Living with risk of natural disasters

Осипов, В. И.; Rumyantseva, Nadezhda; Eremina, Olga

doi:10.2205/2019es000673

Cited by 5 publications

(3 citation statements)

References 1 publication

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“…Emblemsvåg (2008) conducts risk analysis for a region in Norway for rock falls from natural disasters. Osipov et al (2019) conduct a risk assessment for natural disasters such as earthquakes, floods and landslides.…”

Section: Introductionmentioning

confidence: 99%

A New Approach to the Fine Kinney Method with AHP Based ELECTRE I and Math Model on Risk Assessment for Natural Disasters

Derse¹

2021

JGEOG

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Natural disasters impose enormous risks on human living and the environment. Researchers have given more attention to evaluating these risks in the context of disasters. Studies focus on the risk assessment of only one of the natural disasters for the regions. However, a risk assessment should be conducted that includes all-natural disasters for these regions. This risk assessment is dealt with by 8 different provinces in the Aegean region of Turkey in the work. A case data (1990-2020) on natural disasters such as earthquake, fire, landslide, flood, storm/typhoon has been considered for the cities covered. By revising the Fine Kinney risk assessment method for natural disasters, a risk score is obtained for each province. Then, the AHP based ELECTRE I method is applied to these provinces. As a result of this method, the riskiest region is obtained. The location of a crisis center in the riskiest region obtained should ensure effective solutions to the regions which are affected by the results of the disasters. Thus, the problem of choosing the most suitable location in the crisis center is handled with a goal programming approach.

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Section: Introductionmentioning

confidence: 99%

A New Approach to the Fine Kinney Method with AHP Based ELECTRE I and Math Model on Risk Assessment for Natural Disasters

Derse¹

2021

JGEOG

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“…regions [1], [2]. Snow avalanche, as a natural disaster severely affects socio-economic and geomorphic processes through damaging ecosystems, vegetation, landscape, infrastructures, transportation networks, and human life [3], [4]. The recent frequency, irregularity, and uncertainty in the occurrence regime of avalanche had been strongly linked to climate change and global warming [5]- [8].…”

Section: Introductionmentioning

confidence: 99%

Towards an Ensemble Machine Learning Model of Random Subspace Based Functional Tree Classifier for Snow Avalanche Susceptibility Mapping

et al. 2020

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Snow avalanche as a natural disaster severely affects socio-economic and geomorphic processes through damaging ecosystems, vegetation, landscape, infrastructures, transportation networks, and human life. Modeling the snow avalanche has been seen as an essential approach for understanding the mountainous landscape dynamics to assess hazard susceptibility leading to effective mitigation and resilience. Therefore, the main aim of this study is to introduce and implement an ensemble machine learning model of random subspace (RS) based on a classifier, functional tree (FT), named RSFT model for snow avalanche susceptibility mapping at Karaj Watershed, Iran. According to the best knowledge of literature, the proposed model, RSFT, has not earlier been introduced and implemented for snow avalanche modeling and mapping over the world. Four benchmark models, including logistic regression (LR), logistic model tree (LMT), alternating decision tree (ADT), and functional trees (FT) models were used to check the goodnessof-fit and prediction accuracy of the proposed model. To achieve this objective, the most important factors among many climatic, topographic, lithologic, and hydrologic factors, which affect the snow accumulation and snow avalanche occurrence, were determined by the information gain ratio (IGR) technique. The goodness-of-fit and prediction accuracy of the models were evaluated by some statistical-based indexes including, sensitivity, specificity, accuracy, kappa, and area under the ROC curve, Friedman and Wilcoxon sign rank tests. Results indicated that the ensemble proposed model (RSFT), had the highest performance (Sensitivity = 94.1%, Specificity = 92.4%, Accuracy = 93.3%, and Kappa = 0.782) rather than the other soft-computing benchmark models. The snow avalanche susceptibility maps indicated that the high and very high susceptibility avalanche areas are located in the north and northeast parts of the study area, which have a higher elevation with more precipitation and lower temperatures. INDEX TERMSSnow avalanche, susceptibility mapping, ensemble approach, feature selection. AMIRHOSEIN MOSAVI graduated from Kingston University London, U.K. He received the Ph.D. degree in applied informatics. He is currently a Data Scientist in climate change, sustainability, and hazard prediction. He is a Senior Research Fellow with Oxford Brookes University. He is also an Alexander von Humboldt Research Fellow in big data, the IoT, and machine learning. He was a recipient of the Green-Talent Award, the UNESCO Young Scientist Award, the ERCIM Alain Bensoussan Fellowship Award, the Campus France Fellowship Award, the Campus Hungary Fellowship Award, and the Endeavour-Australia Leadership.

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“…In particular, climate change is contributing to raising disaster risk [9,10]. Although this link is widely recognized in the literature [11][12][13], historically, the work of disaster risk specialists and climate change specialists has too often been isolated and autonomous [14].…”

Section: Introductionmentioning

confidence: 99%

A Method for the Definition of Local Vulnerability Domains to Climate Change and Relate Mapping. Two Case Studies in Southern Italy

et al. 2020

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Climate change is contributing to raising disaster risk, with variable impacts depending on the local level of vulnerability. This paper describes a method for the definition of local vulnerability domains to climate change. The application of the methodology is aimed at building local vulnerability maps. The set of indicators of climate exposure, sensitivity and adaptive capacity, theoretically identified and practically tested on two case studies in southern Italy, contributes to support the territories in identifying the main local vulnerabilities, as well as define, subsequently, a systematic framework for identifying the most suitable mitigation and adaptation measures to climate change according to the specificities of the contexts of interest. In this regard, we consider the framework of risks and related impacts due to climate change on urban infrastructure proposed by the European Commission in order to face common challenges in the EU territories. Specifically, reference is made to three thematic focuses, which are “transport infrastructures”, “energy infrastructures” and “buildings and construction sector”. Although still at an early stage, the results of the research contribute to identifying useful elements of interrelation with the urban context for guiding resilient planning choices and reducing risks.

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Living with risk of natural disasters

Cited by 5 publications

References 1 publication

A New Approach to the Fine Kinney Method with AHP Based ELECTRE I and Math Model on Risk Assessment for Natural Disasters

A New Approach to the Fine Kinney Method with AHP Based ELECTRE I and Math Model on Risk Assessment for Natural Disasters

Towards an Ensemble Machine Learning Model of Random Subspace Based Functional Tree Classifier for Snow Avalanche Susceptibility Mapping

A Method for the Definition of Local Vulnerability Domains to Climate Change and Relate Mapping. Two Case Studies in Southern Italy

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