# Hierarchical Fuzzy MCDA Multi-Risk Model for Detecting Critical Urban Areas in Climate Scenarios
###### Abstract  
One of the issues of greatest interest in urban planning today concerns the evaluation of the most vulnerable urban areas in the presence of different types of climate hazards. In this research, a hierarchical fuzzy MCDA model is implemented on a GIS-based platform aimed at detecting the urban areas most at risk in the presence of heatwave and pluvial flooding scenarios. The proposed model aims to detect the urban areas most vulnerable to both the two climatic phenomena and the two types of hazards as independent events; it partitions the physical component of an urban settlement into two subsystems: buildings and open spaces, and it determines the criticality of a subzone of the urban area of study by evaluating the vulnerabilities of the two subsystems to the two phenomena. The use of a hierarchical fuzzy MCDA model facilitates the modeling of the two subsystems and the assessment of their vulnerability to the two phenomena, and it provides a computationally fast tool for detecting critical urban areas. The model was tested on a study area made up of the districts of the central-eastern area of the city of Naples (Italy); it was divided into subzones made up of individual census areas. The most critical areas are represented by the subzones with criticality values higher than a specific threshold. fuzzy hierarchical model; GIS-based MCDA; climate risk scenarios; heatwave; pluvial flooding; urban subsystems; criticality 23 February 20242 April  
In general, two types of hazards that are not related to each other are considered independent hazards, so that, although they occur in the same place and in the same period, they can be treated separately in the assessment of the risks that can be produced by the exposed elements. So, in multi-risk assessment, when referring to independent multi-hazards, the risk analysis can be carried out by treating the two phenomena separately. In these cases, the two risk models are used separately, and the damage caused is assessed by adding the expected risks to the exposed component due to the occurrence of each individual hazard. In order to sufficiently evaluate the aggregate effect of these vulnerabilities, modeling the urban system with all the characteristics that can influence the assessment of vulnerability to each phenomenon is one of the key critical points of approaches for detecting the criticality of urban settlements in the presence of different kinds of hazards. The use of a large number of characteristics, however, represents a serious problem due to the unavailability of data necessary for their measurement. Furthermore, if the risk assessment model is too complex, it becomes computationally slow and not easily portable to different urban settlements. The aim of this study is to develop a model that is both sufficiently accurate and computationally fast to detect the most critical areas of an urban settlement in the presence of heatwave and pluvial flooding climatic hazards, treated as independent hazards, taking into consideration the vulnerabilities of the physical components that make up an urban settlement. For this purpose, a framework is used to decompose an urban settlement into two physical subsystems: buildings and open spaces, and the characteristics of the two subsystems identified in [5] are taken into consideration for the assessment of their vulnerability to heatwave and pluvial flooding phenomena. Since the two types of climate hazards are independent, the most natural way to determine which areas are most critical, as they are vulnerable to both phenomena, is to use a MultiCriteria Decision Making Analysis model (MCDA), in which the vulnerability of each subsystem to each phenomenon is considered a criterion whose sub-criteria are connected to the characteristics of the subsystem that affect its vulnerability. In our research, we implemented a fuzzy-based multi-criteria model proposed in [6] to evaluate which subzones of an urban settlement into which a study area is divided best meet a set of criteria.