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

Mapping urban flood susceptibility in Ouagadougou, Burkina Faso DOI
Maïmouna Bologo Traoré, Tazen Fowé, Mathieu Ouédraogo

et al.

Environmental Earth Sciences, Journal Year: 2024, Volume and Issue: 83(19)

Published: Sept. 24, 2024

Citations

1
SAR-driven flood inventory and multi-factor ensemble susceptibility modelling using machine learning frameworks DOI Creative Commons

Krishnagopal Halder,

Anitabha Ghosh,

Amit Kumar Srivastava

et al.

Geomatics Natural Hazards and Risk, Journal Year: 2024, Volume and Issue: 15(1)

Published: Oct. 16, 2024

Climate change has substantially increased both the occurrence and intensity of flood events, particularly in Indian subcontinent, exacerbating threats to human populations economic infrastructure. The present research employed novel ML models—LR, SVM, RF, XGBoost, DNN, Stacking Ensemble—developed Python environment leveraged 18 flood-influencing factors delineate flood-prone areas with precision. A comprehensive inventory, obtained from Sentinel-1 Synthetic Aperture Radar (SAR) data using Google Earth Engine (GEE) platform, provided empirical for entire model training validation. Model performance was assessed precision, recall, F1-score, accuracy, ROC-AUC metrics. results highlighted Ensemble's superior predictive ability (0.965), followed closely by, XGBoost (0.934), DNN (0.929), RF (0.925), LR (0.921), SVM (0.920) respectively, establishing feasibility applications disaster management. maps depicting susceptibility flooding generated by current provide actionable insights decision-makers, city planners, authorities responsible management, guiding infrastructural community resilience enhancements against risks.

Language: Английский

Citations

1
Impact of ENSO on River Flooding in the Karnali River Basin of Nepal DOI Creative Commons
Tirtha Raj Adhikari, Binod Baniya, Qiuhong Tang

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 28, 2024

Abstract El Niño and La Niña, collectively known as the Southern Oscillation (ENSO), play an important role in river flooding. This study investigates relationship between ENSO associated floods Karnali River Basin (KRB). A specific focus is placed on a comprehensive analysis of extreme flood events Niña years, which would deepen understanding influence hydrological hydrodynamic processes. Precipitation discharge data from 1962 to 2020 were obtained Department Hydrology Meteorology (DHM), Government Nepal. The model (HEC-HMS & HEC-RAS) was used simulate 1983, 2000, 2014 2015 at DHM station. year strong year, while 1983 are years. In 2015, annual precipitation 1190 mm average 1130 m3/s. instantaneous peak for event 4560 However, same basin, during years 1413 1283 mm, respectively. 1D 2D HEC-RAS models behaviors analyze channel shift August 2015. At station Chisapani area, observed 3354 m³/s can well capture with simulated 3365 m³/s. further showed that shifted 2,000 m intervals along both branches event. highlights impact flooding

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