Is multi-hazard mapping effective in assessing natural hazards and integrated watershed management?

Pourghasemi, Hamid Reza; Gayen, Amiya; Edalat, Mohsen; Zarafshar, Mehrdad; Tiefenbacher, John P.

doi:10.1016/j.gsf.2019.10.008

Cited by 87 publications

(47 citation statements)

References 112 publications

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“…The rest of the study area, 83.5%, is likely to be impacted by at least one of the hazards, however. Pourghasemi et al 54 mapped both the individual and collective risks posed by three hazards (floods, forest fires, and landslides) in a multi-hazard study using machine learning techniques. Others have conducted multi-hazard risk assessments, but separately for each risk 46,47,161 .…”

Section: Discussionmentioning

confidence: 99%

“…due to the diverse geomorphological and climatic zones 28 , 12 , 48 – 50 . Snow avalanches, landslides, subsidence, wildfires, and floods occur annually in the Zagros and Alborz mountain chains in Iran; people are more numerous in these regions than in other parts of the country 51 – 54 . In this study, five major natural events (flood, landslides, land subsidence, snow avalanches, and wildfires) in Chaharmahal and Bakhtiari Province provide the basis for a multi-hazard risk assessment map.…”

Section: Introductionmentioning

confidence: 99%

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A machine learning framework for multi-hazards modeling and mapping in a mountainous area

Yousefi

Pourghasemi

Emami

et al. 2020

Sci Rep

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this study sought to produce an accurate multi-hazard risk map for a mountainous region of iran. the study area is in southwestern iran. the region has experienced numerous extreme natural events in recent decades. This study models the probabilities of snow avalanches, landslides, wildfires, land subsidence, and floods using machine learning models that include support vector machine (SVM), boosted regression tree (BRT), and generalized linear model (GLM). Climatic, topographic, geological, social, and morphological factors were the main input variables used. the data were obtained from several sources. The accuracies of GLM, SVM, and functional discriminant analysis (FDA) models indicate that SVM is the most accurate for predicting landslides, land subsidence, and flood hazards in the study area. GLM is the best algorithm for wildfire mapping, and FDA is the most accurate model for predicting snow avalanche risk. The values of AUC (area under curve) for all five hazards using the best models are greater than 0.8, demonstrating that the model's predictive abilities are acceptable. A machine learning approach can prove to be very useful tool for hazard management and disaster mitigation, particularly for multi-hazard modeling. the predictive maps produce valuable baselines for risk management in the study area, providing evidence to manage future human interaction with hazards. Human interactions with natural extreme events, or hazards, are increasing globally 1. Natural disasters have affected people and natural environments generating vast economic losses around the world. However, in some developed counties disasters have been decreasing since 1900 2,3. Hazard is the probability of occurrence in a specified period and within a given area of a potentially damaging of a given magnitude 4,5. The definition incorporates the concepts of location (where?), time (when, or how frequently?) and magnitude (how large?). Total risk (R) means the expected number of lives lost, person injured, damage to property, or disruption of economic activity due to a particular natural phenomenon, and is therefore the product of specific risk (RS) and elements at risk (E) 6. In addition, RS is the expected degree of loss due to a natural phenomenon. Landscapes around the world are reflections of diverse natural processes. The probabilities of extreme natural events are typically greater in more natural areas and are, in fact, extensions of natural systems. Exposure of people to these extreme natural processes could be reduced and limited if predictive models based on new approaches and deeper knowledge of effective factors were employed 7. Mountainous areas are commonly sites of snow avalanches 8,9 , landslides 4,10 , floods 11,12 , mudflows 13 , ice avalanches 14 , soil erosion 15-17 , rock falls 18 , and wildfires 19-24. Most studies focus on a single hazard, even when there are multiple hazardous processes affecting the same landscapes 8,25-30. However, hazards sometimes interact with each other. Sometimes, the mitigation of one

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A machine learning framework for multi-hazards modeling and mapping in a mountainous area

Yousefi

Pourghasemi

Emami

et al. 2020

Sci Rep

Self Cite

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“…Receiver Operating Characteristics (ROC) curve is used for the verification and validation of the model ( Pourghasemi, Gayen, Edalat, Zarafshar, & Tiefenbacher, 2020 ; Pourghasemi, Pouyan, et al, 2020 ). It juxtaposes the validation dataset with the model output ( Gayen, Pourghasemi, Saha, Keesstra, & Bai, 2019 ).…”

Section: Database and Methodologymentioning

confidence: 99%

COVID-19 and urban vulnerability in India

Mishra

Gayen

Haque

2020

Habitat International

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139

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The global pandemic has an inherently urban character. The UN-Habitat's publication of a Response Plan for mollification of the SARS-CoV-2 based externalities in the cities of the world testifies to that. This article takes the UN-Habitat report as the premise to carry out an empirical investigation in the four major metro cities of India. The report's concern with the urban character of the pandemic has underlined the role of cities in disease transmission. In that wake, the study demarcates factors at the sub-city level that tend to jeopardize the two mandatory precautionary measures during COVID-19-Social Distancing and Lockdown. It investigates those factors through a Covid Vulnerability Index. The Index devised with the help of Analytic Hierarchy Process demarcates the low, moderate, high, and very high vulnerable city sub-units. Secondly, UN-Habitat's one of the major action areas is evidence-based knowledge creation through mapping and its analysis. In our study, we do it at a granular scale for arriving at a more nuanced understanding. Thus, in harmony with the UN-habitat's we take the urban seriously and identify the gaps that need to be plugged for the pandemic cities of now and of the future.

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“…Multivariate adaptive regression splines (MARS). MARS is one of the best regression-based algorithms 13,71 . Its predictions can be made based upon both linear and non-linear relationships between independent factors 72 .…”

Section: Random Forest (Rf) Rf Is a Nonparametric Technique Based Onmentioning

confidence: 99%

Assessing the susceptibility of schools to flood events in Iran

Yousefi

Pourghasemi

Emami

et al. 2020

Sci Rep

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Catastrophic floods cause deaths, injuries, and property damages in communities around the world. The losses can be worse among those who are more vulnerable to exposure and this can be enhanced by communities’ vulnerabilities. People in undeveloped and developing countries, like Iran, are more vulnerable and may be more exposed to flood hazards. In this study we investigate the vulnerabilities of 1622 schools to flood hazard in Chaharmahal and Bakhtiari Province, Iran. We used four machine learning models to produce flood susceptibility maps. The analytic hierarchy process method was enhanced with distance from schools to create a school-focused flood-risk map. The results indicate that 492 rural schools and 147 urban schools are in very high-risk locations. Furthermore, 54% of rural students and 8% of urban students study schools in locations of very high flood risk. The situation should be examined very closely and mitigating actions are urgently needed.

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Is multi-hazard mapping effective in assessing natural hazards and integrated watershed management?

Cited by 87 publications

References 112 publications

A machine learning framework for multi-hazards modeling and mapping in a mountainous area

A machine learning framework for multi-hazards modeling and mapping in a mountainous area

COVID-19 and urban vulnerability in India

Assessing the susceptibility of schools to flood events in Iran

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