Application of a Novel Hybrid Method for Flood Susceptibility Mapping with Satellite Images: A Case Study of Seoul, Korea

Abstract

This paper proposes a novel hybrid method for flood susceptibility mapping using a geographic information system (ArcGIS) and satellite images based on the analytical hierarchy process (AHP). Here, the following nine multisource environmental controlling factors influencing flood susceptibility were considered for relative weight estimation in AHP: elevation, land use, slope, topographic wetness index, curvature, river distance, flow accumulation, drainage density, and rainfall. The weight for each factor was determined from AHP and analyzed to investigate critical regions that are more vulnerable to floods using the overlay weighted sum technique to integrate the nine layers. As a case study, the ArcGIS-based framework was applied in Seoul to obtain a flood susceptibility map, which was categorized into six regions (very high risk, high risk, medium risk, low risk, very low risk, and out of risk). Finally, the flood map was verified using real flood maps from the previous five years to test the model’s effectiveness. The flood map indicated that 40% of the area shows high flood risk and thus requires urgent attention, which was confirmed by the validation results. Planners and regulatory bodies can use flood maps to control and mitigate flood incidents along rivers. Even though the methodology used in this study is simple, it has a high level of accuracy and can be applied for flood mapping in most regions where the required datasets are available. This is the first study to apply high-resolution basic maps (12.5 m) to extract the nine controlling factors using only satellite images and ArcGIS to produce a suitable flood map in Seoul for better management in the near future.

A Country Wide Evaluation of Sweden's Spatial Flood Modeling With Optimized Convolutional Neural Network Algorithms

“…Distance from wetlands emerges as a relevant factor for flooding in Sweden, with areas less than 100 m from wetlands being most prone to flooding. Bare land surfaces are also highly prone to flooding, as they are typically associated with low infiltration rates (e.g., Narimani et al., 2021; Paul et al., 2019). Urban land is the third largest land use in Sweden and emerges as susceptible to flooding (Figure 6).…”

Novel Hybrid Models by Coupling Support Vector Regression (SVR) With Meta-heuristic Algorithms (WOA and GWO) for Urban Flood Risk Mapping

“…The usage of land is accounted as an important anthropogenic variables directly affecting velocity and volume of runoff (Yalcin et al, 2011) as well as transportation of sediment (Los Huertos, 2020). Several studies underscored the importance of land use pattern on flooding (e.g., Darabi et al, 2020;Mousavi et al, 2019;Narimani et al, 2021;Yulianto et al, 2020). In the area under study, several types of land uses exist such as thick forest and built up areas that have different impacts on flooding.…”

A novel hybrid GIS‐based multi‐criteria decision‐making approach for flood susceptibility analysis in large ungauged watersheds

“…Moreover, because these approaches do not rely on historical flood data, they can be implemented in ungauged areas which are dealing with data scarcity (Ali et al, 2020; Azareh et al, 2019; Franci et al, 2016; Kanani‐Sadat et al, 2019; Rahmati et al, 2016). In order to assess flood susceptibility, many MCDM techniques have been used in the studies in recent years (Gilandeh et al, 2020; Hammami et al, 2019; Kazakis et al, 2015; Narimani et al, 2021). Another aspect is that standalone methods may not accomplish a satisfactory accuracy and there is a necessity for developing a hybrid model that satisfies both the spatial and mathematics aspects (Ali et al, 2020).…”