Performance evaluation of ensemble learning techniques for landslide susceptibility mapping at the Jinping county, Southwest China

Xudong Hu, Hongbo Mei (), Han Zhang, Yuanyuan Li and Mengdi Li
Additional contact information
Xudong Hu: China University of Geosciences
Hongbo Mei: China University of Geosciences
Han Zhang: China University of Geosciences
Yuanyuan Li: China University of Geosciences
Mengdi Li: China University of Geosciences

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2021, vol. 105, issue 2, No 23, 1663-1689

Abstract: Abstract The objective of this study is to investigate different ensemble learning techniques namely Bagging, Boosting, and Stacking for LSM at the Jinping county, Southwest China. Two well-known machine learning classifiers such as C4.5 decision tree (C4.5) and artificial neural network (ANN) were served as base-learners. A total of five ensemble models, including the Bag-C4.5 model, the Boost-C4.5 model, the Bag-ANN model, the Boost-ANN model, and the Stacking C4.5-ANN model, were constructed by using various ensemble techniques and base-learners. A landslide inventory map and 12 landslide-related factors have been prepared as the spatial database for landslide modeling. The importance of factors was verified using the information gain (IG) method. It turns out that the distance to roads has the greatest contribution to landslide susceptibility assessment. Subsequently, various landslide models were evaluated regarding the goodness of fit, generalization capability, and robustness. The area under the ROC curve (AUC), statistical analysis, and stability index (SI) were used as performance metrics. Evaluation results showed that ensemble learning techniques significantly refined individual landslide models such as the C4.5 (AUC = 0.832) and ANN (AUC = 0.870). In particular, Boosting-based models, e.g., the Boost-C4.5 model (AUC = 0.945) and the Boost-ANN model (AUC = 0.903), gained a higher performance than the Stacking C4.5-ANN model (AUC = 0.900), the Bag-ANN (AUC = 0.892), and the Bag-C4.5 (AUC = 0.878). Additionally, the best modeling robustness was achieved by the Stacking C4.5-ANN method (SI = 1). The results indicate that the Boosting technique has great confidence in strengthening the predictive accuracy for LSM. Also, the Stacking can provide a promising method for stable and improved landslide modeling. Findings from this study may assist to refine the quality of LSM and facilitate risk management for the study area or other similar regions.

Keywords: Landslides; Artificial neural network; Decision tree; Bagging; Boosting; Stacking (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://link.springer.com/10.1007/s11069-020-04371-4 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:nathaz:v:105:y:2021:i:2:d:10.1007_s11069-020-04371-4

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11069

DOI: 10.1007/s11069-020-04371-4

Access Statistics for this article

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards is currently edited by Thomas Glade, Tad S. Murty and Vladimír Schenk

More articles in Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards from Springer, International Society for the Prevention and Mitigation of Natural Hazards
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().