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Utilizing Remote Sensing and GIS Techniques for Flood Hazard Mapping and Risk Assessment

Author(s):





Medium: journal article
Language(s): English
Published in: Civil Engineering Journal, , n. 5, v. 10
Page(s): 1423-1436
DOI: 10.28991/cej-2024-010-05-05
Abstract:

In this paper, a comprehensive flood hazard map for the vicinity of King Talal Dam in Jordan, utilizing advanced remote sensing (RS) and GIS methodologies, is developed. Key geographical and environmental factors, encompassing terrain slope, elevation, aspect, proximity to water streams, drainage density, and land use/land cover, are integrated to highlight areas with increased flood risk. This study, by employing a novel theoretical approach, harnesses the synergistic capabilities of RS and GIS to collect and analyze geospatial data. The Analytic Hierarchy Process (AHP) is applied to assign weights to various flood-conditioning factors, quantifying their relative importance in flood risk assessment. Through the weighted sum overlay technique, the aforementioned factors are integrated to categorize flood risk levels from very low to very high. This study successfully maps flood hazards, identifying areas near main water channels, ravines, and lower-elevation areas prone to flooding. This research provides a robust framework for flood risk assessment, contributing valuable knowledge to the fields of environmental management and disaster mitigation. It underscores the importance of continuous monitoring and updating of flood hazard maps to accommodate changing land use, climate, and hydrological conditions. The innovative application offers crucial insights for urban planners and policymakers, emphasizing the need for proactive strategies in flood-prone areas and serving as a model for similar geographical regions. Doi: 10.28991/CEJ-2024-010-05-05 Full Text: PDF

Structurae cannot make the full text of this publication available at this time. The full text can be accessed through the publisher via the DOI: 10.28991/cej-2024-010-05-05.
  • About this
    data sheet
  • Reference-ID
    10789957
  • Published on:
    20/06/2024
  • Last updated on:
    20/06/2024
 
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