾难性事件,例如在全球或当地大流行相关的停机期间供水系统的污染。©第3期。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Socio-Technological Framework for Optimizing Water Utility Strategies and Resilience to Pandemic Changes and Contamination Events
Water distribution systems are critical infrastructure that deliver high quality drinking water to its consumers. Contamination events in water distribution systems (WDS) are emergencies that can cause distress in the population and require quick response from the responsible utility manager. While regular water quality parameters are monitored at water treatment facilities, it is still a challenge to monitor water quality in the WDS itself. Various models have been developed to explore the reactions and interactions of relevant stakeholders during a contamination event including agent-based modelling. Furthermore, recent research has shown that water demands have significantly changed during the COVID-19 pandemic, and these changes can affect the operation and management of water infrastructure. In this study, an agent-based modelling framework is developed to explore social dynamics and reactions of water consumers and a utility manager during a contamination event, while considering a pandemic demand scenario. Furthermore, innovative response and recovery methods to a contamination event are explored for rehabilitating the water network after a water quality deterioration. Graph theory algorithms are used to place mobile sensor equipment for surveying the water quality in specific network parts, and the distribution system is clustered by the status of endangerment. The Bayesian Belief Network (BBN) was developed using survey data around risk perceptions and social distancing behaviour that were collected during the COVID-19 pandemic. The agent-based model (ABM) was developed using output from the BBN and water use data that were collected during the COVID-19 pandemic. The ABM is coupled with hydraulic simulation of the water infrastructure to evaluate changes in hydraulic performance. The model can be used to explore long and short-term consequences of the pandemic on water distribution systems' management, design, and operations;develop and optimize strategies of how to deal with changes in around water distribution systems due to the pandemic;and investigate how resilient water utilities can cope with additional catastrophic events such as a contamination of a water system during a global or local pandemic related shutdown. © ASCE.
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