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

ABSTRACT:

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.


Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here


PROJECT TITLE :Research-Based Monitoring, Prediction, and Analysis Tools of the Spacecraft Charging Environment for Spacecraft UsersABSTRACT:The Area Weather Analysis Center (http://swrc. gsfc.nasa.gov) at NASA Goddard, part of
PROJECT TITLE : Video Dissemination over Hybrid Cellular and Ad Hoc Networks - 2014 ABSTRACT: We study the problem of disseminating videos to mobile users by using a hybrid cellular and ad hoc network. In particular, we formulate
PROJECT TITLE : Secure and Efficient Data Transmission for Cluster-Based Wireless Sensor Networks - 2014 ABSTRACT: Secure data transmission is a critical issue for wireless sensor networks (WSNs). Clustering is an effective
PROJECT TITLE : PSR A Lightweight Proactive Source Routing Protocol For Mobile Ad Hoc Networks - 2014 ABSTRACT: Opportunistic data forwarding has drawn much attention in the research community of multihop wireless networking,
PROJECT TITLE : Multicast Capacity in MANET with Infrastructure Support - 2014 ABSTRACT: We study the multicast capacity under a network model featuring both node's mobility and infrastructure support. Combinations between

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry