Incremental Versus Non-Incremental Learning in Volcano Monitoring Tasks: A Systematic Review

Since the beginning of planet Earth, volcanic activity has generated a series of events of great destructive capacity. Many populations located in areas close to volcanoes coexist with a complex combination of benefits and several risks. In order to provide early warning of an eventuality, different approaches have been developed to alert the inhabitants of these areas. Computer scientists have built and tested many automated tools based on Supervised Learning (SL) to solve this problem. However, despite the high potential of SL algorithms, current research does not consider the dynamic nature of volcanoes, which constantly change their baseline over time through interactions in the Earth's crust. In this study, a documentary review was developed around SL in volcanology. This study shows that Incremental Learning (IL) offers greater advantages than non-incremental learning for this type of application domains. One of them is that incremental learning considers dynamic systems and updates the objective function in real time without retraining the classifiers. Based on the above, this paper presents an updated review of the current literature examining different types of learning methods (incremental and non-incremental learning) in the volcano detecting task.