A Framework for Imbalanced Modelling in Disaster Management: A Case Study Involving Global Landslide Susceptibility

Abstract

Abstract This paper proposes a modelling framework for imbalanced problems in the field of disaster management. Global landslide susceptibility was used as a case study. After investigating metrics for imbalanced classifiers, six metrics were selected: AUC, F1, Precision, Recall, G-mean and Kappa. A comparison was made between methods in the imbalanced learning domain and commonly used strategies in the disaster domain. Ten supervised learning classifiers were built, and the extra Tree classifier outperformed other classifiers according to the post hoc test. The ET classifier built by the SMOTE & ENN hybrid sampling dataset outperformed the other classifiers, and the AUC and F1 were 0.9533 and 0.1049, respectively, on the five validation sets. Such a result indicates that the model has strong robustness and outstanding performance. It was found that the imbalanced learning framework can significantly improve the performance of disaster classifiers even at a global scale.