The key role of hazard indices and hotspot in disaster risk management: the case study of Napoli and Pozzuoli municipalities (Southern Italy)

Totaro, Federica; Alberico, Ines; Martire, Diego Di; Nunziata, C.; Petrosino, Paola

doi:10.1080/17445647.2019.1698472

Cited by 14 publications

(11 citation statements)

References 44 publications

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“…Finally, following the definition from [30], beaches with high single score indexes or with a co-existence of multiple hazards (most affected) were considered to be hotspots. Here, a hotspot was defined as a beach affected by both storms, with a score higher than the doble of the sum of the mean score per beach for January 2017 and January 2020.…”

Section: Storm-induced Damagementioning

confidence: 99%

The Use of News Information Published in Newspapers to Estimate the Impact of Coastal Storms at a Regional Scale

Sancho-García

Guillén

Gràcia

et al. 2021

JMSE

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The evaluation of coastal damage caused by storms is not straightforward and different approaches can be applied. In this study, damage caused by extreme storms is evaluated at a regional scale based on news information published in regional newspapers. The data derived from the news are compared with hydrodynamic parameters to check the reliability of this methodology as a preliminary” fast approach” to evaluate storm damage and to identify hotspots along the coast. This methodology was applied to the two most extreme storms ever recorded along the Spanish Mediterranean coast, which occurred in January 2017 and January 2020, severely impacting the coast and causing significant community concerns. The news information from different media sources was processed and weighted to describe the resulting erosion, inundation, sand accumulation, and destruction of infrastructures. Moreover, an accuracy index for scoring the quality of the information was proposed. In spite of some limitations of the method, the resulting regional coastal hazard landscape of damage provides a rapid overview of the intensity and distribution of the damage and enables one to identify the location of potential hotspots for the analyzed extreme storm events. The results show that estimated damage intensity is better related to maximum wave energy than cumulative wave energy during a storm, and that beach characteristics should also be included for understanding the distribution of coastal damage.

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Section: Storm-induced Damagementioning

confidence: 99%

The Use of News Information Published in Newspapers to Estimate the Impact of Coastal Storms at a Regional Scale

Sancho-García

Guillén

Gràcia

et al. 2021

JMSE

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“…There is a strong case for investments in mid-to long-term DRR focused on community and household capacity, prioritizing women's active and equal participation as community leaders, and disability inclusion. Totaro et al [11] drew maps of monothematic and synthetic indices to describe the hazard status of metropolitan areas; a hazard hotspot map was also elaborated to identify areas with high hazards.…”

Section: Disaster Risk Reductionmentioning

confidence: 99%

Research on the Construction of a Natural Hazard Emergency Relief Alliance Based on the Public Participation Degree

et al. 2020

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At present, in light of new situations and the new task of natural hazard response, effective public participation in emergency relief has become an urgent task that can reduce economic losses and casualties. The purpose of this paper is to construct a natural hazard emergency relief alliance and analyze the mechanisms and dynamics of public participation. In this study, methods based on a multi-agent system were adopted, and we used different participants as heterogeneous agents with different attitudes and resources. Using four different processes, namely participation proposals, negotiation interval, negotiation decision-making function, and participation strategy, we comprehensively construct an emergency relief alliance for natural hazards. In addition, the dynamic public interaction process is analyzed and a construction algorithm is given. The experimental results show that the proposed method has better performance in alliance formation efficiency, negotiation efficiency, and agent utility. The research results illustrate that the public’s attitudes and resources influence the construction of emergency relief alliances; a greater degree of public participation contributes to a more efficient alliance formation. The findings of this study contribute to the promotion of public cooperation and improvement in the efficiency of natural hazard emergency relief.

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“…In fact, over a few decades, huge neighbourhoods have grown rapidly, reaching a rather high population density of 6000-7000 inhabitants per square kilometer. In many cases, these areas are exposed to natural hazards, such as floods and landslides [8,12]. Recent and repeated landslides, in Naples and other sites in Campania, have created awareness of the landslide hazard in the region, and have encouraged engineering geologists and local administrators to undertake research activities aimed at risk assessment and mitigation.…”

Section: Introductionmentioning

confidence: 99%

Landslide Susceptibility Assessment of Wildfire Burnt Areas through Earth-Observation Techniques and a Machine Learning-Based Approach

et al. 2020

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Climate change has increased the likelihood of the occurrence of disasters like wildfires, floods, storms, and landslides worldwide in the last years. Weather conditions change continuously and rapidly, and wildfires are occurring repeatedly and diffusing with higher intensity. The burnt catchments are known, in many parts of the world, as one of the main sensitive areas to debris flows characterized by different trigger mechanisms (runoff-initiated and debris slide-initiated debris flow). The large number of studies produced in recent decades has shown how the response of a watershed to precipitation can be extremely variable, depending on several on-site conditions, as well as the characteristics of precipitation duration and intensity. Moreover, the availability of satellite data has significantly improved the ability to identify the areas affected by wildfires, and, even more importantly, to carry out post-fire assessment of burnt areas. Many difficulties have to be faced in attempting to assess landslide risk in burnt areas, which present a higher likelihood of occurrence; in densely populated neighbourhoods, human activities can be the cause of the origin of the fires. The latter is, in fact, one of the main operations used by man to remove vegetation along slopes in an attempt to claim new land for pastures or construction purposes. Regarding the study area, the Camaldoli and Agnano hill (Naples, Italy) fires seem to act as a predisposing factor, while the triggering factor is usually represented by precipitation. Eleven predisposing factors were chosen and estimated according to previous knowledge of the territory and a database consisting of 400 landslides was adopted. The present work aimed to expand the knowledge of the relationship existing between the triggering of landslides and burnt areas through the following phases: (1) Processing of the thematic maps of the burnt areas through band compositions of satellite images; and (2) landslide susceptibility assessment through the application of a new statistical approach (machine learning techniques). The analysis has the scope to support decision makers and local agencies in urban planning and safety monitoring of the environment.

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The key role of hazard indices and hotspot in disaster risk management: the case study of Napoli and Pozzuoli municipalities (Southern Italy)

Cited by 14 publications

References 44 publications

The Use of News Information Published in Newspapers to Estimate the Impact of Coastal Storms at a Regional Scale

The Use of News Information Published in Newspapers to Estimate the Impact of Coastal Storms at a Regional Scale

Research on the Construction of a Natural Hazard Emergency Relief Alliance Based on the Public Participation Degree

Landslide Susceptibility Assessment of Wildfire Burnt Areas through Earth-Observation Techniques and a Machine Learning-Based Approach

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