Next Article in Journal
New Dark Area Sensitive Tone Mapping for Deep Learning Based Traffic Sign Recognition
Next Article in Special Issue
Mapping Soil Alkalinity and Salinity in Northern Songnen Plain, China with the HJ-1 Hyperspectral Imager Data and Partial Least Squares Regression
Previous Article in Journal
Application of Fiber Bragg Grating Acoustic Emission Sensors in Thin Polymer-Bonded Explosives
Previous Article in Special Issue
A Novel Hybrid Swarm Optimized Multilayer Neural Network for Spatial Prediction of Flash Floods in Tropical Areas Using Sentinel-1 SAR Imagery and Geospatial Data
Article Menu

Export Article

Open AccessArticle
Sensors 2018, 18(11), 3777; https://doi.org/10.3390/s18113777

Novel GIS Based Machine Learning Algorithms for Shallow Landslide Susceptibility Mapping

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
2
Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran
3
Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran
4
College of Geology & Environment, Xi’an University of Science and Technology, Xi’an 710054, China
5
Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
6
Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, University of Technology Sydney, Sydney, NSW 2007, Australia
7
Department of Energy and Mineral Resources Engineering, Choongmu-gwan, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Korea
8
Department of Geoinformation, Faculty of Geoinformation and Real Estate, Universiti Teknologi Malaysia (UTM), Skudai 81310, Malaysia
9
Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam
10
Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam
*
Author to whom correspondence should be addressed.
Received: 13 August 2018 / Revised: 25 September 2018 / Accepted: 16 October 2018 / Published: 5 November 2018
Full-Text   |   PDF [8120 KB, uploaded 5 November 2018]   |  

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. View Full-Text
Keywords: landslide; alternating decision tree; GIS; machine learning algorithms; Iran landslide; alternating decision tree; GIS; machine learning algorithms; Iran
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Shirzadi, A.; Soliamani, K.; Habibnejhad, M.; Kavian, A.; Chapi, K.; Shahabi, H.; Chen, W.; Khosravi, K.; Thai Pham, B.; Pradhan, B.; Ahmad, A.; Bin Ahmad, B.; Tien Bui, D. Novel GIS Based Machine Learning Algorithms for Shallow Landslide Susceptibility Mapping. Sensors 2018, 18, 3777.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top