A novel CGBoost deep learning algorithm for coseismic landslide susceptibility prediction

The accurate prediction of landslide susceptibility shortly after a violent earthquake is quite vital to the emergency rescue in the 72-h "golden window". However, the limited quantity of interpreted landslides shortly after a massive earthquake makes landslide susceptibility prediction become a challenge. To address this gap, this work suggests an integrated method of Crossing Graph attention network and xgBoost (CGBoost). This method contains three branches, which extract the interrelations among pixels within a slope unit, the interrelations among various slope units, and the relevance between influencing factors and landslide probability, respectively, and obtain rich and discriminative features by an adaptive fusion mechanism. Thus, the difficulty of susceptibility modeling under a small number of coseismic landslides can be reduced. As a basic module of CGBoost, the proposed Crossing graph attention network (Crossgat) could characterize the spatial heterogeneity within and among slope units to reduce the false alarm in the susceptibility results. Moreover, the rainfall dynamic factors are utilized as prediction indices to improve the susceptibility performance, and the prediction index set is established by terrain, geology, human activity, environment, meteorology, and earthquake factors. CGBoost is applied to predict landslide susceptibility in the Gorkha meizoseismal area. 3.43% of coseismic landslides are randomly selected, of which 70% are used for training, and the others for testing. In the testing set, the values of Overall Accuracy, Precision, Recall, F1-score, and Kappa coefficient of CGBoost attain 0.9800, 0.9577, 0.9999, 0.9784, and 0.9598, respectively. Validated by all the coseismic landslides, CGBoost outperforms the current major landslide susceptibility assessment methods. The suggested CGBoost can be also applied to landslide susceptibility prediction in new earthquakes in the future.(c) 2023 China University of Geosciences (Beijing) and Peking University. Published by Elsevier B.V. on behalf of China University of Geosciences (Beijing). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).