An Evaluation Based Hypothetical Methodology to Connect Specific Animal Behaviors in Conditions of Identifying Earthquake Occurrence using Deep Learning mechanisms as a Strategy.

Abstract

Scientists all around the globe agree that it is possible for animals to sense changes in the environment before humans. In this study, we put forth a theoretical approach of a controlled study to connect specific animal behavior with earthquake occurrence using deep learning. Firstly, we identify the behavioral changes of animals at different stages and capture their heart rate, body temperature, vocalization and other vital data. Based on this data, integration of AI and predictive analysis with the combination of complex algorithms provide highly tailored results of population behavior pattern. If the research shows logical results, this method can be used by scientists to associate such signals with danger and give mankind a head starts on fleeing to safety, just like animals do and help evacuate disaster prone zones much more smoothly and effectively.