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Flood resilience is a critical concept in flood risk management (FRM). Meanwhile, flood resilience measurement has become vital for making the business case for investment in FRM. However, information is sparse on measuring the level of resilience of flood-prone communities in Nigeria. Therefore, this study aims to develop a fuzzy logic-based model for measuring the resilience of flood-prone communities towards achieving the United Nations Sustainable Development Goals (SDGs) 11 and 13.

This study describes the development of a fuzzy logic-based flood resilience measuring model, drawing on a synthesis of fuzzy logic literature and extant flood resilience. A generalisation of the flood system for a typical Nigerian community was made. It was followed by an identification and characterisation of the variables and parameters of the system based on SDGs 11 and 13. The generated data was transformed into a fuzzy inference system (FIS) using three input community flood resilience dimensions: natural, socio-technical and socio-economic factors (SEF). The model was then validated with primary data obtained from selected flood-prone communities in Ibadan, Southwest Nigeria. Expert opinions were used in rating the input dimensions for the selected communities.

In spite of various inputs from experts in the same study area (Apete, Ibadan, Nigeria), the resulting FIS generated consistent resilience indices for various natural, socio-technical and SEF. This approach can strengthen flood resilience measurement at the community level.

Although previous attempts have been made to measure flood resilience at the individual property level (Oladokun et al., 2017; Adebimpe et al., 2020), this research focuses on measuring flood resilience at the community level by adapting the fuzzy logic approach. The fuzzy logic-based model can be a tool for flood resilience measurement at the community level. It can also be developed further for regional and national level applications.

Recent changes in climate, rainfall patterns, snow melt and rising sea levels coupled with an increase in urban development have increased the threat of flooding. To curb these threats and mitigate these damages, property-level approaches to improving resilience are now being encouraged as part of an integrated approach to flood risk management. This raises questions such as, what are the flood resilient attributes within individual properties, what is their importance and how can these be quantified. This research sought to develop a quantitative approach for the measurement of property-level flood resilience.

A synthesis of literature was undertaken to establish the main resilient attributes and their relevant sub-attributes. This process led to the development of a new method, named the Composite Flood Resilient Index (CFRI) to weight the attributes and sub-attributes of flood resilience based on their importance. The approach adopts the use of the fuzzy-analytic hierarchy process (F-AHP) approach to quantify flood resilience.

The implications of the proposed methodology in determining the flood resilience of individual property, including the potential use in retrofitting activities, and the benefits to a range of stakeholders are considered.

The methodology offers the potential to support the measurement of flood resilience of individual properties, allowing the identification and prioritisation of specific interventions to improve the resilience of a property.

Whereas previous attempts to quantify flood resilience have adopted qualitative approaches with some level of subjectivity, this proposed methodology represents an important advancement in developing a scientific and quantitative approach.