Next Article in Journal
Characteristics and Applications of the Ground-Based X Band Low Elevation Angle Brightness Temperatures under Low Sea State Based on Measured Data
Next Article in Special Issue
Automatic Mapping of Landslides by the ResU-Net
Previous Article in Journal
Assessing the Accuracy of Multiple Classification Algorithms for Crop Classification Using Landsat-8 and Sentinel-2 Data
Previous Article in Special Issue
Research on Post-Earthquake Landslide Extraction Algorithm Based on Improved U-Net Model
Open AccessArticle

Landslide Susceptibility Mapping: Machine and Ensemble Learning Based on Remote Sensing Big Data

1
RIKEN Center for Advanced Intelligence Project, Goal-Oriented Technology Research Group, Disaster Resilience Science Team, Tokyo 103-0027, Japan
2
Department of Mapping and Surveying, Darya Tarsim Consulting Engineers Co. Ltd., Tehran 15119-43943, Iran
3
Department of Forestry, Faculty of Natural Resources and Marine Sciences, Tarbiat Modares University, Tehran 15119-43943, Iran
4
Department. of Multimedia, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang, Selangor 45000, Malaysia
5
Global Ecology and ARC Centre of Excellence for Australian Biodiversity and Heritage, College of Science and Engineering, Flinders University, GPO Box 2100, Adelaide, SA 5001, Australia
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(11), 1737; https://doi.org/10.3390/rs12111737
Received: 15 April 2020 / Revised: 16 May 2020 / Accepted: 26 May 2020 / Published: 28 May 2020
Predicting landslide occurrences can be difficult. However, failure to do so can be catastrophic, causing unwanted tragedies such as property damage, community displacement, and human casualties. Research into landslide susceptibility mapping (LSM) attempts to alleviate such catastrophes through the identification of landslide prone areas. Computational modelling techniques have been successful in related disaster scenarios, which motivate this work to explore such modelling for LSM. In this research, the potential of supervised machine learning and ensemble learning is investigated. Firstly, the Flexible Discriminant Analysis (FDA) supervised learning algorithm is trained for LSM and compared against other algorithms that have been widely used for the same purpose, namely Generalized Logistic Models (GLM), Boosted Regression Trees (BRT or GBM), and Random Forest (RF). Next, an ensemble model consisting of all four algorithms is implemented to examine possible performance improvements. The dataset used to train and test all the algorithms consists of a landslide inventory map of 227 landslide locations. From these sources, 13 conditioning factors are extracted to be used in the models. Experimental evaluations are made based on True Skill Statistic (TSS), the Receiver Operation characteristic (ROC) curve and kappa index. The results show that the best TSS (0.6986), ROC (0.904) and kappa (0.6915) were obtained by the ensemble model. FDA on its own seems effective at modelling landslide susceptibility from multiple data sources, with performance comparable to GLM. However, it slightly underperforms when compared to GBM (BRT) and RF. RF seems most capable compared to GBM, GLM, and FDA, when dealing with all conditioning factors. View Full-Text
Keywords: big data; landslide susceptibility; flexible discriminant analysis; random forest; ensemble model big data; landslide susceptibility; flexible discriminant analysis; random forest; ensemble model
Show Figures

Graphical abstract

MDPI and ACS Style

Kalantar, B.; Ueda, N.; Saeidi, V.; Ahmadi, K.; Halin, A.A.; Shabani, F. Landslide Susceptibility Mapping: Machine and Ensemble Learning Based on Remote Sensing Big Data. Remote Sens. 2020, 12, 1737. https://doi.org/10.3390/rs12111737

AMA Style

Kalantar B, Ueda N, Saeidi V, Ahmadi K, Halin AA, Shabani F. Landslide Susceptibility Mapping: Machine and Ensemble Learning Based on Remote Sensing Big Data. Remote Sensing. 2020; 12(11):1737. https://doi.org/10.3390/rs12111737

Chicago/Turabian Style

Kalantar, Bahareh; Ueda, Naonori; Saeidi, Vahideh; Ahmadi, Kourosh; Halin, Alfian A.; Shabani, Farzin. 2020. "Landslide Susceptibility Mapping: Machine and Ensemble Learning Based on Remote Sensing Big Data" Remote Sens. 12, no. 11: 1737. https://doi.org/10.3390/rs12111737

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

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop