Effective Disaster Mitigation Research Articles

Future Changes in Global Atmospheric Rivers Projected by CMIP6 Models

AbstractUnderstanding the present and future features of atmospheric rivers (ARs) is critical for effective disaster prevention and mitigation efforts. This study comprehensively assesses the performance of ARs in Phase 6 of the Coupled Model Intercomparison Project (CMIP6) models on both seasonal and interannual timescales within the historical period and investigates the future projection of ARs under different emission scenarios on a global scale. The multi‐model mean results obtained using the PanLu detection algorithm consistently exhibit agreement with the observational AR climatology and capture interannual fluctuations as well as the relationships with large‐scale drivers. The future projections reveal increased AR frequency, intensity, duration, and spatial extent and decreased landfall intervals with regional variations and seasonal fluctuations. Besides, the AR frequency increase will accelerate around the middle of the century, attributed to a non‐linear rise in surface temperature. Furthermore, mid‐latitude ARs are gradually shifting toward higher latitudes in both hemispheres under SSP585, with Greenland experiencing a substantial increase in AR frequency and AR‐induced precipitation. The hydrological implications arising from more frequent ARs are manifested more prominently in AR‐induced heavy precipitation (HP), with regions historically characterized by lower AR occurrence also receiving a higher percentage of precipitation from ARs. At last, an incremental decomposition highlights the dominant role of thermal effects and relatively limited contributions from dynamical effects in AR changes. Besides, the interplay between regionally divergent temperature amplification results in different dynamically driven AR responses across the globe.

Rainfall variability in Indonesia new capital associated with the Madden-Julian Oscillation and its contribution to flood events

Accurate and current rainfall analysis is crucial for planning and development in the new capital city of Indonesia (IKN). This study examines the effect of the Madden-Julian Oscillation (MJO) on rainfall variability in IKN using a 20-year dataset of Integrated Multi-Satellite Retrievals for GPM (IMERG) version 6 and automatic weather station (AWS) data. The study analyzes flood events in IKN by examining flood information provided by the National Agency for Disaster Countermeasures (BNPB) from 2008 to 2022 and the contribution of the MJO to these events. The findings indicate that the MJO significantly influences rainfall variability in IKN, with a more pronounced effect during the dry season (JJASO) than in the wet season (NDJFMAM). This is demonstrated by the higher occurrence of wet days and increased daily rainfall intensity, which is linked to extreme rainfall and longer-duration events, mainly between midnight and morning (0400–0600 LST). Notably, although floods are more common during the rainy season, the MJO can intensify flood events in IKN during the dry season. Therefore, when developing an effective flood disaster mitigation system for IKN, it is crucial to consider the amplitude and phase of the MJO.

Evaluation of landslide disaster susceptibility in Shuangbai County based on random forest modeling

Shuangbai County, Chuxiong Yi Autonomous Prefecture, Yunnan Province, China, has a high incidence of geohazard events and is a key area for geologic risk prevention and control in China, with landslides accounting for the majority of all geohazard events, so understanding landslide-prone areas in the region is critical to reducing the impacts of landslide hazards. In this study, based on literature reference and background investigation of the study area, relevant data and existing research results were collected, and six types of evaluation factors including elevation, slope, slope direction, rainfall, vegetation type and vegetation index were selected to construct a random forest (RF) model to analyze the landslide susceptibility of Shuangbai County, Chuxiong Prefecture, Yunnan Province, and to obtain the evaluation map of landslide susceptibility in Shuangbai County. The results show that the landslide disaster high susceptibility areas in Shuangbai County are mainly distributed in the northern part of the county in Toudian Township and Dazhuang Township, the southeastern part of Anlongbao Township and the southwestern part of Ejia Township in the vicinity of the Hengduan Mountain Range, which is an important reference for the development of scientific and effective disaster prevention and mitigation strategies, and for the enhancement of the quality of life and the safety of the residents in the area of Shuangbai County.

