An innovative machine learning model for short term weather prediction based on radar data
Andrei Mihai - Teaching Assistant @ Computer Science, UBB
9th November, 17:30-18:00
This presentation introduces the innovative AutoNowP machine learning model for short-term weather prediction. Also known as nowcasting, short-term weather forecasts are focused on extreme weather events that represent a danger to life and property. The proposed model employs data obtained from WSR-98D meteorological radars deployed in Romania and predicts the values of future radar products such as reflectivity or wind speed using two auto encoders. This is done as a classification, meaning that, for a specific location, it is predicted if the radar value at that location will be higher or lower than a certain threshold. We introduce short-term weather forecasting focused on extreme meteorological phenomena and present the radar data set as well as the data preprocessing required for using the model. We show the innovative AutoNowP model and detail its performance when compared to other classical machine learning models.
Computer Science, UBB
I work as a teaching assistant in the Department of Computer Science, Faculty of Mathematics and Computer Science from the Babeș-Bolyai University of Cluj-Napoca, Romania. I received my PhD degree in Computer Science in 2021 focusing on machine learning models for weather nowcasting. My teaching activity covers Fundamentals of Programming, Algorithms and Programming, Object-Oriented Programming, and Data Structures and Algorithms. My main research interests are in the fields of Machine Learning and Computational Intelligence.