Evaluating the application of K-mean clustering in Earthquake vulnerability mapping of Istanbul, Turkey

International Journal of Disaster Risk Reduction, Journal Year: 2022, Volume and Issue: 79, P. 103154 - 103154

Published: July 5, 2022

Language: Английский

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Review article: Snow and ice avalanches in high mountain Asia – scientific, local and indigenous knowledge

Natural hazards and earth system sciences, Journal Year: 2023, Volume and Issue: 23(7), P. 2569 - 2592

Published: July 20, 2023

Abstract. The cryosphere in high mountain Asia (HMA) not only sustains the livelihoods of people residing downstream through its capacity to store water but also holds potential for hazards. One these hazards, avalanches, so far remains inadequately studied, as complex relationship between climate and triggers is poorly understood due lack long-term observations, inaccessibility, severe weather conditions, financial logistical constraints. In this study, available literature was reviewed covering period from late 20th century June 2022 identify research societal gaps propose future directions mitigation strategies. Beyond scientific literature, technical reports, newspapers, social media other local sources were consulted compile a comprehensive, open-access version-controlled database avalanche events their associated impacts. Over 681 avalanches with more than 3131 human fatalities identified eight countries region. Afghanistan has highest recorded (1057), followed by India (952) Nepal (508). Additionally, 564 lost lives while climbing peaks above 4500 m a.s.l., one-third which staff employed guides or porters. This makes it less deadly hazard populated European Alps, example, considerably larger number affected who did voluntarily expose themselves risk. Although are significant, impacts may be considerable, far, limited holistic adaptation measures exist These generally rely on indigenous knowledge adapted modern technologies. Considering impact have region, we suggest further develop including zonation maps based datasets historic modelling efforts. should, however, happen acknowledging already existing region close coordination communities, government civil society stakeholders. More studies should attempted understand trends drivers

Language: Английский

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Landslide susceptibility zonation using statistical and machine learning approaches in Northern Lecco, Italy

Natural Hazards, Journal Year: 2021, Volume and Issue: 111(1), P. 901 - 937

Published: Nov. 10, 2021

Language: Английский

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Toward the development of deep learning analyses for snow avalanche releases in mountain regions

Geocarto International, Journal Year: 2021, Volume and Issue: 37(25), P. 7855 - 7880

Published: Sept. 27, 2021

Snow avalanches impose a considerable threat to infrastructure and human safety in snow bound mountain areas. Nevertheless, the spatial prediction of has received little research attention many vulnerable parts world, particularly developing countries. The present study investigates applicability stand alone convolutional neural network (CNN) model, as deep learning approach, along with two metaheuristic algorithms including grey wolf optimization (CNN-GWO) imperialist competitive algorithm (CNN-ICA) avalanche modelling Darvan watershed, Iran. analysis was based on thirteen potential drivers occurrence an inventory map previously documented occurrences. efficiency models' performance evaluated by Area Under Receiver Operating Characteristic curve (AUC) Root Mean Square Error (RMSE). CNN-ICA model yielded highest accuracy both training (AUC= 0.982, RMSE = 0.067) validation 0.972, 0.125) steps, followed CNN-GWO (AUC 0.975 for training, 0.18 AUC 0.968 validation, 0.157 validation). However, standalone CNN showed lower goodness-of-fit 0.864, 0.22) predictive 0.811, 0.330). approach utilized this is broadly applicable identifying areas where hazard likely be high mitigation measures or corresponding land use planning should prioritized.

Language: Английский

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Efficiency exploration of frequency ratio, entropy and weights of evidence-information value models in flood vulnerabilityassessment: a study of raiganj subdivision, Eastern India

Stochastic Environmental Research and Risk Assessment, Journal Year: 2021, Volume and Issue: 36(6), P. 1721 - 1742

Published: Oct. 20, 2021

Language: Английский

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GIS-Based Spatial Modeling of Snow Avalanches Using Analytic Hierarchy Process. A Case Study of the Šar Mountains, Serbia

Atmosphere, Journal Year: 2022, Volume and Issue: 13(8), P. 1229 - 1229

Published: Aug. 3, 2022

Snow avalanches are one of the most devastating natural hazards in highlands that often cause human casualties and economic losses. The complex process modeling terrain susceptibility requires application modern methods software. prediction this study is based on use geographic information systems (GIS), remote sensing, multicriteria analysis—analytic hierarchy (AHP) territory Šar Mountains (Serbia). Five indicators (lithological, geomorphological, hydrological, vegetation, climatic) were processed, where 14 criteria analyzed. results showed approximately 20% investigated area highly susceptible to 24% has a medium susceptibility. Based results, settlements avalanche protection measures should be applied have been singled out. obtained data can will help local self-governments, emergency management services, mountaineering services mitigate material losses from snow avalanches. This first research Republic Serbia deals with GIS-AHP spatial avalanches, methodology used tested other high mountainous regions.

Language: Английский

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Multi-hazard susceptibility mapping for disaster risk reduction in Kargil-Ladakh Region of Trans-Himalayan India

Environmental Earth Sciences, Journal Year: 2023, Volume and Issue: 82(2)

Published: Jan. 1, 2023

Language: Английский

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Snow avalanche susceptibility mapping from tree-based machine learning approaches in ungauged or poorly-gauged regions

CATENA, Journal Year: 2023, Volume and Issue: 224, P. 106997 - 106997

Published: Feb. 14, 2023

Language: Английский

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Spatial modeling of snow avalanche susceptibility using hybrid and ensemble machine learning techniques

CATENA, Journal Year: 2021, Volume and Issue: 206, P. 105524 - 105524

Published: June 17, 2021

Language: Английский

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An integrated approach of artificial intelligence and geoinformation techniques applied to forest fire risk modeling in Gachsaran, Iran

Journal of Environmental Planning and Management, Journal Year: 2022, Volume and Issue: 66(6), P. 1369 - 1391

Published: Feb. 14, 2022

Forest fires are a multidimensional phenomenon that affects many parts of the world, including Zagros region Iran. They often caused by various factors can have natural-, anthropogenic-, or combined origins. Considering significant environmental and socio-economic impacts forest fires, it is essential to take necessary measures identify areas prone develop plans policies for crisis management risk mitigation accordingly. In this study, we applied an integrated geoinformation (remote sensing GIScience) approach analyze map fire in Gachsaran, Iran, which highly fires. For mapping (FFRM), employed GIS-based multi-criteria decision analysis method combination with fuzzy analytical network process (ANP) methods high risk. To distinguish vulnerable sites, 13 independent variables encompassing geomorphological factors, land surface characteristics, climatological anthropological factors. initial criteria maps, determined weights using ANP used technique standardization. Finally, was produced multi-layer perceptron artificial neural network. Our results were also validated against historical data operating characteristics. showed 18.417% province subject very These should be prioritized when designing precautionary protective measures. Among examined temperature, soil moisture, distance from sites received highest scores ANP. The study areas, appropriate planning deal risk, make informed decisions regarding allocation facilities high-risk areas.

Language: Английский

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