###### Abstract  
Earthquakes induce landslides worldwide every year that may cause massive fatalities and financial losses. Precise and timely landslide susceptibility mapping (LSM) is significant for landslide hazard assessment and mitigation in earthquake-affected areas. State-of-the-art LSM approaches connect causative factors from various sources without considering the fusion of different information at the data modal level. To exploit the complementary information of different modalities and boost LSM accuracy, this study presents a new LSM model that integrates data modality and machine learning methods. The presented method first groups causative factors into different modal types based on their intrinsic characteristics, followed by the calculation of the pairwise similarity of modal data. The similarities of different modalities are fused using nonlinear graph fusion to generate a unified graph, which is subsequently classified using different machine learning methods to produce final LSM. Experimental results suggest that the presented method achieves higher performance than existing LSM methods. This study provides a new solution for producing precise LSM from a fusion perspective that can be applied to minimize the potential landslide risk and for sustainable use of erosion-prone slopes. s 2022 29 January 2022 29 January 2022 8 February 2022 29 January 2022 8 February 2022 8 January limit-equilibrium to analyze the slope stability [17; 18]. For instance, [PERSON] et al. [19] proposed a three-dimensional limit equilibrium slope stability model that is robust and capable to deal with shallow and deep landslides. They applied this method to assess slope stability in Umbria, central Italy, and it yielded satisfactory results. [PERSON] et al. [20] integrated hydrogeological and slope stability models to identify potential landslides. Though this kind of method does not need a landslide inventory, it requires exhaustive geotechnical and engineering geological data that are challenging and expensive to obtain. Thus, physically-based methods are restricted to assessing landslide susceptibility at the site-specific scale, and are unsuitable for regional/global scale. The knowledge-driven methods are based on expert knowledge on the contribution of various causative factors to landslides. In general, experts provide detailed descriptions on causative factors within the landscape and the contribution of each factor to landslides. Such knowledge is then assimilated into a certain knowledge-driven model to produce a landslide susceptibility score for each location (e.g., each pixel) as a weighted sum of the contribution by various causative factors. Representative approaches for knowledge-driven LSM include analytic hierarchy process (AHP) [21] and fuzzy logic (FR) assessment [6]. In the AHP approach, a comparison matrix is constructed to represent the expert judgment on pairwise relative importance of the causative factors for landslides. The weight of each causative factor for the landslide susceptibility score is then derived from the comparison matrix. In the FR approach, a fuzzy membership function for each causative factor is constructed to represent expert opinions on its contribution to landslides. The landslide susceptibility score is then generated for every pixel by integrating the fuzzy membership values of various causative factors. Although the knowledge-driven methods are simple and efficient, their results are sensitive to the correctness of expert knowledge. Accurate expert inputs into the knowledge-driven models, such as the comparison matrix in AHP and the fuzzy membership functions in the FR assessment, not only rely on extensive domain knowledge, but also are specific to regions with certain geological structures and climates. This limits the transferability of knowledge-driven models to other geographical regions and this limitation is almost similar to the physically-based methods.