Bayesian Network and System Thinking Modelling to Manage Water-Related Health Risks from Extreme Events Author Bertone, E, Sahin, O, Richards, R, Roiko, RA Published 2015 Conference Title 2015 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND ENGINEERING MANAGEMENT (IEEM) Version Accepted Manuscript (AM) DOI https://doi.org/10.1109/IEEM.2015.7385852 Copyright Statement © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Downloaded from http://hdl.handle.net/10072/123524 Griffith Research Online https://research-repository.griffith.edu.au Citation: Bertone, E.; Sahin, O.; Richards, R.; Roiko, A. (2015). Bayesian Network and System Thinking modelling to manage water-related health risks from extreme events. IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Singapore, 6-9 December 2015 Bayesian Network and System Thinking Modelling to Manage Water-Related Health Risks from Extreme Events E. Bertone1, O. Sahin1, R. Richards2, R. A. Roiko3 1 Griffith School of Engineering, Griffith University, Queensland, Australia 2 School of Agriculture and Food Sciences, University of Queensland, Brisbane, Australia 3 Griffith School of Medicine, Griffith University, Queensland, Australia Email: [email protected] Abstract - A combination of Bayesian Network (BN), system leading to the formation of carcinogen System Dynamics (SD) and participatory modelling to trihalomethanes (THM’s), one of the over 600 develop a risk assessment tool for managing water-related disinfection by-products currently reported in drinking health risks associated with extreme events has been water [2]. developed. The risk assessment tool is applied to the Water turbidity is another very important parameter that is Prospect water filtration plant system, main source of monitored by water authorities. Turbidity refers to how potable water for the Sydney metropolitan region. Conceptual models were developed by the stakeholders clear the water is. It is a result of suspended particles that around the key indicator parameters of turbidity, water can provide food and shelter for pathogens, and if not colour and cryptosporidium. These three conceptual models effectively removed, can promote regrowth of pathogens were and used for developing separate BN and SD models. in the distribution system, leading to waterborne disease Here we present the development of a BN designed to outbreaks [3;4], which can cause e.g. cramps, diarrhea, understand the risk of extreme events on the ability to headache and nausea [5]. provide drinking water of a desired quality. The model has The third key parameter of this project, cryptosporidium, undergone development and preliminary parameterization is an intestinal protozoan pathogen that infects humans, via two participatory workshops. However, its development domestic animals and wildlife worldwide. It can be found is an ongoing process with the next stage involving supplementing the ‘expert opinion’ used to parameterize the in the faeces of infected humans and animals [6], which model so far with ‘hard’ data. can enter surface waters directly or through effluents and runoff from fields that are polluted by sewage sludge or Keywords - Bayesian Networks, Extreme Events, Water [7;8] resulting in pollution of receiving waters. Quality Importantly, the cryptosporidium (oo)cysts have the I. INTRODUCTION capacity to remain infective for months in environmental waters and are highly resistant to chlorinated [9]. Recent history in Australia has been characterized by Therefore, waterborne contamination is a growing a range of extreme weather events (e.g. droughts, concern for water suppliers, causing widespread outbreaks Brisbane floods, cyclone Yasi, Victorian bushfires). These of these diseases [10]. For example, a contamination of events have impacted on the ability of water utilities to cryptosporidium, along with Giardia, occurred in the provide drinking water of a required standard to water supply system of Greater Metropolitan Sydney consumers. At issue, are the short- and long-term impacts during the 1998 Sydney water crisis[11]. of extreme events on the water quality at both the pre- and For the Sydney area, which is the location of this study, it post-treatment (including distribution and end-point). is predicted that due to climate change, the number of Extreme events are projected to change in magnitude and days of extreme rainfall (> 40mm/day), as well as the frequency over the next century [1], further exacerbating number of very hot days (>37°C) and continued dry spells the pressures on water quality management. However, (>15days) will increase considerably [12] bringing there are large uncertainties associated with the timing detrimental effects for the water quality of reservoirs. and nature of specific future events and this uncertainty is Water quality management in this context requires a a major contributor to the challenge of water multi-disciplinary approach, both holistic and management. The key health-related water quality probabilistic, to develop appropriate management parameters that mostly concern the water utility involved strategies. Strong support and active participation from in this research project (i.e. Water NSW) in case of the water industry itself, whose experiences with past extreme events are: water colour, turbidity, and occurrences of extreme events are invaluable sources of cryptosporidium. qualitative and quantitative information, is also required. Water colour is a key parameter in drinking water II. METHODOLOGY reservoirs as it can affect physical and biological properties of the whole lake, as well as creating The Research Team is developing an extreme event discolouration of the raw water redirected to the water risk assessment tool using Bayesian Network (BN) treatment plant (WTP). If discoloured water leaves the modelling and System Dynamics (SD) modelling. These WTP, the dissolved organic matter present in this water modelling frameworks are proposed because of the can react with chlorine when it enters the potable water following combined attributes: • They provide a modelling framework that allows extreme events is an element to be factored into this prediction of an outcome (e.g. decline in water quality) analysis. even when the determining conditions (e.g. an extreme As a first step of the conceptual model development, the event) are both variable and uncertain. main parameters directly affecting turbidity, water colour • They are able to integrate data from different sources or cryptosporidium levels were identified as being: (e.g. model output, monitoring and expert opinion) and of • Avoidance capacity: this is linked to the presence of, different types (environmental, social and economic) into for example, intake towers with multiple gates at the a single model. reservoir, which allow the selection of the optimal (with • SD is able to analyse the behaviour of complex regards to water quality) intake depth. These structures systems (e.g. water quality management) and their reduce the risk of delivering raw water with very poor interacting components with many feedbacks and water quality features to the water treatment plant (e.g. changing over time. after an extreme rainfall event). However, its usefulness is • BN provides an ideal representation for combining limited during lake circulation periods (e.g. winter prior knowledge with data, and it is particularly helpful turnovers) as the water quality is uniform throughout the when dealing with uncertainty [13]. water column. The modelling process comprised the following core • Spill: if the dam spills (due to the storage level steps: exceeding the full capacity), then the water quality is • A first expert workshop was held in order to define expected to deteriorate as the avoidance capacity is the case-study sites, the key water quality parameters to reduced due to the water moving from the bottom to the be modelled and related levels of service, and to populate top of the dam wall (assuming the inflow coming as an the preliminary conceptual models. underflow); the main factors affecting a possible spill is • The conceptual model was converted into a BN by the storage level and inflow. the Research Team. In order to fill the Conditional • Use of alternative reservoirs: the presence of other Probability Tables (CTPs) attached to each node of the reservoir(s) that can be used to deliver raw water to the BN, a second expert workshop, with water utility experts Prospect Water Treatment Plant. This allows for drawing from different fields, was organized. raw water from other sources (than the default reservoir • The BN architecture and findings, along with collected where water is usually drawn from) if the water quality in historical data, will be used to develop the SD model. the default reservoir is ‘poor’. Raw water reaching the III. CONCEPTUAL MODEL DEVELOPMENT Prospect Water Treatment Plant is typically drawn from Warragamba Reservoir, but Prospect Reservoir and the The following section describes the outcomes of the first Upper Canal supply route (which includes Cataract, expert workshop held in Sydney (Australia) in 2015. The Cordeaux, Nepean and Avon