Details

Autor(en) / Beteiligte

• Zhao, Fancheng
• Miao, Fasheng
• Wu, Yiping
• Ke, Chao
• Gong, Shunqi
• Ding, Yanming

Titel

Refined landslide susceptibility mapping in township area using ensemble machine learning method under dataset replenishment strategy

Ist Teil von

• Gondwana research, 2024-07, Vol.131, p.20-37

Ort / Verlag

Elsevier B.V

Erscheinungsjahr

2024

Link zum Volltext

Quelle

Elsevier ScienceDirect Journals Complete

Beschreibungen/Notizen

• [Display omitted] •Different types of landslides generate the corresponding impact factors.•Dataset replenishment strategy reduces uncertainty of landslide susceptibility.•SHAP and Apriori algorithm provide insights into factor contribution to landslides. Landslide susceptibility mapping (LSM) warrants considerable attention as a prerequisite for risk assessment and prevention in response to the increasing global occurrences of landslides. However, refined LSM at the township scale still faces the problem of the limited number of landslides and the oversight of relevant factors associated with different landslide types. This work is dedicated to propose a novel dataset replenishment strategy designed to address landslide sample scarcity, and to identify dominant factors in high landslide susceptibility zones to refine LSM at township scale for decision makers. Herein, the Wuling County in the Three Gorges Reservoir area of China was taken as taken as the research case. Initially, field investigation and calibration of 21 landslides yielded accumulation and rock areas, and the corresponding impact factors were therefore proposed to construct input features for machine learning facilitated by factor diagnosis using Pearson correlation coefficient (PCC) and multicollinearity test. Subsequently, a training and validation dataset split of 8: 2 was randomly generated, enabling the application of ensemble machine learning model for LSM prediction. The study culminated in the feasibility of dataset replenishment strategy, which was employed to explore uncertainties and identify governing factors in LSM. The results indicate that the bagging-ANN model exhibits superior performance in both accumulation and rock areas, with Areas Under Curve (AUC) of 0.987 and 0.994. Furthermore, the dataset replenishment strategy emerges as instrumental in enhancing the reliability of LSM, as evidenced by an 8.97 % reduction in average value (AVG) and a 11.00 % increase in standard deviation (STD). The governing factors for landslides in accumulation and rock areas are designated as river erosion and height, respectively. Together with the excellent performance, this study is expected to provide a promising reference for LSM in other townships worldwide.