Improving the Effectiveness of Disaster Mitigation in Wonogiri Regency, Indonesia Using House of Risk Method

Wonogiri Regency, the Republic of Indonesia, is an area that has potential to be affected by various types of disasters. This study aims to identify the types of disaster potentials in the Wonogiri Regency and to provide recommendations for effective disaster mitigation strategies that can be effectively implemented by the Regional Disaster Management Agency (Badan Penanggulangan Bencana Daerah or BPBD in short) of the Wonogiri Regency during 2021-2026 time period by using the House of Risk (HoR) method. The study found that floods, landslides, strong winds, volcanic eruptions, earthquakes, tsunamis, forest and land fires, droughts, and extraordinary events are likely to take place in the regency. The implementation of the HoR phase 1 yields 19 disaster risk events and 19 disaster risk agents. The use of Pareto diagram to the disaster risk agents results in 8 dominant risk agents of disaster, namely “Unstable ground”; “The trees are too old and fragile”; “The trees are too dense”; Lack of water resources”; Heavy rain intensity”; “Struck by disaster materials”; “Epidemic of a disease”; and “The building construction is not strong”. The implementation of the HoR phase 2 produces 15 mitigation strategies along with their priority order in which the 5 mitigation strategies with the highest priority are “Working with related parties to reduce the potential of disasters”; “Conducting socialization and education of disasters”; “Establishing disaster-resilient villages”; “Mapping disaster-prone areas”; and “Training for disaster volunteers”.

Unveiling spatial inequalities: Exploring county-level disaster damages and social vulnerability on public disaster assistance in contiguous US

Understanding the dynamics between public disaster assistance, disaster damages, and social vulnerability at county-level is crucial for designing effective disaster mitigation strategies. This study utilized the Local Bivariate Moran Index (LBMI) and geographically weighted regression (GWR) models to examine spatial patterns and relationships between disaster damages, social vulnerability, and public disaster assistance in contiguous US counties from 2001 to 2021. LBMI results reveal that public disaster assistance has predominantly been directed towards post-disaster recovery efforts, with a particular focus on coastal communities affected by major declared disasters. However, the distributions of public assistance and individual housing assistance, which are the two primary sources of public disaster assistance, do not adequately cover physically and socially vulnerable communities. The distribution of pre-disaster risk mitigation also falls short of sufficiently covering vulnerable communities. Results further indicate the complex interactions between different categories of natural disasters and public assistances. The GWR model results demonstrate spatial variations in predicting each category of public disaster assistance. These findings indicate the need to address disparities in accessing public disaster assistance in the US, and advocate for more equitable disaster mitigation strategies.

Assessing the Risk of Extreme Storm Surges from Tropical Cyclones under Climate Change Using Bidirectional Attention-Based LSTM for Improved Prediction

Accurate prediction of storm surges is crucial for mitigating the impact of extreme weather events. This paper introduces the Bidirectional Attention-based Long Short-Term Memory (LSTM) Storm Surge Architecture, BALSSA, addressing limitations in traditional physical models. By leveraging machine learning techniques and extensive historical and real-time data, BALSSA significantly enhances prediction accuracy. Utilizing a bidirectional attention-based LSTM framework, it captures complex, non-linear relationships and long-term dependencies, improving the accuracy of storm surge predictions. The enhanced model, D-BALSSA, further amplifies predictive capability through a doubled bidirectional attention-based structure. Training and evaluation involve a comprehensive dataset from over 70 typhoon incidents in Macao between 2017 and 2022. The results showcase the outstanding performance of BALSSA, delivering highly accurate storm surge forecasts with a lead time of up to 72 h. Notably, the model exhibits a low Mean Absolute Error (MAE) of 0.0287 m and Root Mean Squared Error (RMSE) of 0.0357 m, crucial indicators measuring the accuracy of storm surge predictions in water level anomalies. These metrics comprehensively evaluate the model’s accuracy within the specified timeframe, enabling timely evacuation and early warnings for effective disaster mitigation. An adaptive system, integrating real-time alerts, tropical cyclone (TC) chaser, and prospective visualizations of meteorological and tidal measurements, enhances BALSSA’s capabilities for improved storm surge prediction. Positioned as a comprehensive tool for risk management, BALSSA supports decision makers, civil protection agencies, and governments involved in disaster preparedness and response. By leveraging advanced machine learning techniques and extensive data, BALSSA enables precise and timely predictions, empowering coastal communities to proactively prepare and respond to extreme weather events. This enhanced accuracy strengthens the resilience of coastal communities and protects lives and infrastructure from the escalating threats of climate change.

