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Rockfall source identification using a hybrid Gaussian mixture-ensemble machine learning model and LiDAR data

The availability of high-resolution laser scanning data and advanced machine learning algorithms has enabled an accurate potential rockfall source identification. However, the presence of other mass movements, such as landslides within the same region of interest, poses additional challenges to this...

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Main Authors: Fanos, Ali Mutar, Pradhan, Biswajeet, Mansor, Shattri, Md Yusoff, Zainuddin, Abdullah, Ahmad Fikri, Jung, Hyung Sup
Format: Article
Language:English
Published: The Korean Society of Remote Sensing 2019
Online Access:http://psasir.upm.edu.my/id/eprint/82041/1/Rockfall%20source%20identification%20using%20a%20hybrid%20Gaussian%20mixture-ensemble%20machine%20learning%20model%20and%20LiDAR%20data.pdf
http://psasir.upm.edu.my/id/eprint/82041/
https://www.koreascience.or.kr/article/JAKO201909242559364.page
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spelling my.upm.eprints.820412021-09-08T02:17:40Z http://psasir.upm.edu.my/id/eprint/82041/ Rockfall source identification using a hybrid Gaussian mixture-ensemble machine learning model and LiDAR data Fanos, Ali Mutar Pradhan, Biswajeet Mansor, Shattri Md Yusoff, Zainuddin Abdullah, Ahmad Fikri Jung, Hyung Sup The availability of high-resolution laser scanning data and advanced machine learning algorithms has enabled an accurate potential rockfall source identification. However, the presence of other mass movements, such as landslides within the same region of interest, poses additional challenges to this task. Thus, this research presents a method based on an integration of Gaussian mixture model (GMM) and ensemble artificial neural network (bagging ANN [BANN]) for automatic detection of potential rockfall sources at Kinta Valley area, Malaysia. The GMM was utilised to determine slope angle thresholds of various geomorphological units. Different algorithms (ANN, support vector machine [SVM] and k nearest neighbour [kNN]) were individually tested with various ensemble models (bagging, voting and boosting). Grid search method was adopted to optimise the hyperparameters of the investigated base models. The proposed model achieves excellent results with success and prediction accuracies at 95% and 94%, respectively. In addition, this technique has achieved excellent accuracies (ROC = 95%) over other methods used. Moreover, the proposed model has achieved the optimal prediction accuracies (92%) on the basis of testing data, thereby indicating that the model can be generalised and replicated in different regions, and the proposed method can be applied to various landslide studies. The Korean Society of Remote Sensing 2019 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/82041/1/Rockfall%20source%20identification%20using%20a%20hybrid%20Gaussian%20mixture-ensemble%20machine%20learning%20model%20and%20LiDAR%20data.pdf Fanos, Ali Mutar and Pradhan, Biswajeet and Mansor, Shattri and Md Yusoff, Zainuddin and Abdullah, Ahmad Fikri and Jung, Hyung Sup (2019) Rockfall source identification using a hybrid Gaussian mixture-ensemble machine learning model and LiDAR data. Korean Journal of Remote Sensing, 35 (1). pp. 93-115. ISSN 1225-6161; ESSN: 2287-9307 https://www.koreascience.or.kr/article/JAKO201909242559364.page 10.7780/kjrs.2019.35.1.7
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description The availability of high-resolution laser scanning data and advanced machine learning algorithms has enabled an accurate potential rockfall source identification. However, the presence of other mass movements, such as landslides within the same region of interest, poses additional challenges to this task. Thus, this research presents a method based on an integration of Gaussian mixture model (GMM) and ensemble artificial neural network (bagging ANN [BANN]) for automatic detection of potential rockfall sources at Kinta Valley area, Malaysia. The GMM was utilised to determine slope angle thresholds of various geomorphological units. Different algorithms (ANN, support vector machine [SVM] and k nearest neighbour [kNN]) were individually tested with various ensemble models (bagging, voting and boosting). Grid search method was adopted to optimise the hyperparameters of the investigated base models. The proposed model achieves excellent results with success and prediction accuracies at 95% and 94%, respectively. In addition, this technique has achieved excellent accuracies (ROC = 95%) over other methods used. Moreover, the proposed model has achieved the optimal prediction accuracies (92%) on the basis of testing data, thereby indicating that the model can be generalised and replicated in different regions, and the proposed method can be applied to various landslide studies.
format Article
author Fanos, Ali Mutar
Pradhan, Biswajeet
Mansor, Shattri
Md Yusoff, Zainuddin
Abdullah, Ahmad Fikri
Jung, Hyung Sup
spellingShingle Fanos, Ali Mutar
Pradhan, Biswajeet
Mansor, Shattri
Md Yusoff, Zainuddin
Abdullah, Ahmad Fikri
Jung, Hyung Sup
Rockfall source identification using a hybrid Gaussian mixture-ensemble machine learning model and LiDAR data
author_facet Fanos, Ali Mutar
Pradhan, Biswajeet
Mansor, Shattri
Md Yusoff, Zainuddin
Abdullah, Ahmad Fikri
Jung, Hyung Sup
author_sort Fanos, Ali Mutar
title Rockfall source identification using a hybrid Gaussian mixture-ensemble machine learning model and LiDAR data
title_short Rockfall source identification using a hybrid Gaussian mixture-ensemble machine learning model and LiDAR data
title_full Rockfall source identification using a hybrid Gaussian mixture-ensemble machine learning model and LiDAR data
title_fullStr Rockfall source identification using a hybrid Gaussian mixture-ensemble machine learning model and LiDAR data
title_full_unstemmed Rockfall source identification using a hybrid Gaussian mixture-ensemble machine learning model and LiDAR data
title_sort rockfall source identification using a hybrid gaussian mixture-ensemble machine learning model and lidar data
publisher The Korean Society of Remote Sensing
publishDate 2019
url http://psasir.upm.edu.my/id/eprint/82041/1/Rockfall%20source%20identification%20using%20a%20hybrid%20Gaussian%20mixture-ensemble%20machine%20learning%20model%20and%20LiDAR%20data.pdf
http://psasir.upm.edu.my/id/eprint/82041/
https://www.koreascience.or.kr/article/JAKO201909242559364.page
_version_ 1710677153786363904
score 13.211869

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