S
Shattri Mansor
Researcher at Universiti Putra Malaysia
Publications - 166
Citations - 3957
Shattri Mansor is an academic researcher from Universiti Putra Malaysia. The author has contributed to research in topics: Landslide & Land cover. The author has an hindex of 30, co-authored 160 publications receiving 2916 citations. Previous affiliations of Shattri Mansor include University of Dundee.
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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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Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN)
TL;DR: The results show that the random landslide training data selection affected the parameter estimations of the SVM, LR and ANN algorithms and had an effect on the accuracy of the susceptibility model because landslide conditioning factors vary according to the geographic locations in the study area.
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The Performance of Maximum Likelihood, Spectral Angle Mapper, Neural Network and Decision Tree Classifiers in Hyperspectral Image Analysis
TL;DR: It was found that ML performed the best followed by ANN, DT and SAM with accuracies of 86%, 84%, 51% and 49% respectively.
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Modelling urban growth evolution and land-use changes using GIS based cellular automata and SLEUTH models: the case of Sana’a metropolitan city, Yemen
TL;DR: In this article, two models such as cellular automata (CA) and the SLEUTH models are applied in a geographical information system (GIS) to simulate and predict the urban growth and land use change for the City of Sana'a (Yemen) for the period 2004-2020.
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Landslide hazard and risk analyses at a landslide prone catchment area using statistical based geospatial model
TL;DR: In this article, the authors presented the application of remote sensing techniques, digital image analysis and Geographic Information System tools to delineate the degree of landslide hazard and risk areas in the Balik Pulau area in Penang Island, Malaysia.