Abstract
Landslides have brought about serious human and economic losses worldwide. Modeling landslide susceptibility is an important technology to avoid the loss caused by landslide disasters. The aim of this study was to design three integrated models by combining the multilayer perceptron (MLP) model with three meta classifiers (Decorate, MultiboostAB, and Rotation Forest) for modeling landslide susceptibility. Yanshan County was selected as the case study. The multicollinearity diagnoses Spearman’s and ReliefF were applied to judge the proper factors for modeling landslide susceptibility, and the results demonstrate that plan curvature and topographic wetness index are not proper factors in Yanshan County; hence, these factors were not used. Based on several statistical indices, the three integrated models achieve better results than the MLP model. MultiboostAB-MLP is a more stable and effective model, and the three integrated models were better than the existing machine learning algorithm. The result demonstrates that the overall trend of change is stable using different sampling ratios between the training and validation data. Finally, the landslide susceptibility map produced by the three integrated models may be applied for land use management and building reconstruction.
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The authors are hankful to Arindam Basu, Editor-in-Chief of Bulletin of Engineering Geology and the Environment, and two anonymous reviewers whose valuable suggestions and comments have greatly improved the quality of the paper.
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Hong, H. Assessing landslide susceptibility based on hybrid multilayer perceptron with ensemble learning. Bull Eng Geol Environ 82, 382 (2023). https://doi.org/10.1007/s10064-023-03409-8
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DOI: https://doi.org/10.1007/s10064-023-03409-8