M
Mahyat Shafapour Tehrany
Researcher at RMIT University
Publications - 31
Citations - 4278
Mahyat Shafapour Tehrany is an academic researcher from RMIT University. The author has contributed to research in topics: Topographic Wetness Index & Flood myth. The author has an hindex of 23, co-authored 30 publications receiving 2963 citations. Previous affiliations of Mahyat Shafapour Tehrany include University of New England (Australia) & Universiti Putra Malaysia.
Papers
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Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS
TL;DR: In this article, the authors proposed an ensemble weight-of-evidence (WoE) and support vector machine (SVM) model to assess the impact of classes of each conditioning factor on flooding through bivariate statistical analysis.
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Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS
TL;DR: In this article, the authors compared the performance of two different approaches such as rule-based decision tree (DT) and combination of frequency ratio (FR) and logistic regression (LR) statistical methods for flood susceptibility mapping at Kelantan, Malaysia.
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Flood susceptibility assessment using GIS-based support vector machine model with different kernel types
TL;DR: 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.
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Flood susceptibility analysis and its verification using a novel ensemble support vector machine and frequency ratio method
TL;DR: In this article, a novel ensemble method was proposed by integrating support vector machine (SVM) and frequency ratio (FR) to produce spatial modeling in flood susceptibility assessment. But, the proposed method is not suitable for the underwater environment.
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Earthquake induced landslide susceptibility mapping using an integrated ensemble frequency ratio and logistic regression models in West Sumatera Province, Indonesia
TL;DR: In this paper, an ensemble method of frequency ratio (FR) and logistic regression (LR) was proposed for landslide susceptibility mapping (LSM) in order to overcome their weak points.