Toward Explainable Flood Risk Prediction: Integrating A Novel Hybrid Machine Learning Model

Sustainable Cities and Society, Год журнала: 2025, Номер unknown, С. 106140 - 106140

Опубликована: Янв. 1, 2025

Язык: Английский

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Understanding Rainfall Distribution Characteristics over the Vietnamese Mekong Delta: A Comparison between Coastal and Inland Localities

Atmosphere, Год журнала: 2024, Номер 15(2), С. 217 - 217

Опубликована: Фев. 10, 2024

This study examines the changing rainfall patterns in Vietnamese Mekong Delta (VMD) utilizing observational data spanning from 1978 to 2022. We employ Mann–Kendall test, sequential and innovative trend analysis investigate trends annual, wet, dry season rainfall, as well daily events. Our results show significant spatial variations. Ca Mau, a coastal province, consistently showed higher mean annual seasonal compared further inland stations of Can Tho Moc Hoa. Interestingly, Mau experienced notable decrease rainfall. Conversely, Tho, an overall some months wet increase Furthermore, Hoa number rainy days, especially during season. Principal component (PCA) revealed strong correlations between extreme weather events, particularly for emphasizing complex interplay geographic climatic factors within region. findings offer insights policymakers planners, thus aiding development targeted interventions manage water resources prepare climate conditions.

Язык: Английский

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GIS-based assessment of soil erosion and sediment yield using the revised universal soil loss equation (RUSLE) model in the Murredu Watershed, Telangana, India

HydroResearch, Год журнала: 2024, Номер 7, С. 315 - 325

Опубликована: Янв. 1, 2024

The current investigation was conducted in the Murredu watershed, situated India. essential datasets, such as digital elevation model (DEM), soil, land use cover (LULC), and rainfall parameters, were processed analysed using a Geographic Information System (GIS) environment. research utilised revised universal soil loss equation (RUSLE) analysis to assess mean watershed. annual calculated be 14.06 t/ha/year, which is high erosion risk. RUSLE results indicate good outcome with an accuracy of 72.8%. Furthermore, area revealed that sub-watersheds (SW) 2 SW 14 had maximum minimum loss, respectively. SDR for known Murredu, 0.227. watershed outlet received sediment transfer rate 3.19 t/ha/year. Through investigation, it determined average yield, while 11 minimum. This provides valuable insights stakeholders, decision-makers, policymakers regarding sustainable ways managing watersheds.

Язык: Английский

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Evaluation of urban flood susceptibility through integrated Bivariate statistics and Geospatial technology

Environmental Monitoring and Assessment, Год журнала: 2024, Номер 196(6)

Опубликована: Май 9, 2024

Язык: Английский

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Flood risk decomposed: optimized machine learning hazard mapping and multi-criteria vulnerability analysis in the city of Zaio, Morocco.

Journal of African Earth Sciences, Год журнала: 2024, Номер unknown, С. 105431 - 105431

Опубликована: Сен. 1, 2024

Язык: Английский

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A Systematic Review of Urban Flood Susceptibility Mapping: Remote Sensing, Machine Learning, and Other Modeling Approaches

Remote Sensing, Год журнала: 2025, Номер 17(3), С. 524 - 524

Опубликована: Фев. 3, 2025

Climate change has led to an increase in global temperature and frequent intense precipitation, resulting a rise severe urban flooding worldwide. This growing threat is exacerbated by rapid urbanization, impervious surface expansion, overwhelmed drainage systems, particularly regions. As becomes more catastrophic causes significant environmental property damage, there urgent need understand address flood susceptibility mitigate future damage. review aims evaluate remote sensing datasets key parameters influencing provide comprehensive overview of the causative factors utilized mapping. also highlights evolution traditional, data-driven, big data, GISs (geographic information systems), machine learning approaches discusses advantages limitations different mapping approaches. By evaluating challenges associated with current practices, this paper offers insights into directions for improving management strategies. Understanding identifying foundation developing effective resilient practices will be beneficial mitigating

Язык: Английский

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Identification of urban waterlogging risk zones using Analytical Hierarchy Process (AHP): a case of Agartala city

Environmental Monitoring and Assessment, Год журнала: 2025, Номер 197(3)

Опубликована: Фев. 24, 2025

Язык: Английский

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Flood Risk Assessment of the Mand River Basin, Chhattisgarh, Using GIS-Integrated Multi-criteria Decision Analysis

Environmental science and engineering, Год журнала: 2025, Номер unknown, С. 429 - 451

Опубликована: Янв. 1, 2025

Язык: Английский

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Innovative drought monitoring: development and application of the multi-regional aggregated standardized drought index (MRASDI)

Earth Science Informatics, Год журнала: 2025, Номер 18(2)

Опубликована: Апрель 8, 2025

Язык: Английский

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A Revolutionary Hybridised MCDM Approach on Geographic Information System for Evaluation of Flood Risk in Subarnarekha River Basin, India

Geological Journal, Год журнала: 2025, Номер unknown

Опубликована: Апрель 10, 2025

ABSTRACT Determining and characterising locations vulnerable to flooding can help in reducing damage the number of fatalities. During monsoon season, East India's Subarnarekha River frequently floods a significant degree. In current work, we suggest unique hybrid strategy for preparing entire catchment's Flood Susceptibility Mapping (FSM). The study area's FSM was conducted by considering 10 flood conditioning factors utilising Best‐Worst Method (BWM) multi‐parametric Analytical Hierarchy Process (AHP) as per expert knowledge. Meanwhile, proposed incorporates Decision Making Trial Evaluation Laboratory (DEMATEL) examining causal linkages dependencies between different elements affecting process. Several statistical matrices were used compare suggested BWM AHP. Based on our findings, concluded that integration DEMATEL with AHP (ID BWM, ID AHP) more effective than alternative strategies. findings show out factors, slope, elevation, distance from river, drainage density, Topographic wetness Index (TWI), Land Use Cover (LULC), Normalised Difference Vegetation (NDVI), precipitation, soil texture, curvature, have biggest effects local phenomenon are river. For validating efficacy susceptibility map, Area under Receiver Operating Characteristic Curve (AUC‐ROC) adopted demonstrated, showing pretty high accuracy (0.92 or 92% 0.94 94%) respectively. Our research provide highly affordable useful answer problems basin Subarnarekha.

Язык: Английский

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