Improving flood hazard susceptibility assessment by integrating hydrodynamic modeling with remote sensing and ensemble machine learning

Natural Hazards, Год журнала: 2025, Номер unknown

Опубликована: Янв. 11, 2025

Abstract Floods are natural disasters with significant economic and infrastructural impacts. Assessing flood susceptibility in mountainous urban regions is particularly challenging due to the complicated interaction which structures terrain affect behavior. This study employs two ensemble machine learning algorithms, Extreme Gradient Boosting (XGBoost) Random Forest (RF), develop maps for Hunza-Nagar region, has been experiencing frequent flooding past three decades. An unsteady flow simulation carried out HEC-RAS utilizing a 100-year return period hydrograph as an input boundary condition, output of provided spatial inundation extents necessary developing inventory. Ten explanatory factors, including climatic, geological, geomorphological features namely elevation, slope, curvature, topographic wetness index (TWI), normalized difference vegetation (NDVI), land use cover (LULC), rainfall, lithology, distance roads rivers considered mapping. For inventory, random sampling technique adopted create repository non-flood points, incorporating ten geo-environmental conditioning factors. The models’ accuracy assessed using area under curve (AUC) receiver operating characteristics (ROC). prediction rate AUC values 0.912 RF 0.893 XGBoost, also demonstrating superior performance accuracy, precision, recall, F1-score, kappa evaluation metrics. Consequently, model selected represent map area. resulting will assist national disaster management infrastructure development authorities identifying high susceptible zones carrying early mitigation actions future floods.

Язык: Английский

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Unveiling global flood hotspots: Optimized machine learning techniques for enhanced flood susceptibility modeling

Journal of Hydrology Regional Studies, Год журнала: 2025, Номер 58, С. 102285 - 102285

Опубликована: Март 4, 2025

Язык: Английский

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Flash flood susceptibility modeling using optimized deep learning method in the Uttarakhand Himalayas

Earth Science Informatics, Год журнала: 2024, Номер 18(1)

Опубликована: Дек. 11, 2024

Язык: Английский

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Disaster-Based Geographical Region Analysis Using Climate Change Detection Using Deep Learning Algorithm

Remote Sensing in Earth Systems Sciences, Год журнала: 2025, Номер unknown

Опубликована: Апрель 17, 2025

Язык: Английский

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

Remote Sensing, Год журнала: 2025, Номер 17(9), С. 1540 - 1540

Опубликована: Апрель 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.

Язык: Английский

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Advanced Forecasting of Drought Zones in Canada Using Deep Learning and CMIP6 Projections

Climate, Год журнала: 2024, Номер 12(8), С. 119 - 119

Опубликована: Авг. 10, 2024

This study addresses the critical issue of drought zoning in Canada using advanced deep learning techniques. Drought, exacerbated by climate change, significantly affects ecosystems, agriculture, and water resources. Canadian Drought Monitor (CDM) data provided government ERA5-Land daily were utilized to generate a comprehensive time series mean monthly precipitation air temperature for 199 sample locations from 1979 2023. These processed Google Earth Engine (GEE) environment used develop Convolutional Neural Network (CNN) model estimate CDM values, thereby filling gaps historical data. The CanESM5 model, as assessed IPCC Sixth Assessment Report, was employed under four change scenarios predict future conditions. Our CNN forecasts values up 2100, enabling accurate zoning. results reveal significant trends changes, indicating areas most vulnerable droughts, while shows slow increasing trend. analysis indicates that extreme scenarios, certain regions may experience increase frequency severity necessitating proactive planning mitigation strategies. findings are policymakers stakeholders designing effective management adaptation programs.

Язык: Английский

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Flood bend flow prediction in intermittent river reach using a 2D hydraulic model and stacking-ensemble-based LSTM technique

Earth Science Informatics, Год журнала: 2024, Номер 18(1)

Опубликована: Дек. 20, 2024

Язык: Английский

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