Several strong historical earthquakes during Ming and Qing Dynasties and their effects to local reconstruction at Southern Shanxi Province, North China: Insight from the Chinese literature

The dissection of related Chinese literature of historical strong earthquakes is very important for formulating effective disaster mitigation measures. During the Ming and Qing Dynasties (AD 1368–1910) in North China, there were three strong earthquakes (AD 1556 Huaxian M 8.25 earthquake, AD 1695 Linfen M 7.75 earthquake and AD 1815 Pinglu M 6.75 earthquake), caused great damages to local cities of southern Shanxi Province, including damages of various types of buildings, numerous casualties, while the society and economy suffered a sudden heavy blow. Meanwhile, the occurrence of these earthquakes also provides important opportunities to restore the regional social order. In addition to the support of national disaster relief policies, the local officials actively organized people to cope with the disasters, made every effort to reconstruct the cities, that reflected the feelings of "caring about People's livelihood" or "the principle of supremacy of life", to improve the efficiency of regional social order benign operation. Certainly, the recovery and reconstruction process after the three strong earthquakes reflected imbalance between different time, regions, and between urban and rural areas. The literature review in this article has certain reference significance for the formulation and improvement of current disaster reduction measures.

Prediction of the Deformation of Heritage Building Communities under the Integration of Attention Mechanisms and SBAS Technology

The protection of heritage building communities is of important historical significance, the occurrence of a landslide is related to the safety and stability of the heritage building, and ground monitoring and forecasting are the key steps for the early warning and timely restoration of the heritage building. This study utilizes remote sensing technology to monitor the ground of a cultural heritage building, and employs a Long Short-Term Memory (LSTM) network for prediction. Firstly, we conducted ground subsidence monitoring within a specific time series of the study area using heritage remote sensing images and SBAS-InSAR technology. Following the subsidence monitoring, and incorporating an attention mechanism, we effectively localized and extracted features of heritage building clusters within the region. This approach efficiently addresses the challenge of feature identification resulting from the dense distribution of buildings and the similarity between various objects. The results indicate that the maximum subsidence rate in the research area reached −60 mm/year, reached a maximum uplift rate of 45 mm/year, and that the maximum cumulative subsidence reached −65 mm. Secondly, for the multi-level, multi-scale, and class-specific objects in remote sensing images, the LSTM network enables adaptive contextual information during deep and shallow feature extraction. This allows for better contextual modeling and the correlation between predicted and actual results reaches a 0.95 correlation, demonstrating the accurate predictive performance of the LSTM network. In conclusion, both LSTM and SBAS technologies play a crucial role in decision-making for heritage buildings, facilitating effective early warning and disaster mitigation.

Rapid spatio-temporal prediction of coastal urban floods based on deep learning approaches

Accurate real-time prediction of flood inundation ranges and water depths is crucial for effective flood warning and disaster mitigation. However, traditional hydrodynamic flooding simulation methods based on physical mechanisms are very complex in modeling and time-consuming. Consequently, they often fall short in meeting the requirements of near real-time flood warnings. In this study, we explore the potential applications of three deep learning neural network architectures, namely Convolutional Neural Networks (CNN), Convolutional Neural Network with Batch Normalization (CNNBN) and Multilayer Perceptron (MLP), for the spatio-temporal explicit prediction and mapping of urban floods in the coastal city of Linhai in eastern China. The results demonstrate that these three deep learning models exhibit the capability to sensitively capture the dynamic changes in the process of floods evolution and simulate the depth of floodwater efficiently and accurately. Importantly, the deep learning models only take 14–24 s to generate the inundation depth map of the entire research domain, representing a significant improvement in prediction speed, exceeding that of traditional hydrodynamic models by >20 times. Due to the high computational efficiency, superior performance and simple modeling process, the deep learning models are expected to become a powerful tool for near real time flood early warning.

Disaster Mitigation Model for Sustainable Tourism Development at the Nam Salu Openpit Geosite: Insights from the Belitong UNESCO Global Geopark

This research aims to develop an effective disaster mitigation model to support sustainable tourism development at the Nam Salu Openpit Geosite, which is an integral part of the Belitong UNESCO Global Geopark. The qualitative approach method uses descriptive methods through case studies, namely the Open Pit Nam Salu (OPNS) geosite. The results of research conducted regarding the Open Pit Nam Salu (OPNS) Geosite in East Belitung revealed that the potential threat of disaster to this tourist site is very significant. The main factors influencing the vulnerability of OPNS Geosites to natural disasters include ecosystem damage due to unmanaged mining activities, changes in the landscape caused by intensive mining practices, and disruption of slope stability due to excessive exploitation of natural resources. In addition, climate change can also worsen the risk of disasters, including landslides and coastal erosion. Therefore, it is necessary to carry out structured disaster mitigation measures, including an early warning system, emergency management training, regular risk mapping, evacuation planning, development of a disaster information center, as well as education and public awareness programs to maintain the sustainability of the OPNS Geosite as a safe tourist destination. and sustainable.

