Metaheuristic-driven enhancement of categorical boosting algorithm for flood-prone areas mapping

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2025, Volume and Issue: 136, P. 104357 - 104357

Published: Jan. 14, 2025

Language: Английский

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Enhancing flood susceptibility mapping in Meghna River basin by introducing ensemble Naive Bayes with stacking algorithms

Geomatics Natural Hazards and Risk, Journal Year: 2025, Volume and Issue: 16(1)

Published: Feb. 13, 2025

Language: Английский

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Integrating machine learning with the Minimum Cumulative Resistance Model to assess the impact of urban land use on road waterlogging risk

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 132842 - 132842

Published: Feb. 1, 2025

Language: Английский

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Improved flood risk assessment using multi-model ensemble machine-learning techniques in a tropical river basin of Southern India

Physical Geography, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 29

Published: Feb. 22, 2025

Language: Английский

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Accelerated and Interpretable Flood Susceptibility Mapping Through Explainable Deep Learning with Hydrological Prior Knowledge

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(9), P. 1540 - 1540

Published: April 26, 2025

Flooding is one of the most devastating natural disasters worldwide, with increasing frequency due to climate change. Traditional hydrological models require extensive data and computational resources, while machine learning (ML) struggle capture spatial dependencies. To address this, we propose a modified U-Net architecture that integrates prior knowledge permanent water bodies improve flood susceptibility mapping in Northumberland County, UK. By embedding domain-specific insights, our model achieves higher area under curve (AUC) (0.97) compared standard (0.93), also reducing training time by converging three times faster. Additionally, integrate Grad-CAM module provide visualisations explaining areas attention from model, enabling interpretation its decision-making, thus barriers practical implementation.

Language: Английский

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Enhancing urban resilience through machine learning-supported flood risk assessment: integrating flood susceptibility with building function vulnerability

npj Urban Sustainability, Journal Year: 2025, Volume and Issue: 5(1)

Published: May 3, 2025

Language: Английский

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Multi-Hazard Susceptibility Mapping Using Machine Learning Approaches: A Case Study of South Korea

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(10), P. 1660 - 1660

Published: May 8, 2025

The frequency and magnitude of natural hazards have been steadily increasing, largely due to extreme weather events driven by climate change. These pose significant global challenges, underscoring the need for accurate prediction models systematic preparedness. This study aimed predict multiple in South Korea using various machine learning algorithms. area, (100,210 km2), was divided into a grid system with 0.01° resolution. Meteorological, climatic, topographical, remotely sensed data were interpolated each cell analysis. focused on three major hazards: drought, flood, wildfire. Predictive developed two algorithms: Random Forest (RF) Extreme Gradient Boosting (XGB). analysis showed that XGB performed exceptionally well predicting droughts floods, achieving ROC scores 0.9998 0.9999, respectively. For wildfire prediction, RF achieved high score 0.9583. results integrated generate multi-hazard susceptibility map. provides foundational development hazard management response strategies context Furthermore, it offers basis future research exploring interaction effects multi-hazards.

Language: Английский

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A novel approach in comparing the performance of bivariate statistical methods, boosting, and stacking models in flood susceptibility assessment

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 387, P. 125670 - 125670

Published: May 19, 2025

Language: Английский

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Assessing Critical Flood-Prone Districts and Optimal Shelter Zones in the Brahmaputra Valley: Strategies for Effective Flood Risk Management

Physics and Chemistry of the Earth Parts A/B/C, Journal Year: 2024, Volume and Issue: unknown, P. 103772 - 103772

Published: Oct. 1, 2024

Language: Английский

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Urban flood resilience evaluation in China: a systematic review of frameworks, methods, and limitations

Geomatics Natural Hazards and Risk, Journal Year: 2024, Volume and Issue: 16(1)

Published: Dec. 23, 2024

With the acceleration of climate change and urbanization, impacts floods on Chinese cities have become increasingly severe, improving urban flood resilience has an urgent issue. Although scholars in China proposed many methods to evaluate last decade, research this field not been critically reviewed thoroughly detail. Therefore, study selects high-quality original papers from previous decade focuses analyzing location, framework, data, analytical methods, limitations current assessment methods. The study's main objective is inform identification a system applicable context while revealing existing methodology's shortcomings data accuracy quality, consideration regional variability, indicator validity. Finally, finds significant challenges eight areas: talent pool, public participation, investment financing mechanisms. It provides targeted strategies, such as building multi-sectoral synergistic governance mechanism.

Language: Английский

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