Landslide susceptibility evaluation considering sample sensitivity

doi:10.13474/j.cnki.11-2246.2022.0319

Abstract

Abstract: As natural geological phenomena of involving great danger, landslides have seriously threatened people's lives and property. Therefore, it is very important to predict the susceptibility of landslide scientifically and accurately. With the development of machine learning, the prediction of landslide susceptibility based on machine learning has become a research hotspot. But in the real situation, the area ratio of non-landslide and landslide area is very large, which makes the application of machine learning model exist serious sample imbalance problem. In order to obtain the most balanced landslide sample set, the performance of multiple machine learning models on different proportion of positive and negative landslide sample set is analyzed. The multigraded cascade forest model is trained on this sample set and used to predict the landslide susceptibility in the study area. The final prediction results are close to the real distribution, which shows that the method presented in this paper is effective.

Key words: landslide susceptibility, sample sensitivity analysis, machine learning, deep random forest

CLC Number:

P237

Lü Beiru, PENG Ling, LI Qiaomin. Landslide susceptibility evaluation considering sample sensitivity[J]. Bulletin of Surveying and Mapping, 2022, 0(11): 20-25.

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