Applied Sciences  
Special Issue on Natural Hazards Risk Assessment for Disaster Mitigation  
[PERSON] 1,*\({}^{\copyright}\), [PERSON] 2\({}^{\copyright}\) and [PERSON] 2\({}^{\copyright}\)  
Footnote 1: Faculty of Civil Engineering, Architecture and Geodesy, University of Split, 21000 Split, Croatia  
Footnote 2: Engineering Department, University of Ferrara, 44121 Ferrara, Italy  
Footnote 3: Correspondence: [EMAIL_ADDRESS]  
Knowledge and awareness of the risks generated by natural hazards are essential requirements for the enhancement of communities' resilience to disasters. The Sendai Framework for Disaster Risk Reduction 2015-2030 has recently pointed out the necessity of undertaking actions aimed to anticipate, manage, and mitigate disaster risks, to reduce their economic and social impact and protect health, socioeconomic assets, cultural heritage, and ecosystems of communities and countries. While the increasing occurrence of disasters caused by meteorological events, such as floods, storms, and droughts, can be directly ascribed to the consequence of climate change, disasters induced by earthquakes and tsunamis are increasing even if the frequency of occurrence is historically unchanged. Therefore, other anthropogenic causes intervene to determine an increment of a risk exposure and community vulnerability, such as land misuse in densely populated areas and coastal zones. This Special Issue addresses concepts, methods, and predictive methodologies for assessing natural hazards risks. This Special Issue presents fifteen articles focusing on the single-risk assessment of a broad range of natural hazards, such as earthquakes, river, and see floods, meteotsunamis, tornados, hydrological and meteorological drought, liquefaction, as well as on multirisk assessment in the presence of multiple hazards. The adopted methodologies rely on: (a) quantitative, semi-quantitative, and qualitative methods for the assessment of the risks related to natural hazards; (b) risk analysis at different scales; (c) multi-hazard risk assessment techniques; (d) real-time hazard monitoring and warning systems; (e) disaster mitigation strategies; and (f) risk management and emergency planning at multiple scales. [PERSON] et al. [1] propose a Gaussian process regression (GPR) model for analyzing liquefaction-induced lateral displacement based on an impressive amount (247) of case studies of post-liquefaction events. The performance of the GPR model is assessed using statistical parameters, including the coefficient of determination, coefficient of correlation, Nash-Sutcliffe efficiency coefficient, root mean square error (RMSE), and ratio of the RMSE to the standard deviation of the measured data. It was shown that the GPR model can accurately capture the complicated nonlinear relationships between lateral displacements and their influencing factors. [PERSON] et al. [2] present two case studies of the seismic surveys to estimate the elastic properties of the soil and rock in the shallow subsurface: (1) a town on the Croatian coast, near the city of Split, built on hard rock and (2) a site located in the Italian town of Ferrara, in an alluvial plain. A Multichannel Analysis of Surface Waves and a first-break tomography were carried out to obtain P-, SH-, and SV-velocity profiles. This acquisition allowed for computing the equivalent shear-wave velocity of the first 30 m of the subsurface (VS30) from the SH profiles, as well as it made it possible to deduce other useful parameters such as the VP/VS, and to estimate the soil's stratigraphy through the analysis of the VSV/VSH profiles. [PERSON] et al. [3] studied the spatial distribution of the flood risk of the Municipality of Odiongan using the analytical hierarchy process (AHP) and the geographic information system (GIS), considering the disaster risk factors based on the data collected from various government agencies. The weights of the hazard, exposure, and vulnerability parameters were drawn from the experts' judgment.