Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms

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Shallow Landslide Susceptibility Mapping : A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms

Nhu, Viet-Ha (författare): Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam. Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam

Shirzadi, Ataollah (författare): Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran

Shahabi, Himan (författare): Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran. Department of Zrebar Lake Environmental Research, Kurdistan Studies Institute, University of Kurdistan, Sanandaj, Iran

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Singh, Sushant K. (författare): Virtusa Corporation, Irvington, USA

Al-Ansari, Nadhir, 1947- (författare): Luleå tekniska universitet,Geoteknologi

Clague, John J. (författare): Department of Earth Sciences, Simon Fraser University, Burnaby, BC, Canada

Jaafari, Abolfazl (författare): Research Institute of Forests and Rangelands, Agricultural Research, Education, and Extension Organization (AREEO), Tehran, Iran

Chen, Wei (författare): College of Geology & Environment, Xi’an University of Science and Technology, Xi’an, China. Key Laboratory of Coal Resources Exploration and Comprehensive Utilization, Ministry of Natural Resources, Xi’an, China

Miraki, Shaghayegh (författare): Department of Watershed Sciences Engineering, Faculty of Natural Resources, University of Agricultural Science and Natural Resources of Sari, Mazandaran, Iran

Dou, Jie (författare): Department of Civil and Environmental Engineering, Nagaoka University of Technology, Kami-Tomioka, Nagaoka, Niigata, Japan

Luu, Chinh (författare): Faculty of Hydraulic Engineering, National University of Civil Engineering, Hanoi , Vietnam

Górski, Krzysztof (författare): Faculty of Mechanical Engineering, Kazimierz Pulaski University of Technology and Humanities in Radom, Radom, Poland

Pham, Binh Thai (författare): Institute of Research and Development, Duy Tan University, Da Nang, Vietnam

Nguyen, Huu Duy (författare): Faculty of Geography, VNU University of Science, Ha Noi, Vietnam

Ahmad, Baharin Bin (författare): Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia

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Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran (creator_code:org_t)

2020-04-16
2020
Engelska.
Ingår i: International Journal of Environmental Research and Public Health. - Switzerland : MDPI. - 1661-7827 .- 1660-4601. ; 17:8, s. 1-30

Relaterad länk:: https://ltu.diva-por... (primary) (Raw object); visa fler...; https://www.mdpi.com...; https://urn.kb.se/re...; https://doi.org/10.3...; visa färre...

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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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Shallow Landslide Susceptibility Mapping : A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms

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