Internet of Things for Seismographs using Spline Interpolation

This study focused on revolutionizing earthquake monitoring through the utilization of IoT-based seismographs, harnessing the power of Internet of Things (IoT) technology for real-time data collection. Despite the promising potential of these devices, technical glitches often lead to data gaps, posing significant challenges in seismic data analysis. To mitigate this issue, the study implemented cubic spline interpolation to effectively fill the missing data points, and a rigorous comparison with quadratic interpolation was undertaken to gauge accuracy levels. The results underscore the success of cubic spline interpolation in closely mimicking the seismic data pattern. Although slight deviations from the original data were noted, the method exhibited an impressively low Root Mean Square Error (RMSE) of 0.033902 and Mean Square Error (MSE) of 0.001149, highlighting its prowess in addressing incomplete or missing data in seismic recordings. Conversely, another result illustrates the outcomes of quadratic interpolation, demonstrating its ability to approximate the seismic data pattern more accurately than cubic spline. However, similar minor deviations were observed, with an RMSE of 0.033721 and MSE of 0.001137. These findings illuminate the significance of cubic spline interpolation in enhancing earthquake monitoring techniques, showcasing its immense potential in seismic data analysis and paving the way for more robust and accurate earthquake predictions in the future.