APPLICATION OF GOOGLE EARTH ENGINE PLATFORM TO IDENTIFY FLOODED AREAS AND ASSESS FLOOD DEPTH IN QUANG TRI PROVINCE

In recent years, the Application of the Google Earth Engine Platform has gained substantial recognition as a powerful tool for remote sensing and geospatial analysis. This paper delves into its utility within the context of flood monitoring and assessment, focusing on Quang Tri Province for the historic flood in 2020. The study leverages the platform's capabilities to identify and delineate flooded areas, offering a timely and accurate overview of the extent of inundation during flood events. Moreover, the paper demonstrates how the Google Earth Engine Platform enables the assessment of flood depths, providing critical insights for disaster management and response planning. By amalgamating satellite imagery, terrain data, and hydrological information, the study showcases the platform's potential to enhance flood-related decision-making in vulnerable regions like Quang Tri Province. The findings underscore the significance of innovative geospatial technologies in addressing the challenges posed by flooding, ultimately contributing to more effective disaster mitigation strategies and informed policymaking.

Determination of ground motion parameters of urban centers of Balochistan province

Ground motion prediction equations (GMPEs) are cornerstone of Probabilistic Seismic Hazard Assessment. A big pool of GMPEs is available nowadays for active crustal and subduction zone categories which are derived from dataset of different regions including the database of Middle East, Europe (non-NGA) and NGA WEST 2 database. For countries like Pakistan which do not have local GMPEs, selection of GMPE is difficult and is merely based on hit and trial. A Comparative analysis of latest set of GMPEs for these respective categories on seismic hazard of Pakistan is unknown. In this study, Probabilistic Seismic Hazard analysis is conducted on urban centers (Gwadar and Quetta) of Balochistan province, a sample from Pakistan is given their potential to become economic hubs as rapid infrastructure development is expected in the region. The analysis is based on area source approach along with the modelling of major active crustal faults of the region. Three sets of GMPEs are selected where first set consists of GMPEs developed by NGA WEST 2, the second set consists of non-NGA based GMPEs while the third set is a hybrid set in which NGA WEST 2 and non-NGA based GMPEs are used together. Logic tree method is employed in this work to take into consideration of the epistemic uncertainty in different GMPEs for each tectonic zone and the seismic source models as well. For 10% probability of exceedance in 50 years (return period 475 years), updated hazard maps for PGA and spectral acceleration for structural period of 0.1 s, 0.2 s, and 1 s are created. These results can be effectively used to reduce seismic risk, to develop effective disaster mitigation, and management strategies.

The Application of the Long-Short Term Memory (LSTM) Forecasting Method on the Impact of Tropical Cyclones in Indonesia

Effective disaster mitigation strategies are paramount in the realm of risk management concerning natural calamities, with the primary objective of mitigating potential devastation. A pragmatic and impactful method involves predicting the contributory aspects of such disasters, encompassing variables such as torrential rainfall and formidable wind velocities that tropical cyclones bring. In this study, a comparative analysis of forecasting methodologies is undertaken, precisely the Long Short-Term Memory (LSTM) technique and the Holt Winter approach, both directed toward gauging the impact of tropical cyclones. This investigation focuses on two critical factors: the forecast of precipitation intensity and the estimation of maximum wind speed. The outcomes underscore the superior predictive capabilities of the LSTM method, unequivocally revealing its aptitude for predicting rainfall and wind speed. Impressively, the LSTM method yields remarkable precision levels of 97.433% for rainfall and an even higher accuracy of 99.018% for maximum wind speed forecasting. In essence, this study highlights LSTM's efficacy in disaster prediction with substantial accuracy.

