A hybrid machine learning modelling for optimization of flood susceptibility mapping in the eastern Mediterranean DOI
Hazem Ghassan Abdo, Sahar Mohammed Richi, Saeed Alqadhi

et al.

Natural Hazards, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 23, 2024

Language: Английский

Toward Explainable Flood Risk Prediction: Integrating A Novel Hybrid Machine Learning Model DOI
Yongyang Wang, Pan Zhang, Yulei Xie

et al.

Sustainable Cities and Society, Journal Year: 2025, Volume and Issue: unknown, P. 106140 - 106140

Published: Jan. 1, 2025

Language: Английский

Citations

2

Understanding Rainfall Distribution Characteristics over the Vietnamese Mekong Delta: A Comparison between Coastal and Inland Localities DOI Creative Commons
Huỳnh Vương Thu Minh, Bui Thi Bich Lien,

Dang Thi Hong Ngoc

et al.

Atmosphere, Journal Year: 2024, Volume and Issue: 15(2), P. 217 - 217

Published: Feb. 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.

Language: Английский

Citations

8

GIS-based assessment of soil erosion and sediment yield using the revised universal soil loss equation (RUSLE) model in the Murredu Watershed, Telangana, India DOI Creative Commons
Padala Raja Shekar, Aneesh Mathew

HydroResearch, Journal Year: 2024, Volume and Issue: 7, P. 315 - 325

Published: Jan. 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.

Language: Английский

Citations

8

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

Kalidhas Muthu,

R. Sivakumar

Environmental Monitoring and Assessment, Journal Year: 2024, Volume and Issue: 196(6)

Published: May 9, 2024

Language: Английский

Citations

5

Flood risk decomposed: optimized machine learning hazard mapping and multi-criteria vulnerability analysis in the city of Zaio, Morocco. DOI
Maelaynayn El baida, Farid Boushaba, Mimoun Chourak

et al.

Journal of African Earth Sciences, Journal Year: 2024, Volume and Issue: unknown, P. 105431 - 105431

Published: Sept. 1, 2024

Language: Английский

Citations

4

A Systematic Review of Urban Flood Susceptibility Mapping: Remote Sensing, Machine Learning, and Other Modeling Approaches DOI Creative Commons
Tania Islam, Ethiopia Bisrat Zeleke,

Mahmud Afroz

et al.

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(3), P. 524 - 524

Published: Feb. 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

Language: Английский

Citations

0

Identification of urban waterlogging risk zones using Analytical Hierarchy Process (AHP): a case of Agartala city DOI
Bulti Das, Tuhin Kanti Ray, Eshita Boral

et al.

Environmental Monitoring and Assessment, Journal Year: 2025, Volume and Issue: 197(3)

Published: Feb. 24, 2025

Language: Английский

Citations

0

Flood Risk Assessment of the Mand River Basin, Chhattisgarh, Using GIS-Integrated Multi-criteria Decision Analysis DOI
Pooja Patel, Rohan Kar, Arindam Sarkar

et al.

Environmental science and engineering, Journal Year: 2025, Volume and Issue: unknown, P. 429 - 451

Published: Jan. 1, 2025

Language: Английский

Citations

0

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

Asad Ellahi,

Ibrahim Nafisah,

Mohammed M. A. Almazah

et al.

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(2)

Published: April 8, 2025

Language: Английский

Citations

0

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

Sipra Mophapatra,

Dillip K. Ghose, Deba Prakash Satapathy

et al.

Geological Journal, Journal Year: 2025, Volume and Issue: unknown

Published: April 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.

Language: Английский

Citations

0