2020 Volume 1 Issue J1 Pages 242-251
In recent years, machine learning methods such as deep learning have evolved greatly in terms of performance, and they have been used for various purposes in disaster prevention. On the other hand, the intrinsic shortage of the number of data, the improvement of explanatory and interpretive nature of the task processing process are important issues that need to be addressed by computational models for decision making in disaster management. In this paper, we will discuss the concept, methods, and applications for addressing these two points based on research trends in the field of machine learning, and also introduce approaches that integrate mathematical and data-driven models to address these two issues.
