Improved landslide prediction by considering continuous and discrete spatial dependency

Landslide spatial prediction studies predominantly focus on estimating the likelihood of landslide occurrence by considering a set of geo-environmental factors. Nevertheless, most of these studies fail to account for the spatial dependency between landslide occurrences across different terrain units. This study explored how the understanding of spatial dependency can enhance predictions of landslide occurrence across the entire Three Gorges Reservoir area, China. Specifically, we develop spatial binomial generalized additive models (GAMs) that incorporate Duchon spline (DS) and Markov random field (MRF) functions to represent continuous and discrete spatial dependencies, respectively. To test the validity of our proposed models, we compare them against the common GAM model as well as two popular machine learning models: the support vector machine (SVM) and the random forest (RF). The experimental results reveal that our models achieve superior predictive performance, with scores ranging from 0.929 to 0.938, compared to the benchmark methods, which scored between 0.912 and 0.914, based on a tenfold cross-validation procedure. These results demonstrate that incorporating spatial dependency significantly enhances the performance of landslide susceptibility prediction. Moreover, the comparison between GAM-DS and GAM-MRF models prove that the continuous smoothing of spatial dependency offers a more detailed and precise representation of landslide susceptibility. We believe that our approach, which integrates spatial dependency, will lay the foundation for the landslide community to assess landslide susceptibility from a spatially informed perspective.