Prediction of flood susceptibility in an inter-fluvial region of Northern India using machine learning algorithms DOI Creative Commons
Arijit Ghosh, Azizur Rahman Siddiqui

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

Published: Dec. 1, 2024

Language: Английский

Application of frequency ratio model for flood hazard zonation in the Dikhow River basin, Northeast India DOI

Anannya Panging,

Srinivasa Rao Koduru,

A. Simhachalam

et al.

Natural Hazards, Journal Year: 2025, Volume and Issue: unknown

Published: March 11, 2025

Language: Английский

Citations

0
Improving index-based coastal vulnerability assessment using machine learning in Oman DOI
Malik Al-Wardy, Erfan Zarei, Mohammad Reza Nikoo

et al.

The Science of The Total Environment, Journal Year: 2025, Volume and Issue: 976, P. 179311 - 179311

Published: April 9, 2025

Language: Английский

Citations

0
Future flood susceptibility mapping under climate and land use change DOI Creative Commons

Hamidreza Khodaei,

Farzin Nasiri Saleh,

Afsaneh Nobakht Dalir

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 11, 2025

Language: Английский

Citations

0
Flood risk in mountainous settlements: A new framework based on an interpretable NSGA-II-GB from a point-area duality perspective DOI
Qihang Wu, Zhe Sun,

Zhan Wang

et al.

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 373, P. 123842 - 123842

Published: Jan. 1, 2025

Language: Английский

Citations

0
Multi-Hazard Assessment for Flood and Landslide Risk in Kalimantan and Sumatra: Implications for Nusantara, Indonesia's New Capital DOI Creative Commons
Sujung Heo, Wonmin Sohn, Sang-Jin Park

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(18), P. e37789 - e37789

Published: Sept. 1, 2024

Language: Английский

Citations

3
Geospatial Approach to Pluvial Flood-Risk and Vulnerability Assessment in Sunyani Municipality DOI Creative Commons

Aaron Tettey Tetteh,

Abdul–Wadood Moomen, Lily Lisa Yevugah

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(18), P. e38013 - e38013

Published: Sept. 1, 2024

Language: Английский

Citations

3
Assessing Critical Flood-Prone Districts and Optimal Shelter Zones in the Brahmaputra Valley: Strategies for Effective Flood Risk Management DOI
Jatan Debnath, Dhrubajyoti Sahariah, Gowhar Meraj

et al.

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

Citations

2
Enhancing flood monitoring and prevention using machine learning and IoT integration DOI

Syed Asad Shabbir Bukhari,

Imran Shafi,

Jamil Ahmad

et al.

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

Published: Nov. 4, 2024

Language: Английский

Citations

1
From data to decisions: Leveraging ML for improved river discharge forecasting in Bangladesh DOI Creative Commons
Md. Abu Saleh, H. M. Rasel,

Briti Ray

et al.

Watershed Ecology and the Environment, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 1, 2024

Language: Английский

Citations

0
Spatio-Temporal Analysis of Susceptibility Hazardous and Risk Mapping in Post-2023 Simangulampe Devastating Flood DOI Open Access
Togi Tampubolon, Jeddah Yanti,

Juniar Hutahaean

et al.

Journal of Physics Conference Series, Journal Year: 2024, Volume and Issue: 2908(1), P. 012005 - 012005

Published: Nov. 1, 2024

Abstract Over 169 people along the Simangulampe upstream were under devastating flood and worst landslide watches in December 2023 due to a significant storm bringing heaviest rainfall moving giant boulders. Indeed, there are far fewer studies information on susceptibility hazards Simangalumpe than others. First-rate impressive risk mitigation strategies increased climate-change consideration reduced risk. We adopt C-band synthetic aperture radar multispectral imagery from Sentinel identify, visualize, analyze flash mapping mitigating address this issue. Precisely, is considered surface water indices with various parameters: Normalized Difference Vegetation Index (NDVI), Water (NDWI), Modified NDWI (MNDWI), SAR inundation mapping. Results show low NDVI values- over 50 percent of plant canopies damaged (uprooted broken trees) upstream. Combining properties index shows extent bodies Simagalumpe covers Finally, developing spatial temporal analysis data results flooding reducing unnecessary threats.

Language: Английский

Citations

0