Uncertainties in landslide susceptibility prediction modeling: A review on the incompleteness of landslide inventory and its influence rules

Abstract

Landslide inventory is an indispensable output variable of landslide susceptibility prediction (LSP) modelling. However, the influence of landslide inventory incompleteness on LSP and the transfer rules of LSP resulting error in the model have not been explored. Adopting Xunwu County, China, as an example, the existing landslide inventory is first obtained and assumed to contain all landslide inventory samples under ideal conditions, after which different landslide inventory sample missing conditions are simulated by random sampling. It includes the condition that the landslide inventory samples in the whole study area are missing randomly at the proportions of 10%, 20%, 30%, 40% and 50%, as well as the condition that the landslide inventory samples in the south of Xunwu County are missing in aggregation. Then, five machine learning models, namely, Random Forest (RF), and Support Vector Machine (SVM), are used to perform LSP. Finally, the LSP results are evaluated to analyze the LSP uncertainties under various conditions. In addition, this study introduces various interpretability methods of machine learning model to explore the changes in the decision basis of the RF model under various conditions. Results show that (1) randomly missing landslide inventory samples at certain proportions (10%–50%) may affect the LSP results for local areas. (2) Aggregation of missing landslide inventory samples may cause significant biases in LSP, particularly in areas where samples are missing. (3) When 50% of landslide samples are missing (either randomly or aggregated), the changes in the decision basis of the RF model are mainly manifested in two aspects: first, the importance ranking of environmental factors slightly differs; second, in regard to LSP modelling in the same test grid unit, the weights of individual model factors may drastically vary.

Keywords

LSP / Landslide susceptibility prediction / LSI / landslide susceptibility index / LR / Logistic regression / RF / Random forest / SVM / Support vector machine / BPNN / Backpropagation neural network / LSTM / Long short-term memory / LIME / Local Interpretable Model-agnostic Explainer / SHAP / SHapley Additive Explanations / PDP / Partial dependence plot / NDVI / Normalized Difference Vegetation Index / NDBI / Normalized Difference Built-up Index / TWI / Topographic Wetness Index / MNDWI / Modified Normalized Difference Water Index / ROC / Receiver operation characteristic / AUC / Area under the receiver operating characteristic curve

CRediT authorship contribution statement

Faming Huang: Conceptualization, Supervision. Daxiong Mao: Writing – original draft. Shui-Hua Jiang: Writing – review & editing. Chuangbing Zhou: Conceptualization. Xuanmei Fan: Methodology. Ziqiang Zeng: Software, Validation. Filippo Catani: Conceptualization. Changshi Yu: Software, Validation. Zhilu Chang: Data curation. Jinsong Huang: Conceptualization, Supervision. Bingchen Jiang: Investigation. Yijing Li: Visualization, Supervision.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This research is funded by the National Natural Science Foundation of China (Nos. 42377164, 41972280 and 42272326), National Natural Science Outstanding Youth Foundation of China (No. 52222905), Natural Science Foundation of Jiangxi Province, China (No. 20232BAB204091) and Natural Science Foundation of Jiangxi Province, China (No. 20232BAB204077).

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