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Mapping flood-prone areas in Bangladesh and assess how climate change will impact future flood risks

Rahman, Prangshu Md Naziur LU (2025) In Student thesis series INES NGEK01 20251
Dept of Physical Geography and Ecosystem Science
Abstract
Bangladesh faces recurrent and severe flooding due to its low-lying topography and intense monsoon climate, making it one of the most flood-prone countries in the world. These floods pose significant threats to human life, infrastructure, and economic stability which is projected to intensify under future climate change scenarios. This thesis presents a flood susceptibility assessment using the Analytic Hierarchy Process (AHP) combined with Geographic Information Systems (GIS). Eight key environmental and hydrological factors were considered for this spatial overlay analysis. Flood susceptibility maps were generated for three time periods — current, near future (2024–2060), and far future (2061–2100), using precipitation projections from... (More)
Bangladesh faces recurrent and severe flooding due to its low-lying topography and intense monsoon climate, making it one of the most flood-prone countries in the world. These floods pose significant threats to human life, infrastructure, and economic stability which is projected to intensify under future climate change scenarios. This thesis presents a flood susceptibility assessment using the Analytic Hierarchy Process (AHP) combined with Geographic Information Systems (GIS). Eight key environmental and hydrological factors were considered for this spatial overlay analysis. Flood susceptibility maps were generated for three time periods — current, near future (2024–2060), and far future (2061–2100), using precipitation projections from the CMIP6 model, Beijing Climate Centre Climate System Model (BCC-CSM2-MR) under the SSP2-4.5 scenario. The results show a clear increase in high and very high flood susceptibility zones, expanding from 57% of the country currently to 66% by 2060 and remains relatively similar to the end of 2100, with the most affected areas in central and northeastern Bangladesh. The model's predictive accuracy was confirmed through Receiver Operating Characteristic (ROC) curve, with an Area Under the Curve (AUC) value of 0.79, indicating good reliability. These findings emphasize the urgent need for adaptive strategies in flood management and land-use planning. The study offers a spatially framework to support evidence-based decision-making for reducing flood risk in Bangladesh in the face of climate change. (Less)
Please use this url to cite or link to this publication:
author
Rahman, Prangshu Md Naziur LU
supervisor
organization
course
NGEK01 20251
year
type
M2 - Bachelor Degree
subject
keywords
Flood susceptibility mapping in Bangladesh using GIS, AHP, CMIP6 precipitation, and multivariate analysis
publication/series
Student thesis series INES
report number
704
language
English
id
9203662
date added to LUP
2025-06-23 09:20:31
date last changed
2025-06-23 09:20:31
@misc{9203662,
  abstract     = {{Bangladesh faces recurrent and severe flooding due to its low-lying topography and intense monsoon climate, making it one of the most flood-prone countries in the world. These floods pose significant threats to human life, infrastructure, and economic stability which is projected to intensify under future climate change scenarios. This thesis presents a flood susceptibility assessment using the Analytic Hierarchy Process (AHP) combined with Geographic Information Systems (GIS). Eight key environmental and hydrological factors were considered for this spatial overlay analysis. Flood susceptibility maps were generated for three time periods — current, near future (2024–2060), and far future (2061–2100), using precipitation projections from the CMIP6 model, Beijing Climate Centre Climate System Model (BCC-CSM2-MR) under the SSP2-4.5 scenario. The results show a clear increase in high and very high flood susceptibility zones, expanding from 57% of the country currently to 66% by 2060 and remains relatively similar to the end of 2100, with the most affected areas in central and northeastern Bangladesh. The model's predictive accuracy was confirmed through Receiver Operating Characteristic (ROC) curve, with an Area Under the Curve (AUC) value of 0.79, indicating good reliability. These findings emphasize the urgent need for adaptive strategies in flood management and land-use planning. The study offers a spatially framework to support evidence-based decision-making for reducing flood risk in Bangladesh in the face of climate change.}},
  author       = {{Rahman, Prangshu Md Naziur}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Student thesis series INES}},
  title        = {{Mapping flood-prone areas in Bangladesh and assess how climate change will impact future flood risks}},
  year         = {{2025}},
}