Landslide susceptibility mapping by attentional factorization machines considering feature interactions

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.

Automated summary

This study uses a new machine learning model- Attentional Factorization Machines (AFM)- to explicitly consider the influence of feature interactions in Landslide Susceptibility Mapping (LSM). The results show that the performance of AFM is slightly better than that of Random Forest (RF) model in terms of AUC metric. Compared with general LSM models, AFM not only ensures model interpretability but also improves model performance by introducing an attention mechanism to learn the weight of different feature combinations.