Towards a Multimedia Big Data-Driven Approach for Earthquake Monitoring and Forecasting early warning system

Abstract

Digital network has fundamentally changed the way that people thought by enabling them to share their location and other personal information for the benefit of their communities. The popularity of geo-social networks (GN) like Instagram, Twitter, Facebook, and Flickr has increased significantly in recent years. As a result, everyone in the world may now express their opinions, immediately report an occurrence, and interact with others. GN data therefore gives comprehensive data on individual current developments. By evaluating geo-social data in real time, modern GN may be used as digital assets for countries and their governments. Hybrid lion optimized random forest (HLO-RF) is the proposed technique of this research. It seamlessly integrates the Lion Optimizing with random forest methods to improve complicated data-driven activities' forecasting accuracy. The novel technique promotes effective and resilient decision-making in several applications. In order to explore GN while gathering information and rendering decisions in real time while monitoring and forecasting various natural occurrences, we offer an effective system and Machine Learning (ML) technique in this research. To predict the early warning system using HLO-RF. To monitor earthquake occurrences, incidents in real time, make potential real-time decisions, and assist planning for the future, unique in a real-world setting, proposed system is design and used. We demonstrate that the proposed system has increased performance and can analyze a massive quantity of GN data in real time, while simultaneously identifying any occurrence.