Pengesanan Denyutan Pi2 Hampir Masa Nyata Berdasarkan Model Pembelajaran Mesin Terautomatik

Abstract

The impact of geomagnetic substorms on space weather and various technology on the Earth urges the need to detect the phenomenon. However, the brief period of substorm occurrences makes it more difficult to directly detect them, therefore, their detection by proxy in the form of precursors need to be done. Pi2 pulsations, which is a damped irregular signal in the horizontal component of the geomagnetic field ( as a reliable precursor to substorms. The cessation of Hz), is seen index that previously was the main detection indicator of Pi2 pulsations motivates this study to introduce a new detection method by implementing machine learning. In this study, several features were extracted from geomagnetic field data based on several statistical parameters, impulsive and signal metrics to be used in the development of classification model. The development was performed based on the automated machine learning (AutoML) approach that determines the best algorithm and optimizes the hyperparameters automatically. AutoML tries various algorithms like ensemble, neural network, Naïve Bayes, support vector machine, k-nearest neighbour and binary decision tree that are optimized by Asynchronous Successive Halving Algorithm. An ensemble classification model was determined to be the best performing model with an accuracy of 99.15%. It was then used in the development of near-real tine detection system. The system streams the geomagnetic field data continuously and reports the Pi2 detections on an open cloud repository, The detection system is envisioned to be one of the reference sources to inquire the occurrences of Pi2 pulsations globally.