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Source: YouTube - Keynote Risk and Resilience
Source: AIWW 2021 - Plenary keynote Risk and Resilience by Bart van den Hurk
Introduction to Risk and Resilience by Kees van der Lugt, as part of data-driven pathways to approach water-related disasters and challenges.
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AIWW2021 - IR15 - Introduction by Alex van der Helm (Waternet): Data-driven pathways to approach water-related disasters and challenges.
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In a pathogen contamination event, ensuring the health of the public should be the top priority in every emergency response mechanism. The immediate control of such incidents is of utmost importance since there is usually a short time to act. Research and innovation are slow to penetrate to protocols and tools related to waterborne pathogen contamination events, as is seen in the current COVID-19 pandemic that raises concerns and questions about the level of preparedness and the health risks of the public.
With the fast-growing rate of scientific publications, the contained information is often buried under the large volume of journal articles. In the field of environmental microbiology, although knowledge and research are abundant in the literature, scientists and experts cannot constantly stay informed on the latest developments. That is why the responsible authorities have uttered the need for a holistic approach to handle waterborne pathogen contamination events by getting immediate access to up-to-date, science-based information. For this purpose, the EU-funded PathoCERT project is launched with the aim of increasing the coordination capability of the responsible authorities. One of the sub-objectives of the project is to develop an Artificial Intelligence (AI) system that extracts information from scientific publications on pathogen characteristics. This automated approach will help them to quickly gain important information, thus enabling them to assess the health risk from a pathogen contamination event and identify potential control actions.
The objective of this research is twofold. Firstly, we want to determine whether it is feasible to extract both qualitative and quantitative information from scientific publications about a waterborne pathogen (Legionella) using Machine Learning (ML) and Text Mining (TM) techniques. Secondly, we want to assess the quality of the extracted information. Legionella was selected, considering that it is a well-known waterborne pathogen that is frequently associated with outbreaks. A Proof of Concept (POC) was utilized to determine whether an Information Extraction (IE) task (a principal subfield of TM) can extract Information Keywords (IK) from scientific publications such as “Incubation period”, “Source of exposure”, “Route of transmission”, “Symptoms”, “Clinical manifestation”, “Species”, and ” Environmental habitat “.
The POC suggested that the system effectively extracted the desired IK. The evaluation of the POC was made using the analytical metrics of precision, and recall which returned a score of 0.91, 0.80, and 0.85 respectively. The high overall scores indicated that the system captures and predicts the IK correctly. The comparison of the system’s performance with manual extraction of information on 10 new scientific publications substantiated this conclusion as similar results were observed, indicating that the quality of the extracted information is adequate.
Overall, the proposed system showed AI could reliably extract both qualitative and quantitative information keywords about Legionella from scientific literature. Our study paved the way for a better understanding of the processes, specifics, and boundary conditions of the desired information, and is considered a first step towards the extraction of information on waterborne pathogens that can help experts in decision-making in an emergency event.
Source: Paraskevopoulos, S. (KWR Water Research Institute, The Netherlands). 2021. Artificial Intelligence and emergencies: An automated approach to extract information from the literature to tackle pathogen contamination events. Data-driven pathways to approach water-related disasters and challenges, Risk & Resilience. AIWW 2021.
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The 21st century marks the beginning of the digital transition for the water sector. Many water utilities have begun to utilize sensors to detect and locate water quality hazardous events and pipe bursts throughout their drinking water networks. However, these early warning systems often require a substantial amount of historic data to train machine learning algorithms before detecting anomalies within their own systems. As a result of these data requirements, data collection may be needed to take place for more than a year before the model is operationally functioning. The availability of this data, then, presents a significant barrier to many water utilities who remain hesitant to implement an early warning system given the extensive up-front work required before yielding results.
This paper will present a methodology for filling the gaps of historic data requirements through the application of multiple hydraulic model simulations. These simulations develop an initial set of detection thresholds and calibration of localization model. Data from the hydraulic model simulations are then stored and used to train statistical and machine learning models. This allows the early warning system to be implemented as soon as the sensors are installed. Then, as real data is collected from the sensors, the model is recalibrated to improve anomaly detection and localization.
This work has also designed an open-use FIWARE (www.fiware.org) data model to link hydraulic modelling with real-time sensor observations, which further assists with the initial calibration of the early warning system, as well as its online implementations and data exchange with other applications. The Water Distribution Management Model provides a uniform process to store and run hydraulic models (EPANET input data format). Utilities wishing to implement this process can adopt the online Fiware data model to run and store the hydraulic model.
Overall, the methodology presented in this paper will assist other utilities as they develop their own early warning system by (1) accelerating the calibration process and (2) providing a data model that can house an EPANET model online and assist with hydraulic simulations. The methods and tools developed in this research will be crucial as more utilities develop their own early warning systems and continue to advance their digital water transition.
Source: Snider, B. (University of Exeter). 2021. Accelerating Early Warning System Implementation in Water Distribution Networks Through Hydraulic Simulations. Data-driven pathways to approach water-related disasters and challenges, Risk & Resilience. AIWW 2021.
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Source: YouTube - Accelerating Early Warning System Implementation
In January 2019, De Nederlandsche Bank (DNB) published a report on the ever bigger risks for financial institutions due to water scarcity (notwithstanding risks from floods). Dutch financial institutions have invested at least 97 billion euros in companies in areas of extremely high water scarcity, ranging from soft drink makers to mining groups. Water scarcity can therefore cost the financial sector a lot of money in the coming decades. DNB therefore believes that banks, insurers and pension funds should include these types of risks in their sustainability policy. The majority from the 25 parties investigated by DNB in the financial sector (together accounting for 80% of the invested assets worth at least 1,600 billion euro) do not yet have a well-developed policy for managing water scarcity risks.
In 2018, the first Water Benchmark for financial institutions was developed and carried out by the Water Footprint Network (WFN) in the Netherlands, which is a leading international knowledge partner. On the basis of the water footprint concept, three dimensions were distilled and used in the Water Benchmark, to describe the water sustainability of investment decisions. These dimensions are: efficient allocation, sustainable scale and fair distribution. Efficient allocation revolves around the efficient use of water in the production process, and investigates whether an investment does not unnecessarily waste or contaminate water; sustainable scale compares the total of all water users in the region with ecological ceiling values and analyzes the share of the investment in the total local water use and the possible scarcity that this creates; and the fair distribution criterion provides insight into how the investment usage relates to other users, and asks if the investment does not claim a disproportionate part of the limited available water. More than 50 criteria / questions describe these three sustainability dimensions in both direct operations and the supply chains of investments. Scores are awarded based on the study of publicly available sources and reports released by the financial institutions themselves. The scores describe the degree of sustainable use of water by the companies that are invested in by the rated investor.
The results showed that water sustainability is a blind spot to investors, resulting in disclosed policies being neither well-demarcated nor clearly formulated, especially regarding the supply chain of the activities invested in.
In 2021, WFN together with Water Footprint Implementation have reapplied the assessment framework to the same 20 Dutch large investors with the intent of uncovering trends in the financial sector’s consideration of physical, regulatory, or reputational risk posed by water. We are excited to show who rose and who fell in the rankings and we expect both the water and the financial sector are curious too.
Source: Dobrescu, I. (Water Footprint Implementation). 2021. Water Benchmark for Financial Institutions. Data-driven pathways to approach water-related disasters and challenges, Risk & Resilience. AIWW 2021.
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Source: YouTube - Water Benchmark for Financial Institutions
