Journal ArticleDOI
Flood susceptibility assessment using GIS-based support vector machine model with different kernel types
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In this paper, support vector machine (SVM) is used to predict flood susceptibility in the Kuala Terengganu basin, Malaysia, and four SVM kernel types such as linear (LN), polynomial (PL), radial basis function (RBF), and sigmoid (SIG) were used to check the robustness of the SVM model.Abstract:
Statistical learning theory is the basis of support vector machine (SVM) technique. This technique in natural hazard assessment is getting extremely popular these days. It contains a training stage related to the input and desire output values. The main goal of this paper is to assess and evaluate the prediction capability of SVM technique with different kernel functions for spatial prediction of flood occurrence. Kuala Terengganu basin, Malaysia was selected as study area. To begin, a flood inventory map was produced by mapping the flood locations in the Terengganu using documentary sources and field survey. The flood inventory was partitioned into training and testing datasets through random selection. The spatial database was constructed using various flood conditioning factors: altitude, slope, curvature, stream power index (SPI), topographic wetness index (TWI), distance from the river, geology, land use/cover (LULC), soil, and surface runoff. Four SVM kernel types such as linear (LN), polynomial (PL), radial basis function (RBF), and sigmoid (SIG) were utilized to check the robustness of the SVM model. Consequently, four flood susceptibility maps were created. In order to examine the efficiency of the SVM model, a probabilistic based frequency ratio (FR) model was applied and compared with the SVM outcomes. An area under the curve (AUC) method was used to validate the resultant flood susceptibility maps. The validation results demonstrated that the prediction rate curves for flood susceptibility maps generated by the SVM-LN, SVM-PL, SVM-RBF, and SVM-SIG were 84.63%, 83.92%, 84.97%, and 81.88% respectively. On the other hand, the prediction rate achieved by FR showed the lowest accuracy of 61.43%. To evaluate the impacts of conditioning factors on the flood susceptibility mapping, Cohen's kappa index was measured. The result demonstrated that all conditioning factors have reasonably positive influence on the flood analysis in current case study except surface runoff which decreased the accuracy of the final results. The most influential factors were altitude and slope for all kernel types. It can be concluded that SVM technique is an efficient and reliable tool in flood susceptibility assessment. The resultant flood susceptibility maps can be beneficial in flood mitigation strategies.read more
Citations
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Journal ArticleDOI
A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran.
Khabat Khosravi,Binh Thai Pham,Kamran Chapi,Ataollah Shirzadi,Himan Shahabi,Inge Revhaug,Indra Prakash,Dieu Tien Bui +7 more
TL;DR: Results show that the ADT model has the highest prediction capability for flash flood susceptibility assessment, followed by the NBT, the LMT, and the REPT, respectively.
Journal ArticleDOI
A novel hybrid artificial intelligence approach for flood susceptibility assessment
Kamran Chapi,Vijay P. Singh,Ataollah Shirzadi,Himan Shahabi,Dieu Tien Bui,Binh Thai Pham,Khabat Khosravi +6 more
TL;DR: Results indicate that the proposed Bagging-LMT model can be used for sustainable management of flood-prone areas and outperformed all state-of-the-art benchmark soft computing models.
Journal ArticleDOI
A comparative assessment of flood susceptibility modeling using Multi-Criteria Decision-Making Analysis and Machine Learning Methods
Khabat Khosravi,Himan Shahabi,Binh Thai Pham,Jan Adamowski,Ataollah Shirzadi,Biswajeet Pradhan,Biswajeet Pradhan,Jie Dou,Hai-Bang Ly,Gyula Gróf,Huu Loc Ho,Haoyuan Hong,Kamran Chapi,Indra Prakash +13 more
TL;DR: In this article, three Multi-Criteria Decision-Making (MCDM) analysis techniques (VIKOR, TOPSIS and SAW) along with two machine learning methods (NBT and NB) were tested for their ability to model flood susceptibility in one of China's most flood-prone areas, the Ningdu Catchment.
Journal ArticleDOI
Flood prediction using machine learning models: Literature review
TL;DR: In this paper, the state-of-the-art machine learning models for both long-term and short-term floods are evaluated and compared using a qualitative analysis of robustness, accuracy, effectiveness and speed.
Journal ArticleDOI
A GIS-based flood susceptibility assessment and its mapping in Iran: a comparison between frequency ratio and weights-of-evidence bivariate statistical models with multi-criteria decision-making technique
TL;DR: In this paper, the authors used four models, namely frequency ratio (FR), weights-of-evidence (WofE), analytical hierarchy process (AHP), and ensemble of frequency ratio with AHP (FR-AHP) to compare them at Haraz Watershed in Mazandaran Province, Iran.
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