A flood Impact-Based forecasting system by fuzzy inference techniques

Abstract

The increasing frequency of severe floods worldwide highlights the need for effective communication and dissemination of information about potential floods and their possible consequences. Modern technology, such as social media and early warning systems, can play a crucial role in informing the public about impending floods and their potential impact. Impact-Based Forecasting (IBF), conveying meteorological and hydrological data, has emerged as a promising approach to inform vulnerable communities about the potential impact of flood disasters and to mitigate associated risks for resilience enhancement and losses minimization. This study proposes a flood impact-based forecasting system (FIBF) that uses flexible fuzzy inference techniques to construct a relation matrix of likelihoods and impacts and estimate the level of risk and impact for inducing storms. We construct a three-layer fuzzy inference model to determine the flood impact based on the combined effects of hazard, exposure, and vulnerability and then establish a FIBF warning product (up to 36 h ahead) to timely address flood information for inundation-prone regions of the Kemaman River basin in Malaysia. The FIBF can be displayed using Google Maps, providing a spatial visualization of the flooding situation to help decision-makers better understand the extent and severity of the flooding, make informed decisions on emergency response and resource allocation, and take proactive measures to reduce the negative impacts of flooding. Thus, the developed FIBF can be especially helpful in mitigating the socioeconomic costs of flood hazards.