Study of Hybridized Support Vector Regression Based Flood Susceptibility Mapping for Bangladesh

2021 ◽  
pp. 59-71
Author(s):  
Zakaria Shams Siam ◽  
Rubyat Tasnuva Hasan ◽  
Soumik Sarker Anik ◽  
Fahima Noor ◽  
Mohammed Sarfaraz Gani Adnan ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Susceptibility Mapping ◽  
Support Vector ◽  
Flood Susceptibility ◽  
Flood Susceptibility Mapping

scholarly journals A novel GIS-based ensemble technique for flood susceptibility mapping using evidential belief function and support vector machine: Brisbane, Australia

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7653 ◽  
Author(s):  
Mahyat Shafapour Tehrany ◽  
Lalit Kumar ◽  
Farzin Shabani
Keyword(s):  
Support Vector Machine ◽  
Statistical Analysis ◽  
Belief Function ◽  
Susceptibility Mapping ◽  
Support Vector ◽  
Ensemble Technique ◽  
Evidential Belief Function ◽  
Flood Susceptibility ◽  
Flood Susceptibility Mapping ◽  
The Individual

In this study, we propose and test a novel ensemble method for improving the accuracy of each method in flood susceptibility mapping using evidential belief function (EBF) and support vector machine (SVM). The outcome of the proposed method was compared with the results of each method. The proposed method was implemented four times using different SVM kernels. Hence, the efficiency of each SVM kernel was also assessed. First, a bivariate statistical analysis using EBF was performed to assess the correlations among the classes of each flood conditioning factor with flooding. Subsequently, the outcome of the first stage was used in a multivariate statistical analysis performed by SVM. A highest prediction accuracy of 92.11% was achieved by an ensemble EBF-SVM—radial basis function method; the achieved accuracy was 7% and 3% higher than that offered by the individual EBF method and the individual SVM method, respectively. Among all the applied methods, both the individual EBF and SVM methods achieved the lowest accuracies. The reason for the improved accuracy offered by the ensemble methods is that by integrating the methods, a more detailed assessment of the flooding and conditioning factors can be performed, thereby increasing the accuracy of the final map.

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 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.

scholarly journals Flood Susceptibility Assessment Using Novel Ensemble of Hyperpipes and Support Vector Regression Algorithms

Water ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 241
Author(s):  
Asish Saha ◽  
Subodh Chandra Pal ◽  
Alireza Arabameri ◽  
Thomas Blaschke ◽  
Somayeh Panahi ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Accuracy Assessment ◽  
Research Work ◽  
Dominant Factor ◽  
Support Vector ◽  
Accuracy Evaluation ◽  
Ensemble Approach ◽  
Flood Susceptibility

Recurrent floods are one of the major global threats among people, particularly in developing countries like India, as this nation has a tropical monsoon type of climate. Therefore, flood susceptibility (FS) mapping is indeed necessary to overcome this type of natural hazard phenomena. With this in mind, we evaluated the prediction performance of FS mapping in the Koiya River basin, Eastern India. The present research work was done through preparation of a sophisticated flood inventory map; eight flood conditioning variables were selected based on the topography and hydro-climatological condition, and by applying the novel ensemble approach of hyperpipes (HP) and support vector regression (SVR) machine learning (ML) algorithms. The ensemble approach of HP-SVR was also compared with the stand-alone ML algorithms of HP and SVR. In relative importance of variables, distance to river was the most dominant factor for flood occurrences followed by rainfall, land use land cover (LULC), and normalized difference vegetation index (NDVI). The validation and accuracy assessment of FS maps was done through five popular statistical methods. The result of accuracy evaluation showed that the ensemble approach is the most optimal model (AUC = 0.915, sensitivity = 0.932, specificity = 0.902, accuracy = 0.928 and Kappa = 0.835) in FS assessment, followed by HP (AUC = 0.885) and SVR (AUC = 0.871).

Swarm‐based optimizer for convolutional neural network: An application for flood susceptibility mapping

2020 ◽  
Author(s):  
Tien‐Yin Chou ◽  
Thanh‐Van Hoang ◽  
Yao‐Min Fang ◽  
Quoc‐Huy Nguyen ◽  
Tuan Anh Lai ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Susceptibility Mapping ◽  
Flood Susceptibility ◽  
Flood Susceptibility Mapping
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