Flood Susceptibility Mapping for Kedah State, Malaysia: Geographics Information System-Based Machine Learning Approach

Background: The world economy is significantly impacted by floods. Identifying flood risk is essential to flood mitigation techniques. AIM: The primary goal of this study is to create a geographic information system (GIS)-based flood susceptibility map for the study area. Methods: Ten flood-influenc...

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Bibliographic Details
Published in:	Medical journal of Dr. D Y Patil University Vol. 17; no. 5; pp. 990 - 1003
Main Authors:	Keya, Tahmina A., Sreeramanan, S, Siventhiran, SB, Maheswaran, S, Selvan, Saravana, Fernandez, Kevin, An, Low J, Leela, A, Prahankumar, R, Lokeshmaran, A, Boratne, AV
Format:	Journal Article
Language:	English
Published:	Wolters Kluwer Medknow Publications 01-09-2024
Subjects:	ensemble machine learning flood susceptibility geographic information system kedah random forest
Online Access:	Get full text
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Summary:	Background: The world economy is significantly impacted by floods. Identifying flood risk is essential to flood mitigation techniques. AIM: The primary goal of this study is to create a geographic information system (GIS)-based flood susceptibility map for the study area. Methods: Ten flood-influencing factors from a geospatial database were taken into account when mapping the flood-prone areas. Every element demonstrated a robust relationship with the probability of flooding. Results: The highest contributing elements for the flood disaster in the study region were drainage density, distance, and the curvature. Flood susceptibility models’ performance was validated using standard statistical measures and AUC. The ROC curves demonstrated that all ensemble models had good performance on the validation data sets (AUC = >0.97) with high accuracy scores of 0.80. Based on the flood susceptibility maps, most of the northwest regions of the study area are more likely to flood because of low land areas, areas with a lower gradient slope, linear and concave shape curvature, high drainage density with high rainfall, more “water bodies,” “crops land,” and “built areas,” abundance on sea and surface water, and Quaternary types of soil feature and so on. The very high flood susceptibility class accounts for 18.2% of the study area, according to the RF-embedding model, whereas the high, moderate, low, and very low susceptibility classes were found at about 20.0%, 24.6%, 24.3%, and 12.9%, respectively. Conclusion: In comparison with other commonly used applied approaches, this research presents a novel modeling approach for flood susceptibility that integrates machine learning and geospatial data. It has been found to be stronger and more efficient, highly accurate, has good prediction performance, and is less biased. Overall, our research into machine learning-based solutions points in a positive path technologically and can serve as a reference manual for future research and applications for academic specialists and decision-makers.
ISSN:	2589-8302 2278-7119
DOI:	10.4103/mjdrdypu.mjdrdypu_985_23