Assessing water resources systems' dynamic resilience under hazardous events via a genetic fuzzy rule-based system. Issue 2 (2nd March 2023)

• Record Type: Journal Article
• Authors: Simic, Visnja; Stojkovic, Milan; Milivojevic, Nikola; Bacanin, Nikola
• Is Part Of: Journal of hydroinformatics. Volume 25:Issue 2(2023)
• Page Start: 318
• Page End: 331
• Publication Date: 2023-03-02
• Subjects: fuzzy rule-based system -- genetic algorithm -- water system resilience; Hydrology -- Data processing -- Periodicals; Geographic information systems -- Periodicals; Geographic information systems; Hydrology -- Data processing; Electronic journals; Periodicals; 551.480285
• DOI: 10.2166/hydro.2023.101
• Languages: English
• ISSNs: 1464-7141

Abstract

Abstract: In this paper, the use of a novel genetic fuzzy rule-based system (FRBS) is proposed for assessing the resilience of a water resources system to hazards. The proposed software framework generates a set of highly interpretable rules that transparently represent the causal relationships of hazardous events, their timings, and intensities that can lead to the system's failure. This is achieved automatically through an evolutionary learning procedure that is applied to the data acquired from system dynamics (SD) and hazard simulations. The proposed framework for generating an explainable predictive model of water resources system resilience is applied to the Pirot water resources system in the Republic of Serbia. The results indicate that our approach extracted high-level knowledge from the large datasets derived from multi-model simulations. The rule-based knowledge structure facilitates its common-sense interpretation. The presented approach is suitable for identifying scenario components that lead to increased system vulnerability, which are very hard to detect from massive raw data. The fuzzy model also proves to be a satisfying fuzzy classifier, exhibiting precisions of 0.97 and 0.96 in the prediction of low resilience and high rapidity, respectively. HIGHLIGHTS: A FRBS was proposed for assessing the resilience of a water resources system to hazardous events and system element failures. An evolutionary learning procedure based on a genetic algorithm (GA) was used to generate a set of linguistic rules from data. Fuzzy rules provide a means for transparent reasoning about a system's resilience using natural language. Graphical Abstract