Landslide susceptibility mapping by attentional factorization machines considering feature interactions

Geomatics, Natural Hazards & Risk

Lei-Lei Liu¹  Can Yang¹  Fa-Ming Huang²  Xiao-Mi Wang³ 

[1] Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, School of Geosciences and Info-Physics, Central South University;School of Civil and Architecture, Nanchang University;School of Resources and Environmental Science, Hunan Normal University;

关键词: landslide susceptibility mapping;    machine learning;    random forest;    feature interaction;    attentional factorization machine (afm).;   

DOI  :  10.1080/19475705.2021.1950217

来源: DOAJ

【 摘 要 】

Landslide susceptibility mapping (LSM) is a commonly used approach to reduce landslide risk. However, conventional LSM methods generally only consider the influence of each single conditioning factor on landslide occurrence or absence, which neglects the interactions of different conditioning factors and may lead to biased LSM results. Therefore, this study aims to use a new machine learning model—attentional factorization machines (AFM)—to explicitly consider the influence of feature interactions in LSM to improve and obtain more reliable LSM results. The Anhua County in China is chosen as the study area. The area under the receiver operating characteristic curve (AUC) and statistical indicators are used to evaluate the performance of LSM models. For comparison, the common LSM models such as the logistic regression (LR) and random forest (RF) models are also used to conduct the LSM. The results show that the performance of AFM is a little better than RF in the AUC metric, whereas the LR model has the worst performance. Compared with general LSM models, AFM considers feature interactions by introducing an attention mechanism to learn the weight of different feature combinations, which not only ensures the model interpretability but also improves the model performance.

【 授权许可】

Unknown