Advanced CMD predictor screening approach coupled with cellular automata-artificial neural network algorithm for efficient land use-land cover change prediction

Journal of Cleaner Production, Год журнала: 2024, Номер 449, С. 141822 - 141822

Опубликована: Март 19, 2024

Язык: Английский

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Climate change, hailstorm incidence, and livelihood security: a perspective from Kashmir valley India

Natural Hazards, Год журнала: 2023, Номер 120(3), С. 2803 - 2827

Опубликована: Ноя. 28, 2023

Язык: Английский

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Disaster risk assessment of educational infrastructure in mountain geographies using PROMETHEE-II

International Journal of Disaster Risk Reduction, Год журнала: 2024, Номер 107, С. 104489 - 104489

Опубликована: Апрель 21, 2024

Язык: Английский

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Evaluation of disaster preparedness in educational infrastructure of remote geographies using Analytical Hierarchy process

International Journal of Disaster Risk Reduction, Год журнала: 2024, Номер 108, С. 104514 - 104514

Опубликована: Май 7, 2024

Язык: Английский

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Flood susceptibility assessment of Jhelum River Basin: A comparative study of TOPSIS, VIKOR and EDAS methods

Geosystems and Geoenvironment, Год журнала: 2024, Номер 3(4), С. 100304 - 100304

Опубликована: Июль 6, 2024

Floods are frequent natural hazards that cause widespread destruction, particularly in low-elevated areas. This study focuses on identifying flood susceptible zones the Kashmir Valley, known for historical flooding attributed to overflow of Jhelum River. Various Multi-Criteria Decision Making (MCDM) techniques, including Technique Order Preference by Similarity Ideal Solution (TOPSIS), Vise Kriterijumska Optimizacijai Compromission Resenje (VIKOR), and Evaluation Based Distance from Average (EDAS), were employed this research. A total 17 multidimensional factors considered, multicollinearity tests revealed no correlation among these factors. The results MCDM models indicate areas along River classified under very high zone. Specifically, Srinagar city is consistently zone all three models. Approximately 4.27 %, 9.67 5.39 % area identified as TOPSIS, VIKOR, EDAS, respectively. exhibited robust performance, evidenced Area Under Curve Receiver Operating Characteristics curve (AUC-ROC). Notably, VIKOR demonstrated excellent performance generating maps. favorable outcomes underscore their potential application similar regions facing comparable challenges. carries significant implications policymakers, administrators, local authorities involved management within Valley. insights provided can inform proactive measures strategies mitigate impact floods enhance overall resilience region.

Язык: Английский

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Disaster-development interface and its impact on emerging vulnerability scenario in Ladakh region of northwestern Himalayas

Journal of Environmental Studies and Sciences, Год журнала: 2023, Номер 13(2), С. 253 - 270

Опубликована: Фев. 8, 2023

Язык: Английский

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Machine learning and GIS-based multi-hazard risk modeling for Uttarakhand: Integrating seismic, landslide, and flood susceptibility with socioeconomic vulnerability

Environmental and Sustainability Indicators, Год журнала: 2025, Номер unknown, С. 100664 - 100664

Опубликована: Март 1, 2025

Язык: Английский

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Landslide Susceptibility Mapping in Complex Topo‐Climatic Himalayan Terrain, India Using Machine Learning Models: A Comparative Study of XGBoost, RF and ANN

Geological Journal, Год журнала: 2025, Номер unknown

Опубликована: Март 20, 2025

ABSTRACT Landslides present a significant danger to both infrastructure and human lives in the challenging terrain of Himalayas. Therefore, it is crucial accurately map areas prone landslides facilitate informed decision‐making proactive planning, allowing for effective management this hazard. Since landslide occurrences are accentuated by floods through toe erosion, wildfires research aims integrate machine learning techniques with analysis multiple hazards, such as forest fires, novel conditioning factors create comprehensive susceptibility. Geospatial was conducted examine relationship between 19 elements, including related flood fire susceptibility, which contribute occurrence landslides. This study tested efficacy three models mapping landslide‐prone areas: eXtreme Gradient Boost (XGBoost), Random Forest (RF) Artificial Neural Network (ANN). These can identify complex correlations patterns among resulting more accurate regions A regression performed evaluate multicollinearity confirm association dependent independent variables. The revealed variance inflation factor within acceptable bounds, providing validation correlation. ROC–AUC curve approach used assess models' accuracy. Among tested, XGB exhibited highest accuracy at 94%, followed RF 92% ANN 77%. results offer insightful information about how combine data from various hazard forecast work be instrumental local authorities disaster organisations prioritising resources, implementing mitigation plans enhancing resilience against threats.

Язык: Английский

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Integrating the Combined Impact of Rainfall and Earthquakes on Landslide Susceptibility in Bhagirathi Valley of the Indian Himalayas Using Machine Learning and GIS

Lecture notes in civil engineering, Год журнала: 2025, Номер unknown, С. 363 - 372

Опубликована: Янв. 1, 2025

Язык: Английский

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Comparative livelihood vulnerability assessment of villages to climate change in high-altitude cold desert, Ladakh, India

Environment Development and Sustainability, Год журнала: 2023, Номер unknown

Опубликована: Дек. 2, 2023

Язык: Английский

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