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Spatial prediction of shallow landslide: application of novel rotational forest-based reduced error pruning tree

Arabameri Department of Literature and Humanities, Tarbiat Modares University, Tehran, Iran|
Romulus (55888132500) Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Viet Nam| Hossein (55923628500); Costache Informetrics Research Group, Ton Duc Thang University, Ho Chi Minh City, Viet Nam| John P. (6602615383); Moayedi Department of Geography, Texas State University, San Marcos, TX, United States| Jagabandhu (57209537831); Tiefenbacher Department of Geoinformatics–Z_GIS, University of Salzburg, Salzburg, Austria| Omid (57200006495); Roy Department of Geography, University of Gour Banga, Malda, West Bengal, India| Sunil (57221551341); Ghorbanzadeh State Key Laboratory of Continental Dynamics, Department of Geology, Northwest University, Xi'an, China| M. (55110642200); Saha School of Earth Sciences and Resources, China University of Geosciences, Beijing, Beijing, China| Alireza (57193608494); Santosh Department of Earth Sciences, University of Adelaide, Adelaide, SA, Australia|

Geomatics, Natural Hazards and Risk Số 1, năm 2021 (Tập 12, trang 1343-1370)

ISSN: 19475705

ISSN: 19475705

DOI: 10.1080/19475705.2021.1914753

Tài liệu thuộc danh mục:

Article

English

Từ khóa: Artificial intelligence; Decision trees; Forestry; Open source software; Reliability analysis; Sustainable development; Golestan provinces; Hybrid artificial intelligences; Landslide susceptibility; Landslide susceptibility mapping; Receiver operating characteristic curves; Reduced-error pruning; Spatial prediction; Sustainable environmental planning; Landslides
Tóm tắt tiếng anh
Landslides are a form of soil erosion threatening the sustainability of some areas of the world. There is, therefore, a need to investigate landslide rates and behaviour. In this research, we introduced a novel hybrid artificial intelligence approach of rotation forest (RF) as a meta classifier based on reduced error pruning tree (REPTree) as a base classifier called RF-REPTree, for landslide susceptibility mapping (LSM) in the Kalaleh watershed, Golestan Province, Iran. Some benchmark models, including the open-source Java decision tree (J48), naive Bayes tree (NBTree), and REPTree were used to compare the designed model. A total of 249 landslide locations were identified and mapped. The group was split into training (70%) and testing (30%) data for modelling and reliability analysis. Based on a literature review and multi-collinearity tests, 16 landslide conditioning factors (LCFs) were selected. Of the LCFs, the topographical position index (TPI) had the highest correlation with landslide occurrence. The LSM produced by RF-REPTree revealed that nearly 29% of the study areas have high to very high landslide susceptibility (LS). Statistical analysis of the model results included the receiver operating characteristic curve (ROC), the efficiency test, the true skill statistic (TSS), and the kappa index. ROC demonstrated that the AUC values of RF-REPTree, REPTree, J48, and NBTree models were 0.832, 0.700, 0.695, and 0.759 for succession rate curves and 0.794, 0.740, 0.788, and 0.728 for prediction rate curves, respectively. Therefore, all models were judged to be acceptably accurate for LSM. Among the LS models, the RF-REPTree model achieved the highest accuracy, followed by REPTree, J48, and NBTree. The results of LSM can be used to target the mitigation of landslide hazards and provide a foundation for sustainable environmental planning. � 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

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