Universiti Teknologi Malaysia Institutional Repository

Novel GIS based machine learning algorithms for shallow landslide susceptibility mapping

Shirzadi, A. and Soliamani, K. and Habibnejhad, M. and Kavian, A. and Chapi, K. and Shahabi, H. and Chen, W. and Khosravi, K. and Pham, B. T. and Pradhan, B. and Ahmad, A. and Ahmad, B. and Bui, D. T. (2018) Novel GIS based machine learning algorithms for shallow landslide susceptibility mapping. Sensors (Switzerland), 18 (11). ISSN 1424-8220

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Official URL: http://dx.doi.org/10.3390/s18113777

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.

Item Type:Article
Uncontrolled Keywords:Alternating decision tree, GIS, Iran, Landslide, Machine learning algorithms
Subjects:G Geography. Anthropology. Recreation > G Geography (General) > G70.212-70.215 Geographic information system
Divisions:Geoinformation and Real Estate
ID Code:79659
Deposited By: Fazli Masari
Deposited On:28 Jan 2019 12:58
Last Modified:28 Jan 2019 12:58

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