## 5 Conclusions  
Landslide susceptibility mapping (LSM) is imperative to landslide-related disaster reduction and prevention and sustainable uses of slopes. Machine learning algorithms have been widely applied to LSM and many scholars designed various methods to boost LSM performance by combining results of multiple machine learning algorithms. Compared to fusion at the decision level, information fusion for LSM at the data modality level has been seldom reported. To address this issue, this paper exploits data modality to boost the LSM performance. Experimental results suggested that the data modality remarkably affects LSM accuracy, and the presented LSM model effectively integrates the complementary information of different data modalities and achieves more satisfactory results than mainstream LSM models without consideration of data modalities. This study provides new insight on the data modality applications to LSM. This study finds that the performance of machine learning algorithms (e.g., SVM, LR, KNN and RF used in this study) varies in accordance with topographic variables. Such results agree with those of state-of-the-art studies, and suggest that the patterns in causative factors for landslides are highly complex and variable for the different facets of causative factor attributes (e.g., spatial scale). This highlights the importance of considering data modality at the data and modeling level for LSM, as done by the proposed approach in this study. As shown in the experimental results, the proposed MMC approach can improve the accuracy of LSM when used with any of SVM, LR, KNN and RF. Alternatively, assembling the results from different machine learning algorithms at the decision level may further improve the reliability of LSM. The generation of training data in this study relies on a complete landslide inventory which is beneficial for generating a good amount of training data to get reliable LSM Extensive labelled training samples, however, are often a constraint as a complete landslide inventory after an earthquake is rarely available for a specific area in a very short time. In such circumstances, the selection of suitable machine learning algorithms is critical. If the incomplete landslide inventory cannot generate sufficient training samples or the number of causative factors is larger than that of training samples, statistical learning algorithms with high bias and low variance (e.g., linear regression, Naive Bayes, and linear SVM) would be recommended. Otherwise, low bias and high variance algorithms (e.g., KNN, decision trees, and kernel SVM) can be considered. Another direction is to develop suitable algorithms (e.g., pseudo training sample generation [49], mixed- effects models [50]) to reduce the influence of the inventory incompleteness on the statistical learning based LSM. The third direction is to exploit the potential of crowdsourcing data (e.g., website, social media) to improve the completeness and the effectiveness of the landslide inventory. This paper focuses on the multi-modal analysis of causative factors, but the high quality of input causative factor data required is also an important factor that cannot be ignored to produce satisfactory performance of LSM. The rapid development of new sensors undoubtedly provides new sources for high quality data on causative factors ([PERSON], et al., 2019). However, the higher resolution of the causative data does not necessarily lead to the high accuracy of LSM. The appropriate resolution needs to be further discussed. In addition, the current research directly uses causative factors, which lack the necessary inspection on quality. Therefore, the quality of causative factors requires in-depth analysis to control the influence of its inherent bias error on LSM. The dataset may have a large number of features that may not all be relevant and significant. For a certain data modality, the feature number may be larger compared to training data size.