Improving pixel-based regional landslide susceptibility mapping

Regional landslide susceptibility mapping (LSM) is essential for risk mitigation. While deep learning algorithms are increasingly used in LSM, their extensive parameters and scarce labels (limited landslide records) pose training challenges. In contrast, classical statistical algorithms, with typically fewer parameters, are less likely to overfit, easier to train, and offer greater interpretability. Additionally, integrating physics -based and data -driven approaches can potentially improve LSM. This paper makes several contributions to enhance the practicality, interpretability, and cross -regional generalization ability of regional LSM models: (1) Two new hybrid models, composed of data -driven and physics -based modules, are proposed and compared. Hybrid Model I combines the infinite slope stability analysis (ISSA) with logistic regression, a classical statistical algorithm. Hybrid Model II integrates ISSA with a convolutional neural network, a representative of deep learning techniques. The physics -based module constructs a new explanatory factor with higher nonlinearity and reduces prediction uncertainty caused by incomplete landslide inventory by pre -selecting non -landslide samples. The data -driven module captures the relation between explanatory factors and landslide inventory. (2) A step -wise deletion process is proposed to assess the importance of explanatory factors and identify the minimum necessary factors required to maintain satisfactory model performance. (3) Single -pixel and local -area samples are compared to understand the effect of pixel spatial neighborhood. (4) The impact of nonlinearity in data -driven algorithms on hybrid model performance is explored. Typical landslide -prone regions in the Three Gorges Reservoir, China, are used as the study area. The results show that, in the testing region, by using local -area samples to account for pixel spatial neighborhoods, Hybrid Model I achieves roughly a 4.2% increase in the AUC. Furthermore, models with 30 m resolution land -cover data surpass those using 1000 m resolution data, showing a 5.5% improvement in AUC. The optimal set of explanatory factors includes elevation, land -cover type, and safety factor. These findings reveal the key elements to enhance regional LSM, offering valuable insights for LSM practices. (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/).