PROJECT TITLE :
Manifestation of LiDAR-Derived Parameters in the Spatial Prediction of Landslides Using Novel Ensemble Evidential Belief Functions and Support Vector Machine Models in GIS
Landslide susceptibility mapping is indispensable for disaster management and coming up with development operations in mountainous regions. The potential use of light detection and ranging (LiDAR) knowledge was explored during this study for deriving landslide-conditioning factors for the spatial prediction of landslide-inclined areas in an exceedingly landslide-prone space in Ulu Klang, Malaysia. 9 landslide-conditioning factors, such as altitude, slope, facet, curvature, stream power index (SPI), topographic wetness index (TWI), terrain roughness index (TRI), sediment transport index (STI), and slope length (SL), were directly derived from LiDAR for landslide-susceptibility mapping. The main objective of this research was to propose a novel ensemble landslide susceptibility mapping method to boost the performance of individual methods of support vector machine (SVM) and evidential belief function (EBF). SVM is time-consuming when various data sorts, such as nominal, scale, and ordinal, are used. This characteristic of the individual SVM method isn't optimal for hazard modeling. This downside will be resolved by assessing the effects of the classes of every conditioning factor on landslide occurrence through a knowledge-driven EBF method. Hence, EBF was applied during this study, and weights were acquired for the classes of each conditioning factor. The conditioning factors were reclassified based mostly on the attained weights and entered into SVM as a scale to judge the correlation between landslide prevalence and each conditioning issue. Four SVM kernel sorts [radial basis operate kernel (RBF), sigmoid kernel (SIG), linear kernel (LN), and polynomial kernel (PL)] were tested to explore the efficiency of every kernel in SVM modeling. The efficiencies of the ensemble EBF and SVM ways were examined through area below curve (AUC). The RBF kernel obtained better results than the opposite kernel sorts. The success and prediction rates obtained from the validation results of ensemble EBF and RBF-SVM - ethod were eighty three.04% and eighty.04%, respectively. The proposed novel ensemble methodology fairly accelerated the processing and enhanced the results by combining the advantages of each methods.
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