Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms
by
Viet-Ha Nhu 1,2
, Ataollah Shirzadi 3, Himan Shahabi 4,5, Sushant K. Singh 6, 
Nadhir Al-Ansari 7,*, 
John J. Clague 8, Abolfazl Jaafari 9, Wei Chen 10,11, Shaghayegh Miraki 12, Jie Dou 13, Chinh Luu 14, Krzysztof Górski 15, Binh Thai Pham 16,*, Huu Duy Nguyen 17 and Baharin Bin Ahmad 18
Viet-Ha Nhu 1,2, Ataollah Shirzadi 3, Himan Shahabi 4,5, Sushant K. Singh 6, Nadhir Al-Ansari 7,*, John J. Clague 8, Abolfazl Jaafari 9, Wei Chen 10,11, Shaghayegh Miraki 12, Jie Dou 13, Chinh Luu 14, Krzysztof Górski 15, Binh Thai Pham 16,*, Huu Duy Nguyen 17 and Baharin Bin Ahmad 18
1
Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City 758307, Vietnam
2
Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City 758307, Vietnam
3
Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran
4
Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran
5
Board Member of Department of Zrebar Lake Environmental Research, Kurdistan Studies Institute, University of Kurdistan, Sanandaj 66177-15175, Iran
6
Virtusa Corporation, 10 Marshall Street, Irvington, NJ 07111, USA
7
Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, 971 87 Lulea, Sweden
8
Department of Earth Sciences, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
9
Research Institute of Forests and Rangelands, Agricultural Research, Education, and Extension Organization (AREEO), Tehran 13185-116, Iran
10
College of Geology & Environment, Xi’an University of Science and Technology, Xi’an 710054, China
11
Key Laboratory of Coal Resources Exploration and Comprehensive Utilization, Ministry of Natural Resources, Xi’an 710021, China
12
Department of Watershed Sciences Engineering, Faculty of Natural Resources, University of Agricultural Science and Natural Resources of Sari, Mazandaran 48181-68984, Iran
13
Department of Civil and Environmental Engineering, Nagaoka University of Technology, 1603-1, Kami-Tomioka, Nagaoka, Niigata 940-2188, Japan
14
Faculty of Hydraulic Engineering, National University of Civil Engineering, Hanoi 112000, Vietnam
15
Faculty of Mechanical Engineering, Kazimierz Pulaski University of Technology and Humanities in Radom, Chrobrego 45 Street, 26-200 Radom, Poland
16
Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
17
Faculty of Geography, VNU University of Science, 334 Nguyen Trai, Ha Noi 100000, Vietnam
18
Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia (UTM), Johor Bahru 81310, Malaysia
*
Authors to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(8), 2749; https://doi.org/10.3390/ijerph17082749
Received: 28 February 2020 / Revised: 9 April 2020 / Accepted: 13 April 2020 / Published: 16 April 2020
(This article belongs to the Special Issue Landslide Risk Assessment and Mitigation)
Shallow landslides damage buildings and other infrastructure, disrupt agriculture practices, and can cause social upheaval and loss of life. As a result, many scientists study the phenomenon, and some of them have focused on producing landslide susceptibility maps that can be used by land-use managers to reduce injury and damage. This paper contributes to this effort by comparing the power and effectiveness of five machine learning, benchmark algorithms—Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine—in creating a reliable shallow landslide susceptibility map for Bijar City in Kurdistan province, Iran. Twenty conditioning factors were applied to 111 shallow landslides and tested using the One-R attribute evaluation (ORAE) technique for modeling and validation processes. The performance of the models was assessed by statistical-based indexes including sensitivity, specificity, accuracy, mean absolute error (MAE), root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). Results indicate that all the five machine learning models performed well for shallow landslide susceptibility assessment, but the Logistic Model Tree model (AUC = 0.932) had the highest goodness-of-fit and prediction accuracy, followed by the Logistic Regression (AUC = 0.932), Naïve Bayes Tree (AUC = 0.864), ANN (AUC = 0.860), and Support Vector Machine (AUC = 0.834) models. Therefore, we recommend the use of the Logistic Model Tree model in shallow landslide mapping programs in semi-arid regions to help decision makers, planners, land-use managers, and government agencies mitigate the hazard and risk.
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Keywords:
Shallow landslide; artificial intelligence; prediction accuracy; logistic model tree; goodness-of-fit; Iran
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MDPI and ACS Style
Nhu, V.-H.; Shirzadi, A.; Shahabi, H.; Singh, S.K.; Al-Ansari, N.; Clague, J.J.; Jaafari, A.; Chen, W.; Miraki, S.; Dou, J.; Luu, C.; Górski, K.; Thai Pham, B.; Nguyen, H.D.; Ahmad, B.B. Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms. Int. J. Environ. Res. Public Health 2020, 17, 2749.
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