Geo-spatial Analysis for Flash Flood Susceptibility Mapping in the North-East Haor (Wetland) Region in Bangladesh

2021 ◽  
Author(s):  
Md. Nazmul Haque ◽  
Sharmin Siddika ◽  
Mizbah Ahmed Sresto ◽  
Md. Mustafa Saroar ◽  
Kazi Redwan Shabab
Keyword(s):  
Spatial Analysis ◽  
Flash Flood ◽  
Susceptibility Mapping ◽  
North East ◽  
The North ◽  
Flood Susceptibility ◽  
Flood Susceptibility Mapping

scholarly journals Prediction Success of Machine Learning Methods for Flash Flood Susceptibility Mapping in the Tafresh Watershed, Iran

2019 ◽  
Vol 11 (19) ◽  
pp. 5426 ◽  
Author(s):  
Saeid Janizadeh ◽  
Mohammadtaghi Avand ◽  
Abolfazl Jaafari ◽  
Tran Van Phong ◽  
Mahmoud Bayat ◽  
...  
Keyword(s):  
Machine Learning ◽  
Flash Flood ◽  
Susceptibility Mapping ◽  
Annual Rainfall ◽  
Slope Aspect ◽  
Flood Events ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Flood Susceptibility ◽  
Flood Susceptibility Mapping

Floods are some of the most destructive and catastrophic disasters worldwide. Development of management plans needs a deep understanding of the likelihood and magnitude of future flood events. The purpose of this research was to estimate flash flood susceptibility in the Tafresh watershed, Iran, using five machine learning methods, i.e., alternating decision tree (ADT), functional tree (FT), kernel logistic regression (KLR), multilayer perceptron (MLP), and quadratic discriminant analysis (QDA). A geospatial database including 320 historical flood events was constructed and eight geo-environmental variables—elevation, slope, slope aspect, distance from rivers, average annual rainfall, land use, soil type, and lithology—were used as flood influencing factors. Based on a variety of performance metrics, it is revealed that the ADT method was dominant over the other methods. The FT method was ranked as the second-best method, followed by the KLR, MLP, and QDA. Given a few differences between the goodness-of-fit and prediction success of the methods, we concluded that all these five machine-learning-based models are applicable for flood susceptibility mapping in other areas to protect societies from devastating floods.

A novel hybrid approach based on a swarm intelligence optimized extreme learning machine for flash flood susceptibility mapping

CATENA ◽  
2019 ◽  
Vol 179 ◽  
pp. 184-196 ◽  
Author(s):  
Dieu Tien Bui ◽  
Phuong-Thao Thi Ngo ◽  
Tien Dat Pham ◽  
Abolfazl Jaafari ◽  
Nguyen Quang Minh ◽  
...  
Keyword(s):  
Swarm Intelligence ◽  
Extreme Learning Machine ◽  
Flash Flood ◽  
Hybrid Approach ◽  
Susceptibility Mapping ◽  
Flood Susceptibility ◽  
Flood Susceptibility Mapping ◽  
Learning Machine

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

2021 ◽  
Vol 13 (14) ◽  
pp. 2786
Author(s):  
Roya Narimani ◽  
Changhyun Jun ◽  
Saqib Shahzad ◽  
Jeill Oh ◽  
Kyoohong Park
Keyword(s):  
High Risk ◽  
Hybrid Method ◽  
Satellite Images ◽  
Controlling Factors ◽  
Susceptibility Mapping ◽  
Low Risk ◽  
Flood Susceptibility ◽  
Flood Susceptibility Mapping ◽  
Flood Maps

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.

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