Journal
GEOMATICS NATURAL HAZARDS & RISK
Volume 12, Issue 1, Pages 1837-1861Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/19475705.2021.1950217
Keywords
Landslide susceptibility mapping; machine learning; random forest; feature interaction; attentional factorization machine (AFM)
Funding
- National Natural Science Foundation of China [41902291]
- Natural Science Foundation of Hunan Province, China [2020JJ5704]
- Hunan Provincial Innovation Foundation for Postgraduate [CX20200236]
- Fundamental Research Funds for Central South University [2020zzts651]
- Open Research Fund Program of Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring (Central South University), Ministry of Education [2017YSJS11]
- Scientific Research Program of the Hunan Education Department [19C1113]
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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.
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.
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