All data and codes used in an application of Fuzzy Inference System optimized by a genetic algorithm in R programming language, developed during a master's program in sanitary and environmental engineering. The research yielded an article published in the scientific journal Environmental Challanges.
- Development of a reservoir hedging rule using fuzzy rule-based inference.
- Hedging rules are crucial to mitigate continuous and severe water shortages.
- Fuzzified hedging rule (FHR) provided the highest efficiency for priority demands.
- FHR promoted the shortest time in dead volume.
This study quantitatively evaluated different operating rules that were optimized with a genetic algorithm using a water budget simulation model. The study area was a reservoir located in the Brazilian semiarid region, which has great hydrological variability and severe droughts. In this context, the performances of standard operating policy (SOP), simple hedging rule (HR), and fuzzified hedging rule (FHR) were assessed. The objective was to maximize the volumetric reliability of meeting water demands during the dry season, a period with high water demand. Indicators such as volumetric reliability, resilience, vulnerability, and dead storage timeline were analyzed. The results showed that, in decreasing order, FHR, HR, and SOP varied in their effective mitigation of water scarcity during the dry season. FHR provided the greatest volumetric reliability for priority demands in both the dry season and the wet season, a significant reduction in vulnerability, and shorter durations of dead storage. However, HR and FHR induced more frequent supply rationing than SOP. The results confirm that predicting water rationing measures is crucial to mitigating continuous and severe water scarcity.