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Application of a novel hybrid machine learning algorithm in shallow landslide susceptibility mapping in a mountainous area

Landslides can be a major challenge in mountainous areas that are influenced by climate and landscape changes. In this study, we propose a hybrid machine learning model based on a rotation forest (RoF) meta classifier and a random forest (RF) decision tree classifier called RoFRF for landslide predi...

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Main Authors: Ghasemian, Bahareh, Shahabi, Himan, Shirzadi, Ataollah, Al-Ansari, Nadhir, Jaafari, Abolfazl, Geertsema, Marten, M. Melesse, Assefa, K. Singh, Sushant, Ahmad, Anuar
Format: Article
Language:English
Published: Frontiers Media S.A. 2022
Subjects:
G70.39-70.6 Remote sensing
Online Access:http://eprints.utm.my/104043/1/AnuarAhmad2022_ApplicationofaNovelHybridMachineLearning.pdf
http://eprints.utm.my/104043/
http://dx.doi.org/10.3389/fenvs.2022.897254
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spelling my.utm.1040432024-01-14T00:51:49Z http://eprints.utm.my/104043/ Application of a novel hybrid machine learning algorithm in shallow landslide susceptibility mapping in a mountainous area Ghasemian, Bahareh Shahabi, Himan Shirzadi, Ataollah Al-Ansari, Nadhir Jaafari, Abolfazl Geertsema, Marten M. Melesse, Assefa K. Singh, Sushant Ahmad, Anuar G70.39-70.6 Remote sensing Landslides can be a major challenge in mountainous areas that are influenced by climate and landscape changes. In this study, we propose a hybrid machine learning model based on a rotation forest (RoF) meta classifier and a random forest (RF) decision tree classifier called RoFRF for landslide prediction in a mountainous area near Kamyaran city, Kurdistan Province, Iran. We used 118 landslide locations and 25 conditioning factors from which their predictive usefulness was measured using the chi-square technique in a 10-fold cross-validation analysis. We used the sensitivity, specificity, accuracy, F1-measure, Kappa, and area under the receiver operating characteristic curve (AUC) to validate the performance of the proposed model compared to the Artificial Neural Network (ANN), Logistic Model Tree (LMT), Best First Tree (BFT), and RF models. The validation results demonstrated that the landslide susceptibility map produced by the hybrid model had the highest goodness-of-fit (AUC = 0.953) and higher prediction accuracy (AUC = 0.919) compared to the benchmark models. The hybrid RoFRF model proposed in this study can be used as a robust predictive model for landslide susceptibility mapping in the mountainous regions around the world. Frontiers Media S.A. 2022 Article PeerReviewed application/pdf en http://eprints.utm.my/104043/1/AnuarAhmad2022_ApplicationofaNovelHybridMachineLearning.pdf Ghasemian, Bahareh and Shahabi, Himan and Shirzadi, Ataollah and Al-Ansari, Nadhir and Jaafari, Abolfazl and Geertsema, Marten and M. Melesse, Assefa and K. Singh, Sushant and Ahmad, Anuar (2022) Application of a novel hybrid machine learning algorithm in shallow landslide susceptibility mapping in a mountainous area. Frontiers in Environmental Science, 10 (NA). pp. 1-14. ISSN 2296-665X http://dx.doi.org/10.3389/fenvs.2022.897254 DOI : 10.3389/fenvs.2022.897254
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic G70.39-70.6 Remote sensing
spellingShingle G70.39-70.6 Remote sensing
Ghasemian, Bahareh
Shahabi, Himan
Shirzadi, Ataollah
Al-Ansari, Nadhir
Jaafari, Abolfazl
Geertsema, Marten
M. Melesse, Assefa
K. Singh, Sushant
Ahmad, Anuar
Application of a novel hybrid machine learning algorithm in shallow landslide susceptibility mapping in a mountainous area
description Landslides can be a major challenge in mountainous areas that are influenced by climate and landscape changes. In this study, we propose a hybrid machine learning model based on a rotation forest (RoF) meta classifier and a random forest (RF) decision tree classifier called RoFRF for landslide prediction in a mountainous area near Kamyaran city, Kurdistan Province, Iran. We used 118 landslide locations and 25 conditioning factors from which their predictive usefulness was measured using the chi-square technique in a 10-fold cross-validation analysis. We used the sensitivity, specificity, accuracy, F1-measure, Kappa, and area under the receiver operating characteristic curve (AUC) to validate the performance of the proposed model compared to the Artificial Neural Network (ANN), Logistic Model Tree (LMT), Best First Tree (BFT), and RF models. The validation results demonstrated that the landslide susceptibility map produced by the hybrid model had the highest goodness-of-fit (AUC = 0.953) and higher prediction accuracy (AUC = 0.919) compared to the benchmark models. The hybrid RoFRF model proposed in this study can be used as a robust predictive model for landslide susceptibility mapping in the mountainous regions around the world.
format Article
author Ghasemian, Bahareh
Shahabi, Himan
Shirzadi, Ataollah
Al-Ansari, Nadhir
Jaafari, Abolfazl
Geertsema, Marten
M. Melesse, Assefa
K. Singh, Sushant
Ahmad, Anuar
author_facet Ghasemian, Bahareh
Shahabi, Himan
Shirzadi, Ataollah
Al-Ansari, Nadhir
Jaafari, Abolfazl
Geertsema, Marten
M. Melesse, Assefa
K. Singh, Sushant
Ahmad, Anuar
author_sort Ghasemian, Bahareh
title Application of a novel hybrid machine learning algorithm in shallow landslide susceptibility mapping in a mountainous area
title_short Application of a novel hybrid machine learning algorithm in shallow landslide susceptibility mapping in a mountainous area
title_full Application of a novel hybrid machine learning algorithm in shallow landslide susceptibility mapping in a mountainous area
title_fullStr Application of a novel hybrid machine learning algorithm in shallow landslide susceptibility mapping in a mountainous area
title_full_unstemmed Application of a novel hybrid machine learning algorithm in shallow landslide susceptibility mapping in a mountainous area
title_sort application of a novel hybrid machine learning algorithm in shallow landslide susceptibility mapping in a mountainous area
publisher Frontiers Media S.A.
publishDate 2022
url http://eprints.utm.my/104043/1/AnuarAhmad2022_ApplicationofaNovelHybridMachineLearning.pdf
http://eprints.utm.my/104043/
http://dx.doi.org/10.3389/fenvs.2022.897254
_version_ 1789424369958649856
score 13.211869

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