Abstract

The main objective of this research was to introduce a novel machine learning algorithm of alternating decision tree (ADTree) based on the multiboost (MB), bagging (BA), rotation forest (RF) and random subspace (RS) ensemble algorithms under two scenarios of different sample sizes and raster resolutions for spatial prediction of shallow landslides around Bijar City, Kurdistan Province, Iran. The evaluation of modeling process was checked by some statistical measures and area under the receiver operating characteristic curve (AUROC). Results show that, for combination of sample sizes of 60%/40% and 70%/30% with a raster resolution of 10 m, the RS model, while, for 80%/20% and 90%/10% with a raster resolution of 20 m, the MB model obtained a high goodness-of-fit and prediction accuracy. The RS-ADTree and MB-ADTree ensemble models outperformed the ADTree model in two scenarios. Overall, MB-ADTree in sample size of 80%/20% with a resolution of 20 m (area under the curve (AUC) = 0.942) and sample size of 60%/40% with a resolution of 10 m (AUC = 0.845) had the highest and lowest prediction accuracy, respectively. The findings confirm that the newly proposed models are very promising alternative tools to assist planners and decision makers in the task of managing landslide prone areas.

Highlights

  • Landslides, which are very frequent natural hazards in mountainous regions, cause serious damages to economy and human lives

  • The results generally indicated that the combination of 70%/30% with raster resolution of 10 m had highest and the combination of 60%/40% with raster resolution of 20 m the lowest performance

  • 7e show that the prediction accuracy were 0.899 and 0.912 (Figure 7d)

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Summary

Introduction

Landslides, which are very frequent natural hazards in mountainous regions, cause serious damages to economy and human lives. Many methods have been developed and applied for spatially predicting landslides in recent years, which can be grouped into two main types, namely qualitative methods and quantitative methods [1]. Borrelli et al [5] used a bivariate statistical model for landslide modeling and achieved reasonable results and Ciurleo et al [6] and Cascini et al [7] compared heuristic, statistical and deterministic methods. Their results depicted that deterministic methods are slightly better than the other models. Analysis of past landslides may give useful data which can be used in methods based on physical modeling [8,9,10,11,12,13]

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