Landslide susceptibility modelling using GIS and Random Forest Machine Learning

Landslide a disaster that often occurs due to human intervention and it requires more attention nowadays more than ever since people that have been impacted by the aftermath of such incidents significantly, especially to those who tends to live and work in country or a part of an area that are made...

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Main Author: Soo, Neng Wu
Format: Final Year Project
Language: English
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-utpedia.20989 /
Published: Universiti Teknologi PETRONAS 2020
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access: http://utpedia.utp.edu.my/20989/1/CV50_23383_2SET_wordthesis.pdf
http://utpedia.utp.edu.my/20989/
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spelling utp-utpedia.209892021-09-12T22:14:46Z http://utpedia.utp.edu.my/20989/ Landslide susceptibility modelling using GIS and Random Forest Machine Learning Soo, Neng Wu TA Engineering (General). Civil engineering (General) Landslide a disaster that often occurs due to human intervention and it requires more attention nowadays more than ever since people that have been impacted by the aftermath of such incidents significantly, especially to those who tends to live and work in country or a part of an area that are made up of mountains where the gradient of the slope is generally steeper. The aftermath of landslides caused wide ranges of adversary effects in the past and still do now. Property are destroyed or damaged, people who affected are injured or possibly death; even after the disaster, ruptured or blocked roadways due to landslides cut off connections that requires the road for the vehicles to pass. Several precautions can be made to deduce its negative effects on the society. In such occasion, the development of the LSM will be considered a vital step to tackle the problems as it provides required information and turns it into a plan on which area is the most vulnerable against landslide to occur. Universiti Teknologi PETRONAS 2020-05 Final Year Project NonPeerReviewed application/pdf en http://utpedia.utp.edu.my/20989/1/CV50_23383_2SET_wordthesis.pdf Soo, Neng Wu (2020) Landslide susceptibility modelling using GIS and Random Forest Machine Learning. Universiti Teknologi PETRONAS. (Submitted)
institution Universiti Teknologi Petronas
collection UTPedia
language English
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Soo, Neng Wu
Landslide susceptibility modelling using GIS and Random Forest Machine Learning
description Landslide a disaster that often occurs due to human intervention and it requires more attention nowadays more than ever since people that have been impacted by the aftermath of such incidents significantly, especially to those who tends to live and work in country or a part of an area that are made up of mountains where the gradient of the slope is generally steeper. The aftermath of landslides caused wide ranges of adversary effects in the past and still do now. Property are destroyed or damaged, people who affected are injured or possibly death; even after the disaster, ruptured or blocked roadways due to landslides cut off connections that requires the road for the vehicles to pass. Several precautions can be made to deduce its negative effects on the society. In such occasion, the development of the LSM will be considered a vital step to tackle the problems as it provides required information and turns it into a plan on which area is the most vulnerable against landslide to occur.
format Final Year Project
author Soo, Neng Wu
author_sort Soo, Neng Wu
title Landslide susceptibility modelling using GIS and Random Forest Machine Learning
title_short Landslide susceptibility modelling using GIS and Random Forest Machine Learning
title_full Landslide susceptibility modelling using GIS and Random Forest Machine Learning
title_fullStr Landslide susceptibility modelling using GIS and Random Forest Machine Learning
title_full_unstemmed Landslide susceptibility modelling using GIS and Random Forest Machine Learning
title_sort landslide susceptibility modelling using gis and random forest machine learning
publisher Universiti Teknologi PETRONAS
publishDate 2020
url http://utpedia.utp.edu.my/20989/1/CV50_23383_2SET_wordthesis.pdf
http://utpedia.utp.edu.my/20989/
_version_ 1741195692006703104
score 11.62408

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