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A geospatial approach for assessing urban flood risk zones in Chennai, Tamil Nadu, India.

Murugesan BagyarajVenkatramanan SenapathiSang Yong ChungGnanachandrasamy GopalakrishnanYong XiaoSivakumar KarthikeyanAta Allah NadiriRahim Barzegar
Published in: Environmental science and pollution research international (2023)
Chennai, the capital city of Tamil Nadu in India, has experienced several instances of severe flooding over the past two decades, primarily attributed to persistent heavy rainfall. Accurate mapping of flood-prone regions in the basin is crucial for the comprehensive flood risk management. This study used the GIS-MCDA model, a multi-criteria decision analysis (MCDA) model that incorporated geographic information system (GIS) technology to support decision making processes. Remote sensing, GIS, and analytical hierarchy technique (AHP) were used to identify flood-prone zones and to determine the weights of various factors affecting flood risk, such as rainfall, distance to river, elevation, slope, land use/land cover, drainage density, soil type, and lithology. Four groups (zones) were identified by the flood susceptibility map including high, medium, low, and very low. These zones occupied 16.41%, 67.33%, 16.18%, and 0.08% of the area, respectively. Historical flood events in the study area coincided with the flood risk classification and flood vulnerability map. Regions situated close to rivers, characterized by low elevation, slope, and high runoff density were found to be more susceptible to flooding. The flood susceptibility map generated by the GIS-MCDA accurately described the flood-prone regions in the study area.
Keyphrases
  • climate change
  • decision making
  • high resolution
  • healthcare
  • machine learning
  • deep learning
  • social media
  • early onset