A new approach based on integration of random subspace and C4.5 decision tree learning method for spatial prediction of shallow landslides

Viet-Ha Nhu; Tinh Thanh Bui; Linh Nguyen My; Hoe Vuong; Hoang Nhat Duc

doi:10.15625/2615-9783/16929

Author affiliations

Authors

Viet-Ha Nhu Department of Geological-Geotechnical Engineering, Hanoi University of Mining and Geology, Hanoi Vietnam
Tinh Thanh Bui Department of Prospecting and Exploration Geology, Hanoi University of Mining and Geology, Hanoi, Vietnam
Linh Nguyen My Department of Hydrogeoloy and Engineering Geology, Vietnam Institute of Geosciences and Mineral Resources, Thanh Xuan, Hanoi 100000, Vietnam
Hoe Vuong Faculty of Surveying, Mapping and Geographic Information, Hanoi university of Natural Resources and Environment, Vietnam
Hoang Nhat Duc Faculty of Civil Engineering, Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam

DOI:

https://doi.org/10.15625/2615-9783/16929

Keywords:

Landslide, random subspace, C4.5, GIS, Quang Ninh

Abstract

The research approaches a new machine learning ensemble which is a hybridization of Random subspace (RS) and C4.5, named RandSub-DT, for improving the performance of the landslide susceptibility model. This is based on the GIS database, including 170 landslide polygons and ten predisposing landslide factors, i.e., slope, aspect, curvature, TWI, land use, distance to road, distance to the river, soil type, distance to fault, and lithology. We carried out this study in the Halong and Cam Pha City areas which are important economic centers in the Quang Ninh province, Vietnam, where landslides seriously influence the daily life of the citizen causing economic damage. We then used a GIS database to construct and validate the proposed RandSub-DT model. The model performance was assessed using a confusion matrix and a set of statistical measures. The result showed that the RandSub-DT model with the classification accuracy of 90.34% in the training dataset and the prediction capability of 77.48% had a high performance for landslide prediction. This research proved that an ensemble of the C4.5 and RS provided a highly accurate estimate of landslide susceptibility in the research area.

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