In this paper, the workflow of the machine learning model development for the space weather forecast in the Earth’s ionosphere is presented, as an ongoing project. The problem of space weather forecasting using traditional approaches is discussed, as well as the advantages of using machine learning instead. In addition, the methods and approaches for building a machine learning model are presented, together with challenges related to data and algorithms. The machine learning workflow for the problem of space weather forecast is discussed from problem formulation and data acquisition, data preparation and feature engineering, learning algorithms, to model training, evaluation and deployment. This paper provides an overview of a machine learning project for space weather forecasting and discusses challenges and open issues.     «

In this paper, the workflow of the machine learning model development for the space weather forecast in the Earth’s ionosphere is presented, as an ongoing project. The problem of space weather forecasting using traditional approaches is discussed, as well as the advantages of using machine learning instead. In addition, the methods and approaches for building a machine learning model are presented, together with challenges related to data and algorithms. The machine learning workflow for the pro...     »