## 1 Introduction  
The frequency of natural extreme events is increasing worldwide [1, 2, 3, 4, 5, 6, 7, 8, 9], and human activities often interact with devastating effects, affecting people and natural environments, and producing great economic losses, especially in developing countries. On the other hand, in some developed countries, disasters have been decreasing since the beginning of the 20 th century [3, 4]. Understanding risk involving vast inhabited areas is, therefore, paramount, particularly when assessing potential losses produced by a combination of multiple hazards, which are defined as the probability of occurrence in a specified period of a potentially damaging event of a given magnitude on a given area [5]. In fact, total risk is a measure of the expected human (casualties and injuries) and economic (damage to property and activity disruption) losses due to a particular adverse natural phenomenon. Such a measure is conceptually assumed as the product of hazard, vulnerability, and exposure instances [6]. Exposure of people to the consequences of extreme natural phenomena could be reduced if predictive models based on new approaches and deeper knowledge of effective factors were employed [7].  
Many areas on Earth are subjected to the effects of coexisting multiple hazards, among which floods and earthquakes are some of the most widespread [8; 9] and even if it is well established that inhabited environments are affected by multiple hazardous processes, most studies focus on a single hazard [10]. However, hazards usually interact with each other and contribute to the overall risk in a complex way. For this reason, the development of multi-hazard risk assessment approaches is of first importance [11] and multi-hazard mapping is receiving increasing attention [12; 13]. In particular, [PERSON] et al. proposed a multi-hazard risk assessment methodology in New Zealand, devising an adaptable computational tool allowing its users to input the natural phenomena of interest [11]. Still, relatively scarce are the studies exploiting machine learning techniques to assess multi-hazard risks [14; 15; 16], albeit machine learning is especially useful when dealing with the huge amount of data encountered in risk analysis, particularly at the regional scale.  
In this study, machine learning is used to construct a risk assessment framework in which the combined effects of two major natural events (flood and earthquakes) are analyzed for the Emilia Romagna test region (Italy). A large input dataset containing, for each municipality of the test region, a wide number of quantitative variables related to hazard, exposure, and vulnerability instances for both flood and earthquake hazards is adopted. Then, the number of variables is suitably reduced by means of Principal Component Analysis (PCA) [17; 18; 19], and the municipalities are subsequently grouped into four approximately risk-wise homogeneous clusters using a K-means clustering algorithm [20; 21]. Finally, a qualitative overall risk level is assigned to each cluster. The proposed methodology represents a robust tool for the qualitative multi-hazard risk assessment at the regional scale, which enables suitable extraction of risk-related information from a large input dataset and provides a useful instrument that assists stakeholders in decision-making processes, especially with respect to intervention prioritization.