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http://dx.doi.org/10.9720/kseg.2020.3.315

Prediction of Landslides and Determination of Its Variable Importance Using AutoML  

Nam, KoungHoon (Geoscience and Geoenvironmental Science, Shimane University)
Kim, Man-Il (Forest Engineering Research Institute, National Forestry Cooperative Federation)
Kwon, Oil (Road Infrastructure Project Team, Korea Institute of Civil Engineering and Building Technology)
Wang, Fawu (Department of Civil Engineering, Tongji University)
Jeong, Gyo-Cheol (Department of Earth and Environmental Sciences, Andong National University)
Publication Information
The Journal of Engineering Geology / v.30, no.3, 2020 , pp. 315-325 More about this Journal
Abstract
This study was performed to develop a model to predict landslides and determine the variable importance of landslides susceptibility factors based on the probabilistic prediction of landslides occurring on slopes along the road. Field survey data of 30,615 slopes from 2007 to 2020 in Korea were analyzed to develop a landslide prediction model. Of the total 131 variable factors, 17 topographic factors and 114 geological factors (including 89 bedrocks) were used to predict landslides. Automated machine learning (AutoML) was used to classify landslides and non-landslides. The verification results revealed that the best model, an extremely randomized tree (XRT) with excellent predictive performance, yielded 83.977% of prediction rates on test data. As a result of the analysis to determine the variable importance of the landslide susceptibility factors, it was composed of 10 topographic factors and 9 geological factors, which was presented as a percentage for each factor. This model was evaluated probabilistically and quantitatively for the likelihood of landslide occurrence by deriving the ranking of variable importance using only on-site survey data. It is considered that this model can provide a reliable basis for slope safety assessment through field surveys to decision-makers in the future.
Keywords
AutoML; XRT; landslide prediction model; probabilistic prediction; variable importance;
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