REVIEW OF MACHINE LEARNING ALGORITHMS FOR IT OPERATIONS
Manju Vyas, Anima Mishra Sharma, Deepika Bansal, Nayan Chaudhary · Journal of Analysis and Computations · 2024
In today's dynamic landscape, Earthquakes pose significant threats to human safety, infrastructure, and the environment. The accurate prediction of earthquakes is necessary for the development of early warning systems, disaster planning, risk assessment, and scientific research. This project aims to predict the magnitude and probability of Earthquakes occurring in a particular region from the historical data of that region using various Machine learning models. Early prediction of seismic events remains a challenging task, but advancements in machine learning and the availability of vast seismic datasets offer new opportunities for developing effective prediction models. In numerous applications and systems, it is crucial to find more efficient real-time detection of anomalies in time series data. ranging from intelligent transportation, structural health monitoring, heart disease, and earthquake prediction. Although the range of applications is wide, anomaly detection algorithms are usually domain-specific and build on experts' knowledge. This research paper proposes a comprehensive earthquake prediction model that integrates machine learning techniques with seismological data analysis.The model aims to improve accuracy, reliability, and timeliness in forecasting seismic events, thereby contributing to enhanced preparedness and mitigation strategies.Moreover, the discussion covers regional and global seismic data sets used, and tools employed, to predict earthquakes for different geographical regions.