| Citation: | DOU Hongqiang，LIU Yin，WANG Hao，et al. Susceptibility assessment and zoning of coastal landslides based on heterogeneous ensemble machine learning models[J]. Bulletin of Geological Science and Technology，2026，45（2）：1-14 doi: 10.19509/j.cnki.dzkq.tb20240567
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With the rapid development of marine engineering and the increasing frequency of extreme weather events, the risk of coastal landslides has increased significantly. However, existing studies on landslide susceptibility and zoning primarily focus on inland mountainous regions, and systematic research on coastal landslide susceptibility remains insufficient.
In this study, the coastal zone of Fujian Province was selected as the study area. Historical data on coastal landslides were collected, and a susceptibility assessment indicator system suitable for coastal landslides was established using the information gain ratio method and Pearson correlation coefficient method. Particle swarm optimization support vector machine (PSO-SVM) and random forest (RF) were used as base learners to construct a stacking heterogeneous ensemble learning model. This model was then used to conduct the susceptibility assessment and zoning of coastal landslides in Fujian Province, and the effects of different training-to-testing ratios on the prediction accuracy of the heterogeneous ensemble model were examined.
The comparison results demonstrated that the stacking model performed optimally when the training-to-testing ratio was 70:30, achieving an accuracy of 0.869, a precision of 0.842, a recall of 0.909, and an
The findings indicate that the stacking heterogeneous ensemble model exhibits strong applicability and superior performance in susceptibility assessment of coastal landslides.
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