期刊
REMOTE SENSING
卷 13, 期 8, 页码 -出版社
MDPI
DOI: 10.3390/rs13081464
关键词
landslide susceptibility; unsupervised machine learning; supervised machine learning; hybrid model; geographic information system
类别
资金
- Graduate Innovation Fund of Jilin University [101832020CX232]
- National Natural Science Foundation of China [41972267, 41977221, 41572257]
This study employed a hybrid model that utilized the advantages of both supervised and unsupervised learning, and through a two-stage modeling process, constructed a robust landslide prediction model with improved performance.
Landslides cause huge damage to social economy and human beings every year. Landslide susceptibility mapping (LSM) occupies an important position in land use and risk management. This study is to investigate a hybrid model which makes full use of the advantage of supervised learning model (SLM) and unsupervised learning model (ULM). Firstly, ten continuous variables were used to develop a ULM which consisted of factor analysis (FA) and k-means cluster for a preliminary landslide susceptibility map. Secondly, 351 landslides with 1 label were collected and the same number of non-landslide samples with 0 label were selected from the very low susceptibility area in the preliminary map, constituting a new priori condition for a SLM, and thirteen factors were used for the modeling of gradient boosting decision tree (GBDT) which represented for SLM. Finally, the performance of different models was verified using related indexes. The results showed that the performance of the pretreated GBDT model was improved with sensitivity, specificity, accuracy and the area under the curve (AUC) values of 88.60%, 92.59%, 90.60% and 0.976, respectively. It can be concluded that a pretreated model with strong robustness can be constructed by increasing the purity of samples.
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