Slop failure model based on UAV images using GWR technique / Nor Ziana Mohd Zain

Slope failure is the serious natural phenomena occur in Malaysia especially during the monsoon season. The majority of catastrophes in this country have occurred in hillside areas, resulting deaths, injuries, and property destruction. Over the past few decades, numerous research has created landslid...

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Main Author:	Mohd Zain, Nor Ziana
Format:	Thesis
Language:	English
Published:	2022
Subjects:	Aerial geography Slopes (Physical geography)
Online Access:	https://ir.uitm.edu.my/id/eprint/69125/1/69125.pdf
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id	my-uitm-ir.69125
record_format	uketd_dc
spelling	my-uitm-ir.691252022-11-14T06:07:53Z Slop failure model based on UAV images using GWR technique / Nor Ziana Mohd Zain 2022 Mohd Zain, Nor Ziana Aerial geography Slopes (Physical geography) Slope failure is the serious natural phenomena occur in Malaysia especially during the monsoon season. The majority of catastrophes in this country have occurred in hillside areas, resulting deaths, injuries, and property destruction. Over the past few decades, numerous research has created landslide susceptibility maps using a variety of methods. This study was focused on monitoring a slope failure using Unmanned Aerial Vehicles (UAV) images. The primary data that were used in the study were images data from UAV in Lojing Gua Musang. Orthomosaic, Digital Surface Model and Digital Terrain Model were constructed through photogrammetry processes. A Digital Elevation Model was also created, from which the slope, elevation and aspect were created using the algorithm in ArcGIS software. Geographically Weighted Regression (GWR) provide better prediction for local estimation where four related factors were calculated and extracted from the spatial database and used to analyse slope failure model. Compared with the global logistic regression model, the Akaike Information Criteria was improved by 1409.565, the adjusted R-squared was improved from 0.244 to 0.995, and the sum of square ANOVA of this analysis was improved from 37331.061 to 223.206. The comparisons obtained from the models show that geographically weighted regression has higher predictive performance. As the result, the appropriate combination colours and symbols of the slope failure map was produced for spatial prediction model in order to be applied for slope failure monitoring approaches by the authorities and environmentalist. 2022 Thesis https://ir.uitm.edu.my/id/eprint/69125/ https://ir.uitm.edu.my/id/eprint/69125/1/69125.pdf text en public degree Universiti Teknologi MARA, Perlis Faculty of Architecture, Planning and Surveying Talib, Noorfatekah
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Talib, Noorfatekah
topic	Aerial geography Slopes (Physical geography)
spellingShingle	Aerial geography Slopes (Physical geography) Mohd Zain, Nor Ziana Slop failure model based on UAV images using GWR technique / Nor Ziana Mohd Zain
description	Slope failure is the serious natural phenomena occur in Malaysia especially during the monsoon season. The majority of catastrophes in this country have occurred in hillside areas, resulting deaths, injuries, and property destruction. Over the past few decades, numerous research has created landslide susceptibility maps using a variety of methods. This study was focused on monitoring a slope failure using Unmanned Aerial Vehicles (UAV) images. The primary data that were used in the study were images data from UAV in Lojing Gua Musang. Orthomosaic, Digital Surface Model and Digital Terrain Model were constructed through photogrammetry processes. A Digital Elevation Model was also created, from which the slope, elevation and aspect were created using the algorithm in ArcGIS software. Geographically Weighted Regression (GWR) provide better prediction for local estimation where four related factors were calculated and extracted from the spatial database and used to analyse slope failure model. Compared with the global logistic regression model, the Akaike Information Criteria was improved by 1409.565, the adjusted R-squared was improved from 0.244 to 0.995, and the sum of square ANOVA of this analysis was improved from 37331.061 to 223.206. The comparisons obtained from the models show that geographically weighted regression has higher predictive performance. As the result, the appropriate combination colours and symbols of the slope failure map was produced for spatial prediction model in order to be applied for slope failure monitoring approaches by the authorities and environmentalist.
format	Thesis
qualification_level	Bachelor degree
author	Mohd Zain, Nor Ziana
author_facet	Mohd Zain, Nor Ziana
author_sort	Mohd Zain, Nor Ziana
title	Slop failure model based on UAV images using GWR technique / Nor Ziana Mohd Zain
title_short	Slop failure model based on UAV images using GWR technique / Nor Ziana Mohd Zain
title_full	Slop failure model based on UAV images using GWR technique / Nor Ziana Mohd Zain
title_fullStr	Slop failure model based on UAV images using GWR technique / Nor Ziana Mohd Zain
title_full_unstemmed	Slop failure model based on UAV images using GWR technique / Nor Ziana Mohd Zain
title_sort	slop failure model based on uav images using gwr technique / nor ziana mohd zain
granting_institution	Universiti Teknologi MARA, Perlis
granting_department	Faculty of Architecture, Planning and Surveying
publishDate	2022
url	https://ir.uitm.edu.my/id/eprint/69125/1/69125.pdf
_version_	1783735847408893952

Slop failure model based on UAV images using GWR technique / Nor Ziana Mohd Zain

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