Abstract
Understanding the complex dynamics of global landslides is essential for disaster planners to make timely and effective decisions that save lives and reduce the economic impacts on society. Using NASA's inventory of global landslide data, we developed a new machine learning (ML)-based system for town planners, disaster recovery strategists, and landslide researchers. Our system revealed hidden knowledge about a range of complex scenarios created from five landslide feature attributes. Users of our system can select from a list of 1.295\times {10}^{64} possible global landslide scenarios to discover valuable knowledge and predictions about the selected scenario in an interactive manner. Three ML algorithms-anomaly detection, decomposition analysis, and automated regression analysis-are used to elicit detailed knowledge about 25 scenarios selected from 14,532 global landslide records covering 12,220 injuries and 63,573 fatalities across 157 countries. Anomaly detection, logistic regression, and decomposition analysis performed well for all scenarios under study, with the area under the curve averaging 0.951, 0.911, and 0.896, respectively. Moreover, the prediction accuracy of linear regression had a mean absolute percentage error of 0.255. To the best of our knowledge, our scenario-based ML knowledge discovery system is the first of its kind to provide a comprehensive understanding of global landslide data.