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Damage to infrastructure, human casualties, and the economy can all take a nosedive when an earthquake strikes, making it one of the deadliest and most unpredictable natural disasters. Factors such as height, ground motion, rainfall, rock bed material, and area tectonics all have a role in their occurrence. Geologists and seismologists still have a significant obstacle when trying to predict when, where, and how big of an earthquake will be. Using data preprocessing techniques like transformation and cleaning in conjunction with advanced feature extraction methods based on precursory patterns, this study presents an Earthquake Prediction System. The system expands upon previous research that found precursory indicators like changes in animal behaviour, increasing temperatures, radon gas emissions, and variations in seismicity. Particular attention was given to the LSSVM-FPA hybrid model, which integrates the LS-SVM and the FPA, in the development of two predictive models that make use of seismic indicators and hybrid ML techniques. When compared to other models, the LSSVM-FPA model utperforms them all with a prediction accuracy of 97.43%. In light of these findings, it is reasonable to conclude that the proposed Earthquake Prediction System provides a solid basis for enhancing early detection and reducing the devastating effects of future seismic events.
Earthquakes are among the most destructive natural disasters that can occur, impacting not only people but also economy and structures. A great deal of attention from seismologists has focused on the problem of earthquake prediction. The Earth's outer shell is not entirely static, though, therefore it is still regarded as an extremely complex subject today. The earlier approaches to earthquake prediction relied on established information, statistical regularities, signal analysis, and historical earthquake occurrences however, these methods are relatively less accurate. Building cohesive models that can be used to create precise forecasting systems is particularly difficult due to the variances in geology and the uncertainty of the forces at play[1]. Data loss during earthquake monitoring can occur for a number of reasons, including malfunctioning instruments and communication systems. This problem is reflected in seismic monitoring data when there are continuous missing values in numerous fields or when solitary values arise. This will significantly affect data analysis and business applications in the future. Maintenance work cannot be finished quickly because of the intricacy of the geographic dispersion of earthquake monitoring stations and the unpredictability of hardware equipment replacement[2]. Thus, it is a practical and significant issue to determine the ways to employ technical methods to fill in the missing data and guarantee data integrity during the time when the monitoring station cannot function normally. Earthquake monitoring stations differ substantially in terms of the hardware they use and the data they monitor.