Flash flood-risk areas zoning using integration of decision-making trial and evaluation laboratory, GIS-based analytic network process and satellite-derived information

Assessing areas prone to flash floods is crucial for effective disaster management and mitigation. This study proposes a framework for mapping flood-prone areas by integrating geographic information system (GIS), remote sensing data, and multi-criteria decision-making (MCDM) techniques. The hybrid MCDM model combines the decision-making trial and evaluation laboratory (DEMATEL) with GIS-based analytic network process (ANP) to evaluate flood vulnerability in Golestan province, Iran. Fourteen criteria related to flood potential, including elevation, slope, aspect, vegetation density, soil moisture, flow direction, river distance, rainfall and runoff, flow time, geomorphology, drainage density, soil type, lithology, and land use, were considered. In areas where official data was lacking, a questionnaire was administered to gather information from 15 specialists, experts, and 20 local managers. The relationships between criteria were analyzed using the DEMATEL method, and their weights were determined using the ANP method. Topography was found to have the greatest impact on flood risk, followed by the type of surface and vegetation cover. Hydrographic, soil and geology, climatic also influence flooding in the region. The study identified the northern and central parts of the study area being at higher risk of flooding compared to the southern part. Based on the flood intensity map, 68 villages (50% of all villages in the Qarasu watershed) with a population of approximately 83,595 were identified as at risk of flooding. The proposed GIS-DANP model provides a valuable tool for flood management and decision-making, aiding in risk reduction and minimizing casualties.

Eruptive history of Mt. Fuji over the past 8000 years based on integrated records of lacustrine and terrestrial tephra sequences and radiocarbon dating

Reliable records of past volcanic activities are essential for assessing future eruption probabilities and establishing effective volcanic disaster mitigation plans. We integrated the lacustrine and terrestrial sequences of tephra deposits at the northeastern foot of Mt. Fuji based on high-resolution radiocarbon (14C) dating to elucidate its eruptive history over the past 8000 years. Accurate chronologies of 29 tephra layers in the Lake Yamanaka sediments (core YA-1) were determined using the age-depth model of the 14C of bulk organic matter after variability correction of lake reservoir ages. The following ages were compared to those of major tephra deposits in the proximal trench section (MF20-01) and in the literature. From ∼7300 to ∼3200 cal yr BP, four tephra layers in core YA-1 (i.e., YA-T1, 2, 7, and 10) were likely correlated with the scoria fall deposits S-3, 5, 6, and 10 respectively, as reported in the outcrops near the lake. The core also features six undetected eruptions from ∼5050 to ∼3900 cal yr BP, demonstrating the advantages of lake sediments proximal to the volcano in identifying previously undetected eruptions. After ∼3200 cal yr BP, five tephra layers in core YA-1, ranging from ∼2900 to ∼1600 cal yr BP (YA-T11, 12, 14, 20, and 22), showed similar age ranges as the major tephra layers in MF20-01 (IY5, 8, 11, 25, and 26), with identical lithological characteristics and stratigraphic orders. This suggests that the tephra sequence in the trench section positively reflects the major eruptive events over the past 2900 years, providing a basis for the comprehensive reconstruction of volcanic eruption histories in the region.

Facing the Wildfire Spread Risk Challenge: Where Are We Now and Where Are We Going?

Wildfire is a sudden and highly destructive natural disaster that poses significant challenges in terms of response and rescue efforts. Influenced by factors such as climate, combustible materials, and ignition sources, wildfires have been increasingly occurring worldwide on an annual basis. In recent years, researchers have shown growing interest in studying wildfires, leading to a substantial body of related research. These studies encompass various topics, including wildfire prediction and forecasting, the analysis of spatial and temporal patterns, the assessment of ecological impacts, the simulation of wildfire behavior, the identification of influencing factors, the development of risk assessment models, techniques for managing combustible materials, decision-making technologies for firefighting, and fire-retardant methods. Understanding the factors that affect wildfire spread behavior, employing simulation methods, and conducting risk assessments are vital for effective wildfire prevention, disaster mitigation, and emergency response. Consequently, it is imperative to comprehensively review and explore further research in this field. This article primarily focuses on elucidating and discussing wildfire spread behavior as a key aspect. It summarizes the driving factors of wildfire spread behavior and introduces a wildfire spread behavior simulation software and its main applications based on these factors. Furthermore, it presents the research progress in wildfire risk assessment based on wildfire spread behavior factors and simulation, and provides an overview of various methods used for wildfire risk assessment. Finally, the article proposes several prospects for future research on wildfire spread: strengthening the dynamic monitoring of wildfires and utilizing comprehensive data from multiple sources, further exploring the differential effects of key factors on wildfire spread, investigating differences in driving factors, improving wildfire models in China, developing applicable software, and conducting accurate and scientific assessments of wildfire risks to protect ecological resources.

