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Journal Article

Enhancing landslide susceptibility modelling through a novel non-landslide sampling method and ensemble learning technique

Authors
/persons/resource/zhouchao

Zhou,  Chao
1.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Wang,  Yue
External Organizations;

Cao,  Ying
External Organizations;

Singh,  Ramesh P.
External Organizations;

Ahmed,  Bayes
External Organizations;

/persons/resource/motagh

Motagh,  M.
1.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Wang,  Yang
External Organizations;

Chen,  Ling
External Organizations;

Tan,  Guangchao
External Organizations;

Li,  Shanshan
External Organizations;

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Fulltext (public)

5025707.pdf
(Publisher version), 5MB

Supplementary Material (public)
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Citation

Zhou, C., Wang, Y., Cao, Y., Singh, R. P., Ahmed, B., Motagh, M., Wang, Y., Chen, L., Tan, G., Li, S. (2024): Enhancing landslide susceptibility modelling through a novel non-landslide sampling method and ensemble learning technique. - Geocarto International, 39, 1, 2327463.
https://doi.org/10.1080/10106049.2024.2327463


Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5025707
Abstract
In recent years, several catastrophic landslide events have been observed throughout the globe, threatening to lives and infrastructures. To minimize the impact of landslides, the need of landslide susceptibility map is important. The study aims to extract high-quality non-landslide samples and improve the accuracy of landslide susceptibility modelling (LSM) outcomes by applying a coupled method of ensemble learning and Machine Learning (ML). The Zigui-Badong section of the Three Gorges Reservoir area (TGRA) in China was considered in the present study. Twelve influencing factors were selected as inputs for LSM, and the relationship between each causal factor and landslide spatial development was quantitatively analyzed. A total of 179 landslides have been used in the present study. About 70% of the landslide pixels were randomly considered for training, and the remaining 30% were used for validation. Logistic Regression (LR) model was applied to produce an initial susceptibility map, and the non-landslide samples were selected within the classified low-susceptibility zone. Subsequently, two ML classifiers – the Classification and Regression Tree (CART), and the Multi-Layer Perceptron (MLP), and four coupling models – the CART-Bagging, CART-Boosting, MLP-Bagging, and MLP-Boosting, were utilized for LSM. Finally, the receiver operating characteristics (ROC) curve and statistical analysis were applied for accuracy assessment. The results show that altitude and distance to rivers were the main causal factors of landslides in the study area. The LR-MLP-Boosting performed the best with an accuracy of 0.986 followed by the LR-CART-Bagging, LR-CART-Boosting, and LR-MLP-Bagging. Accuracy comparisons demonstrate that ensemble learning algorithm can notably enhance the LSM performance of ML classifiers, and the Boosting algorithm marginally outperforms the Bagging algorithm. Moreover, the LR model can effectively constrain the selection range of non-landslide samples. The non-landslide sampling method constrained by LR yields higher quality samples compared to raditional random sampling method with no constraints, which develops a more excellent LSM.