Measure and spatial identification of social vulnerability, exposure and risk to natural hazards in Japan using open data

Raduszynski, Theo; Numada, Muneyoshi

doi:10.1038/s41598-023-27831-w

Cited by 16 publications

(11 citation statements)

References 20 publications

(12 reference statements)

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“…Vulnerability assessment is a crucial part in understanding the risk and mitigate it. To understand the risk associated with hazard event, event occurrence probability, exposed element and vulnerability need to be quantified 65 . Risk associated with natural hazard can be defined as the function of hazard, exposure, and vulnerability 66 as shown in Eq.…”

Section: Vulnerability Assessmentmentioning

confidence: 99%

Enhancing co-seismic landslide susceptibility, building exposure, and risk analysis through machine learning

Pyakurel,

K.C.,

Dahal

2024

Sci Rep

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Landslides are devastating natural disasters that generally occur on fragile slopes. Landslides are influenced by many factors, such as geology, topography, natural drainage, land cover, rainfall and earthquakes, although the underlying mechanism is too complex and very difficult to explain in detail. In this study, the susceptibility mapping of co-seismic landslides is carried out using a machine learning approach, considering six districts covering an area of 12,887 km2 in Nepal. Landslide inventory map is prepared by taking 23,164 post seismic landslide data points that occurred after the 7.8 MW 2015 Gorkha earthquake. Twelve causative factors, including distance from the rupture plane, peak ground acceleration and distance from the fault, are considered input parameters. The overall accuracy of the model is 87.2%, the area under the ROC curve is 0.94, the Kappa coefficient is 0.744 and the RMSE value is 0.358, which indicates that the performance of the model is excellent with the causative factors considered. The susceptibility thus developed shows that Sindhupalchowk district has the largest percentage of area under high and very high susceptibility classes, and the most susceptible local unit in Sindhupalchowk is the Barhabise municipality, with 19.98% and 20.34% of its area under high and very high susceptibility classes, respectively. For the analysis of building exposure to co-seismic landslide susceptibility, a building footprint map is developed and overlaid on the co-seismic landslide susceptibility map. The results show that the Sindhupalchowk and Dhading districts have the largest and smallest number of houses exposed to co-seismic landslide susceptibility. Additionally, when conducting a risk analysis based on susceptibility mapping, as well as considering socio-economic and structural vulnerability in Barhabise municipality, revealed that only 106 (1.1%) of the total 9591 households, were found to be at high risk. As this is the first study of co-seismic landslide risk study carried out in Nepal and covers a regional to the municipal level, this can be a reference for future studies in Nepal and other parts of the world and can be helpful in planning development activities for government bodies.

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Section: Vulnerability Assessmentmentioning

confidence: 99%

Enhancing co-seismic landslide susceptibility, building exposure, and risk analysis through machine learning

Pyakurel,

K.C.,

Dahal

2024

Sci Rep

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“…In the literature, different methodologies to assess socioeconomic exposure using standard measures exist [93][94][95][96][97]. The measures widely used to assess natural hazard exposure levels are the built-up area [98], the gross domestic product [99], and the population density [100,101].…”

Section: Future Multi-hazard Exposure Assessmentmentioning

confidence: 99%

A Remote Sensing Method to Assess the Future Multi-Hazard Exposure of Urban Areas

Salvo,

Vitale

2023

Remote Sensing

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As more than 75% of the global population is expected to live in urban areas by 2050, there is an urgent need to assess the risk of natural hazards through a future-focused lens so that adequately informed spatial planning decisions can be made to define preventive risk policies in the upcoming decades. The authors propose an innovative methodology to assess the future multi-hazard exposure of urban areas based on remote sensing technologies and statistical and spatial analysis. The authors, specifically, applied remote sensing technologies combined with artificial intelligence to map the built-up area automatically. They assessed and calibrated a transferable Binary Logistic Regression Model (BLRM) to model and predict future urban growth dynamics under different scenarios, such as the business as usual, the slow growth, and the fast growth scenarios. Finally, considering specific socioeconomic exposure indicators, the authors assessed each scenario’s future multi-hazard exposure in urban areas. The proposed methodology is applied to the Municipality of Rende. The results revealed that the multi-hazard exposure significantly changed across the analyzed scenarios and that urban socioeconomic growth is the main driver of risk in urban environments.

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“…These studies provide theoretical and methodological support for typhoon disaster warning and management based on machine learning. However, the subjective choice of the index framework makes it difficult to compare the research results across different studies [17,18]. In this study, we built a practical framework based on the classification standards in disaster investigation in China and used a combination model of factor analysis and random forest regression to construct a reliable and scientific typhoon disaster loss assessment model.…”

Section: Introductionmentioning

confidence: 99%

Assessment of typhoon disaster loss based on the factor analysis-random forest model

Liu,

Liang,

et al. 2024

J. Phys.: Conf. Ser.

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Typhoon disasters in China’s coastal areas pose significant challenges for disaster prevention and mitigation, urban planning and national economic construction. This study aims to address the problem of incomparable disaster assessment indicators and low prediction accuracy of machine learning for small sample data. It establishes an index system based on the practical disaster investigation classification standards, which ensures data sources and uniformity. It also proposes a combination algorithm of factor analysis-random forest regression for direct economic loss prediction, which improves the typhoon disaster losses prediction. The results show that the optimized model has higher accuracy than single decision tree model, random forest model and factor analysis-decision tree model. The factor analysis method verifies the importance of influencing factors, which indicates that China faces great risks of coastal floods caused by super typhoons. The combination regression model can predict disaster losses reasonably, providing effective technical support for typhoon disaster assessment and management.

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Measure and spatial identification of social vulnerability, exposure and risk to natural hazards in Japan using open data

Cited by 16 publications

References 20 publications

Enhancing co-seismic landslide susceptibility, building exposure, and risk analysis through machine learning

Enhancing co-seismic landslide susceptibility, building exposure, and risk analysis through machine learning

A Remote Sensing Method to Assess the Future Multi-Hazard Exposure of Urban Areas

Assessment of typhoon disaster loss based on the factor analysis-random forest model

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