Methodological approach for mapping the flood physical vulnerability index with geographical open-source data: an example in a small-middle city (Ponferrada, Spain) DOI Creative Commons

Laura Tascón-González,

Montserrat Ferrer‐Julià, Eduardo García‐Meléndez

et al.

Natural Hazards, Journal Year: 2024, Volume and Issue: 120(5), P. 4053 - 4081

Published: Jan. 4, 2024

Abstract To increase the resilience of communities against floods, it is necessary to develop methodologies estimate vulnerability. The concept vulnerability multidimensional, but most flood studies have focused only on social approach. Nevertheless, in recent years, following seismic analysis, physical point view has increased its relevance. Therefore, present study proposes a methodology map and applies using an index at urban parcel scale for medium-sized town (Ponferrada, Spain). This based multiple indicators fed by geographical open-source data, once they been normalized combined with different weights extracted from Analytic Hierarchic Process. results show raster that facilitates future emergency risk management diminish potential damages. A total 22.7% parcels studied value higher than 0.4, which considered highly vulnerable. location these would passed unnoticed without use open governmental datasets, when average calculated overall municipality. Moreover, building percentage covered water was influential indicator area, where simulated generated alleged dam break. exceeds spatial constraints collecting this type data direct interviews inhabitants allows working larger areas, identifying buildings infrastructure differences among parcels.

Language: Английский

Climate change impacts on magnitude and frequency of urban floods under scenario and model uncertainties DOI
Luyao Wang, Zhenyu Huang, Bin Gan

et al.

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 366, P. 121679 - 121679

Published: July 11, 2024

Language: Английский

Citations

5
Segmentation and Visualization of Flooded Areas Through Sentinel-1 Images and U-Net DOI Creative Commons
Fernando Pech May, Raúl Aquino-Santos, Omar Álvarez Cárdenas

et al.

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Journal Year: 2024, Volume and Issue: 17, P. 8996 - 9008

Published: Jan. 1, 2024

Floods are the most common phenomenon and cause significant economic social damage to population.They becoming more frequent dangerous.Consequently, it is necessary create strategies intervene effectively in mitigation resilience of affected areas.Different methods techniques have been developed mitigate caused by this phenomenon.Satellite programs provide a large amount data on Earth's surface, geospatial information processing tools help manage different natural disasters.Likewise, deep learning an approach capable forecasting time series that can be applied satellite images for flood prediction mapping.This paper presents segmentation visualization using U-Net architecture Sentinel-1 SAR imagery.The capture relevant features images.The comprises various phases, from loading preprocessing inference visualization.For study, georeferenced dataset Sen1Floods11 used train validate model through epochs training.A study area southeastern Mexico floods was chosen.The results demonstrate achieves high accuracy detecting flooded areas, with promising metrics regarding loss, precision, F1-score.

Language: Английский

Citations

4
Flooding: Contributing factors to residential flood damage in Canada DOI Creative Commons
Bernard Deschamps, Mathieu Boudreault, Philippe Gachon

et al.

International Journal of Disaster Risk Reduction, Journal Year: 2025, Volume and Issue: unknown, P. 105348 - 105348

Published: March 1, 2025

Language: Английский

Citations

0
Assessing Flood and Waterlogging Vulnerability and Community Governance in Urban Villages in the Context of Climate Change: A Case Study of 89 Urban Villages in Shanghai DOI
Shijun Chen, Jianing Lin, Tuolei Wu

et al.

Sustainable Cities and Society, Journal Year: 2025, Volume and Issue: unknown, P. 106377 - 106377

Published: April 1, 2025

Language: Английский

Citations

0
Enhancing urban resilience through machine learning-supported flood risk assessment: integrating flood susceptibility with building function vulnerability DOI Creative Commons
Xiaoling Qin, Shifu Wang, Meng Meng

et al.

npj Urban Sustainability, Journal Year: 2025, Volume and Issue: 5(1)

Published: May 3, 2025

Language: Английский

Citations

0
Integrating satellite and street-level images for local climate zone mapping DOI Creative Commons
Rui Cao, Cai Liao, Qing Li

et al.

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2023, Volume and Issue: 119, P. 103323 - 103323

Published: May 1, 2023

Timely and accurate local climate zone (LCZ) classification maps are valuable for urban studies. The integration of remote sensing street-level images is promising to produce high-quality LCZ maps, since the former can efficiently capture information landscapes on a large-scale while latter include ground-level details. However, due their significant differences in spatial distributions views, as well existing sampling issues images, how fuse them effectively challenging remains an uncharted research area. To address these fill gap, this study proposes effective method integrate satellite mapping. Additionally, simple yet image proposed. Extensive experiments have been performed results demonstrate effectiveness proposed data fusion also confirm usefulness fusing with enhancing performance Moreover, increase representativeness avoid redundancy, thus significantly reducing number required retaining high accuracy. best our knowledge, first attempt cross-view methods contribute development multi-source map production further benefit climatic research.

Language: Английский

Citations

10
Disaster loss calculation method of urban flood bimodal data fusion based on remote sensing and text DOI Creative Commons
Xiazhong Zheng, Chenfei Duan, Yun Chen

et al.

Journal of Hydrology Regional Studies, Journal Year: 2023, Volume and Issue: 47, P. 101410 - 101410

Published: May 8, 2023

Major urban areas in Henan Province of central China. data fusion technology is also a key and difficult point the field flood research. Remote sensing text have different modalities scales, making difficult. This study proposed remote bimodal model based on UFCLI, we validated spatiotemporal distribution floods calculation results disaster losses. The show that through coupling analysis data, rainstorm events can be fully reproduced space time. UFCLI effectively improves accuracy single-data inversion for loss 121.98 billion yuan, improvement result R² increased by 0.08 MAPE decreased 0.88. In case sudden storm flooding with complex spatial temporal evolution, traditional hydrological-hydraulic has many pending parameters, which makes it to accurately calculate large-scale By establishing theoretical fusion, use complementary information using solve differences scales existing between data. timeliness damage estimation further improved. Not applicable.

Language: Английский

Citations

10
Urban Infrastructure Vulnerability to Climate-Induced Risks: A Probabilistic Modeling Approach Using Remote Sensing as a Tool in Urban Planning DOI Creative Commons
Ignacio Rodríguez Antuñano, Brais Barros, J. Martínez-Sánchez

et al.

Infrastructures, Journal Year: 2024, Volume and Issue: 9(7), P. 107 - 107

Published: July 4, 2024

In our contemporary cities, infrastructures face a diverse range of risks, including those caused by climatic events. The availability monitoring technologies such as remote sensing has opened up new possibilities to address or mitigate these risks. Satellite images allow the analysis terrain over time, fostering probabilistic models support adoption data-driven urban planning. This study focuses on exploration various satellite data sources, nighttime land surface temperature (LST) from Landsat-8, well ground motion derived techniques MT-InSAR, Sentinel-1, and proximity infrastructure water. Using information Local Climate Zones (LCZs) current use each building in area, economic implications any changes features soil are evaluated. Through construction Bayesian Network model, synthetic datasets generated identify areas quantify risk Barcelona. results this model were also compared with Multiple Linear Regression concluding that provides crucial for managers. It enables adopting proactive measures reduce negative impacts reducing eliminating possible disparities.

Language: Английский

Citations

3
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
A Pioneering DelugeNet Model with Optimization for Enhanced Urban Flood Detection and Analysis DOI

G. Vasumathi,

R. Bha vani

Water Resources Management, Journal Year: 2025, Volume and Issue: unknown

Published: March 15, 2025

Language: Английский

Citations

0