A Bibliometric Review of Earthquake and Machine Learning Research

Abstract

This article presents a bibliometric review of earthquake research and its integration with machine learning techniques. Over the past two decades, there has been a growing interest in using machine learning to enhance earthquake prediction and research. The review collected 1172 scholarly articles from the Web of Science database, focusing on the keywords "earthquake" and "machine learning." Machine learning has shown promise in improving earthquake forecasting models and aiding decision-making in disaster management, infrastructure design, and emergency response. However, it is noted that the application of machine learning in earthquake engineering is still in its early stages and requires further exploration. Key findings of this review include the increasing importance of certain keywords in earthquake and machine learning research, such as "prediction," "neural network," "classification," "logistic regression," and "performance." These keywords highlight the central areas of research focus within this field. The review also identifies research trends and gaps, including the need for more exploration of large-scale, high-dimensional, nonlinear, non-stationary, and heterogeneous spatiotemporal data in earthquake engineering. It emphasizes the necessity for novel machine learning algorithms tailored specifically for earthquake prediction and analysis. Furthermore, it highlights the need for addressing uncertainty in earthquake research and improving forecasting models. The review underscores the growth in interest and collaboration in earthquake research and machine learning, evident in the increasing number of scholarly contributions over the years. In summary, this bibliometric review highlights the importance of accurate forecasting and the potential of machine learning techniques in advancing this field.