Sediment production susceptibility index in urban area: a case study of Campo Grande – MS, Brazil DOI Creative Commons
Rafael Brandão Ferreira de Moraes, Cláudia Gonçalves Vianna Bacchi, Fábio Veríssimo Gonçalves

et al.

RBRH, Journal Year: 2024, Volume and Issue: 29

Published: Jan. 1, 2024

ABSTRACT Inadequate urban planning has contributed to the sediment production in Urban Hydrographic Micro-basins (UHMs). The present study aims develop and apply Sediment Production Susceptibility Index (SPSI) UHMs from Campo Grande – Mato Grosso do Sul (MS), Brazil, based on Analysis Hierarchical Process (AHP) Geographic Information System (GIS) aggregation. indicators selected for composition of SPSI are Soil Class (49%), Average Slope (22%), Vegetation Cover (13%), Unpaved Streets (16%). It is essentially jointly analyze both spheres (natural anthropogenic) obtain greater reliability studies related sedimentation areas. undergoing urbanization more susceptible than that already densely occupied. can assist public managers environmental adoption preventive measures against silting water bodies obstruction drainage systems.

Language: Английский

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

2
Assessment of urban flood susceptibility based on a novel integrated machine learning method DOI
Haidong Yang, Ting Zou, Biyu Liu

et al.

Environmental Monitoring and Assessment, Journal Year: 2024, Volume and Issue: 197(1)

Published: Dec. 5, 2024

Language: Английский

Citations

1
Sediment production susceptibility index in urban area: a case study of Campo Grande – MS, Brazil DOI Creative Commons
Rafael Brandão Ferreira de Moraes, Cláudia Gonçalves Vianna Bacchi, Fábio Veríssimo Gonçalves

et al.

RBRH, Journal Year: 2024, Volume and Issue: 29

Published: Jan. 1, 2024

ABSTRACT Inadequate urban planning has contributed to the sediment production in Urban Hydrographic Micro-basins (UHMs). The present study aims develop and apply Sediment Production Susceptibility Index (SPSI) UHMs from Campo Grande – Mato Grosso do Sul (MS), Brazil, based on Analysis Hierarchical Process (AHP) Geographic Information System (GIS) aggregation. indicators selected for composition of SPSI are Soil Class (49%), Average Slope (22%), Vegetation Cover (13%), Unpaved Streets (16%). It is essentially jointly analyze both spheres (natural anthropogenic) obtain greater reliability studies related sedimentation areas. undergoing urbanization more susceptible than that already densely occupied. can assist public managers environmental adoption preventive measures against silting water bodies obstruction drainage systems.

Language: Английский

Citations

0