Disaster Preparedness and Awareness among University Students: A Structural Equation Analysis

Students have long been among those most emotionally and physically affected by natural or manmade disasters, yet universities and colleges continue to lack effective disaster response and mitigation practices. This research identifies how students' socio-demographics and disaster preparedness indicators (DPIs) impact their awareness of the dangers of disasters and their ability to survive and cope with the changes that disasters bring. A comprehensive survey was designed and distributed to university students to gain an in-depth understanding of their perceptions of disaster risk reduction factors. A total of 111 responses were received, and the impact of the socio-demographics and DPIs on the students' disaster awareness and preparedness were evaluated by employing structural equation modeling. The results indicate that the university curriculum impacts the disaster awareness of students while the establishment of university emergency procedures impacts the disaster preparedness of students. The purpose of this research is to enable university stakeholders to identify the DPIs that are important to the students so that they can upgrade their programs and design effective DRR courses. It will also aid policymakers in redesigning effective emergency preparedness policies and procedures.

A Methodology for Assessing the Component-Level Fragility Curves and Its Application to a Class of Integral Abutment Bridges

Seismic vulnerability assessment of structures is a major research area and the output, i.e., fragility curves (FCs), within a probabilistic framework, devises effective seismic disaster mitigation. The present study proposes a novel methodology for obtaining the structural component-level FC through two new approaches with respect to the assessment of seismic demand and the computation of fragility. The present study evolves an existing nonlinear static method by a few steps to be able to assess the probabilistic demands and develops a numerical computation technique that exploits the actual profiles of the evaluated distributions of the structural capacity and demand data in a study to obtain the fragility magnitudes. The substitute methodology is implemented to derive the FCs of a class of integral abutment bridges while assessing the demands in a simple manner and avoiding the complexities of dynamic analysis and yet with reliable precision as well as averting the misjudgment of fragilities stemming from the lognormal or normal fit on the data, which are practically very rough fit or misfit to it, as imposed in the traditional fragility formulation.

Human and economic impacts of natural disasters: can we trust the global data?

Reliable and complete data held in disaster databases are imperative to inform effective disaster preparedness and mitigation policies. Nonetheless, disaster databases are highly prone to missingness. In this article, we conduct a missing data diagnosis of the widely-cited, global disaster database, the Emergency Events Database (EM-DAT) to identify the extent and potential determinants of missing data within EM-DAT. In addition, through a review of prominent empirical literature, we contextualise how missing data within EM-DAT has been handled previously. A large proportion of missing data was identified for disasters attributed to natural hazards occurring between 1990 and 2020, particularly on the economic losses. The year the disaster occurred, income-classification of the affected country and disaster type were all significant predictors of missingness for key human and economic loss variables. Accordingly, data are unlikely to be missing completely at random. Advanced statistical methods to handle missing data are thus warranted when analysing disaster data to minimise the risk of biasing statistical inferences and to ensure global disaster data can be trusted.

A modular approach for vulnerability assessment in Southern Italy: A forest fire hazard scenario.

Vulnerability is an important component for risk assessment, representing the main element in the perception of the risk. This paper shows a methodological approach to describe a composite vulnerability index suitable to be used at the census scale. The aim of this study is threefold: at first, a new administrative limit, "Territorial Context" (TC), for the purpose of the emergency management is investigated. Second, it improves the common vulnerability methods applying different weights and a new approach of vulnerability levels classification by using fuzzy analysis. Finally, it provides vulnerability maps, which represent a simple way to identify areas where the capacity to cope with a hazard, during an event, is strongly influenced by social and territorial conditions and the road infrastructure accessibility. The proposed TC Vulnerability Index (TCVI) aggregates three indices to capture the complex realities of the investigated system that cannot be adequately quantified by a single index. Specifically, two indices measure the social (TCVIpeople) and territorial (TCVIexposure) vulnerability through an inductive approach based on the principal component analysis. The third index (TCVIemergency) provides a measure of the emergency management operating system, essentially based on the possibility of moving to safe areas, for citizens, or to coordination centers and risk areas, for rescuers. Results show notable differences in the spatial distribution of vulnerability, highlighting the multidimensionality and heterogeneity of the census area characteristics. These findings would provide a scientific base for the public decision-makers to implement.