Affiliations 

  • 1 Department of Watershed Sciences Engineering, Faculty of Natural Resources, University of Agricultural Science and Natural Resources of Sari, Sari P.O. Box 48181-68984, Iran. a.shirzadi@uok.ac.ir
  • 2 Department of Watershed Sciences Engineering, Faculty of Natural Resources, University of Agricultural Science and Natural Resources of Sari, Sari P.O. Box 48181-68984, Iran. solaimani2001@yahoo.co.uk
  • 3 Department of Watershed Sciences Engineering, Faculty of Natural Resources, University of Agricultural Science and Natural Resources of Sari, Sari P.O. Box 48181-68984, Iran. roshanbah@yahoo.com
  • 4 Department of Watershed Sciences Engineering, Faculty of Natural Resources, University of Agricultural Science and Natural Resources of Sari, Sari P.O. Box 48181-68984, Iran. ataollah.kavian@yahoo.com
  • 5 Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran. k.chapi@uok.ac.ir
  • 6 Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran. h.shahabi@uok.ac.ir
  • 7 College of Geology & Environment, Xi'an University of Science and Technology, Xi'an 710054, China. chenwei0930@yahoo.com
  • 8 Department of Watershed Sciences Engineering, Faculty of Natural Resources, University of Agricultural Science and Natural Resources of Sari, Sari P.O. Box 48181-68984, Iran. khabat.khosravi@gmail.com
  • 9 Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam. phambinhgtvt@gmail.com
  • 10 Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, University of Technology Sydney, Sydney, NSW 2007, Australia. Biswajeet.Pradhan@uts.edu.au
  • 11 Department of Geoinformation, Faculty of Geoinformation and Real Estate, Universiti Teknologi Malaysia (UTM), Skudai 81310, Malaysia. anuarahmad@utm.my
  • 12 Department of Geoinformation, Faculty of Geoinformation and Real Estate, Universiti Teknologi Malaysia (UTM), Skudai 81310, Malaysia. baharinahmad@utm.my
  • 13 Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam. buitiendieu@tdt.edu.vn
Sensors (Basel), 2018 Nov 05;18(11).
PMID: 30400627 DOI: 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.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.