Comparison of the presence-only method and presence-absence method in landslide susceptibility mapping

Presence-absence methods are widely-used data-driven models for landslide susceptibility mapping. Landslide absence data included in the training data of presence-absence methods is usually not available and has to be generated. In consideration of low availability and uncertain quality of landslide absence data, many presence-only methods which simply use landslide presence as training data were proposed to map landslide susceptibility. However, whether the presence-only methods can circumvent the influence of the shortcomings inherent to landslide absence data and perform better than presence-absence methods are worth studying. Moreover, the effect of landslide absence data in data-driven models for landslide susceptibility mapping can be discussed. In this study, two presence-only methods including one-class support vector machine (one-class SVM), kernel density estimation (KDE), and two presence-absence methods including artificial neural networks (ANN) and two-class support vector machine (two-class SVM) are developed and compared to evaluate their respective performance in mapping landslide susceptibility. The AUC values are 0.705, 0.720, 0.929, and 0.951 for one-class SVM, KDE, ANN, and two-class SVM, respectively. From the comparison of the four methods, two-class SVM has the best performance in landslide susceptibility mapping among the four methods, while one-class SVM has the worst. Two presence-absence methods can constrain the over-prediction of susceptibility value better and have better performance than the two presence-only methods since they classify less percentage of areas to be susceptible with more landslide occurrences located inside. The landslide absence data is proven to constrain the over-prediction of models, which makes it necessary in landslide susceptibility mapping.