Flood risk assessment using GIS and multi-criteria analysis: a pilot study from Rönne å River basin, Scania, Sweden
(2024) In Student thesis series INES NGEK01 20241Dept of Physical Geography and Ecosystem Science
- Abstract
- Following the flooding event in Ringsjön within the Rönne å river basin in January 2024, this catchment area was utilized for a flood risk assessment. The study focused on the precipitation event during August 2023, known as Storm Hans. The flood risk assessment for the Rönne å catchment area was conducted using multi-criteria analysis (MCA) and geographical information systems (GIS). Seven factors impacting flooding were considered in the MCA: slope, elevation, monthly precipitation, land use, soil texture, proximity to water bodies, and drainage density, based on the studies by Noori & Bonakdari (2023), Ali et al. (2023), and Hagos et al. (2022). The factors were ranked according to the weights provided in these three studies, as well as... (More)
- Following the flooding event in Ringsjön within the Rönne å river basin in January 2024, this catchment area was utilized for a flood risk assessment. The study focused on the precipitation event during August 2023, known as Storm Hans. The flood risk assessment for the Rönne å catchment area was conducted using multi-criteria analysis (MCA) and geographical information systems (GIS). Seven factors impacting flooding were considered in the MCA: slope, elevation, monthly precipitation, land use, soil texture, proximity to water bodies, and drainage density, based on the studies by Noori & Bonakdari (2023), Ali et al. (2023), and Hagos et al. (2022). The factors were ranked according to the weights provided in these three studies, as well as one analysis assigning equal weights to all factors. The factors were normalized using fuzzy linear equations, resulting in values that ranged from 0 (very low flood risk) to 1 (very high flood risk). These normalized factors were combined using the weighted linear combination (WLC) method to create four distinct flood risk maps, where each map was based on the different weights used in the articles. The reliability of these maps was evaluated by overlaying them with a flood map created by Sweco, which utilized the hydraulic model MIKE 11. Since MIKE 11 is a flood simulation model, the resulting polygon from this analysis was considered to represent a 100% very high flood risk. Consequently, when the different maps from the MCA were overlaid with this polygon, the higher the percentage of the high-risk class in that area, the more reliable the map was assumed to be. The map based on the weights from Hagos et al. (2022) proved to be the most accurate, where 79% of the overlaid area with the MIKE 11 model was classified as high and very high risk. Conversely, the map using weights from Noori & Bonakdari (2023) was the least accurate, where only 29% of high to very high areas were found in the polygon layer produced by Sweco. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9163737
- author
- Steichen, Florian LU
- supervisor
- organization
- course
- NGEK01 20241
- year
- 2024
- type
- M2 - Bachelor Degree
- subject
- keywords
- GIS, MCA, Rönne å, flood risk assessment, fuzzy membership, WCL, extreme precipitation, climate change
- publication/series
- Student thesis series INES
- report number
- 649
- language
- English
- id
- 9163737
- date added to LUP
- 2024-06-14 12:40:53
- date last changed
- 2024-06-14 12:40:53
@misc{9163737,
abstract = {{Following the flooding event in Ringsjön within the Rönne å river basin in January 2024, this catchment area was utilized for a flood risk assessment. The study focused on the precipitation event during August 2023, known as Storm Hans. The flood risk assessment for the Rönne å catchment area was conducted using multi-criteria analysis (MCA) and geographical information systems (GIS). Seven factors impacting flooding were considered in the MCA: slope, elevation, monthly precipitation, land use, soil texture, proximity to water bodies, and drainage density, based on the studies by Noori & Bonakdari (2023), Ali et al. (2023), and Hagos et al. (2022). The factors were ranked according to the weights provided in these three studies, as well as one analysis assigning equal weights to all factors. The factors were normalized using fuzzy linear equations, resulting in values that ranged from 0 (very low flood risk) to 1 (very high flood risk). These normalized factors were combined using the weighted linear combination (WLC) method to create four distinct flood risk maps, where each map was based on the different weights used in the articles. The reliability of these maps was evaluated by overlaying them with a flood map created by Sweco, which utilized the hydraulic model MIKE 11. Since MIKE 11 is a flood simulation model, the resulting polygon from this analysis was considered to represent a 100% very high flood risk. Consequently, when the different maps from the MCA were overlaid with this polygon, the higher the percentage of the high-risk class in that area, the more reliable the map was assumed to be. The map based on the weights from Hagos et al. (2022) proved to be the most accurate, where 79% of the overlaid area with the MIKE 11 model was classified as high and very high risk. Conversely, the map using weights from Noori & Bonakdari (2023) was the least accurate, where only 29% of high to very high areas were found in the polygon layer produced by Sweco.}},
author = {{Steichen, Florian}},
language = {{eng}},
note = {{Student Paper}},
series = {{Student thesis series INES}},
title = {{Flood risk assessment using GIS and multi-criteria analysis: a pilot study from Rönne å River basin, Scania, Sweden}},
year = {{2024}},
}