RISK MANAGEMENTOF INDUSTRIESDURING CRISIS

Abstract

Automated classification of seismic signals is an essential task in the field of seismology, as it helps to identify and distinguish different types of seismic events, such as big earthquake, mild earthquake and noise (remaining earth motions). In recent years, there has been grow ing interest in the use of machine learning algorithms for automated classification, with one such approach being the use of Continuous Random Forests. This is an extension of the tra ditional Random Forest algorithm and are particularly well-suited to handling time-series data, such as seismic signals. They use a sliding window approach to capture temporal dependencies in the data and can be trained on large datasets to learn complex patterns and features that are indicative of specific types of seismic events. The application of Contin uous Random Forests in automated classification of seismic signals has shown promising results, with high levels of accuracy achieved in distinguishing between different types of seismic events. However, many previous works using Automated classification techniques for distinguishing different types of seismic events have achieved limited efficacy due to challenges such as limited dataset, data sets with limited and non-vital parameter as at tributes. In this work, we present an evaluation of seismic waves of earth motion on the basis of 59 vital attributes. After evaluating various machine learning (ML)Algorithm, we select Continuous Random Forests as the final algorithm, since it achieves high accuracy. Since the available datasets includes limited /non vital parameter as attributes, we have created a python code to create own datasets consisting of 59 vital parameters of seismic signal as attributes. This approach has also demonstrated the ability to generalize well to new datasets, suggesting that it may be applicable across a range of different contexts and environments. Furthermore, the use of automated classification methods such as Contin uous Random Forests has the potential to significantly improve the efficiency of seismic monitoring and analysis, as it reduces the need for manual classification and allows for the rapid detection and identification of seismic events. This can have important implica tions for disaster management and emergency response, as it enables rapid and accurate assessment of potential risks and hazards.