Abstract
Communities are confronted with the rapidly growing impact of disasters, due to many factors that cause an increase in the vulnerability of society combined with an increase in hazardous events such as earthquakes and floods. The possible impacts of such events are large, also in developed countries, and governments and stakeholders must adopt risk reduction strategies at different levels of management stages of the communities. This study is aimed at proposing a sound qualitative multi-hazard risk analysis methodology for the assessment of combined seismic and hydraulic risk at the regional scale, which can assist governments and stakeholders in decision making and prioritization of interventions. The method is based on the use of machine learning techniques to aggregate large datasets made of many variables different in nature each of which carries information related to specific risk components and clusterize observations. The framework is applied to the case study of the Emilia Romagna region, for which the different municipalities are grouped into four homogeneous clusters ranked in terms of relative levels of combined risk. The proposed approach proves to be robust and delivers a very useful tool for hazard management and disaster mitigation, particularly for multi-hazard modeling at the regional scale.
Highlights
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
We recall that this paper discusses an individual application of machine learning tools to a multi-risk assessment of a Northern Italy case study
We had at our disposal a massive amount of data from the ISTAT database containing indicators and data on seismic, hydrogeological, and volcanic risk as well as demographic, housing, territorial and geographical information, obtained through the integration of various institutional sources such as Istat, Institute of Geophysics and Vulcanology (INGV), ISPRA, Italian Ministry for Cultural Heritage
Summary
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. 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]
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