A Comparative Analysis of the Use of Deep Learning and Machine Learning in Weather Forecasting : Using Meteorological Dataset on Vaasa

Abstract

This study presents a comparative analysis of two prominent technologies, namely deep learning, and machine learning, in the context of weather forecasting. The main research question is “How can machine learning and deep learning algorithm be implemented to obtain near-accurate weather forecasting”?

The objectives of this research are identifying the fundamental differences between deep learning and machine learning algorithms handling weather-related dataset and to ascertain the accuracy of using deep learning as compared to machine learning in weather forecasting. The study begins by providing a detailed overview of deep learning and machine learning techniques, explaining their fundamental principles, and highlighting their respective imple-mentation in weather dataset.

In addition, the focus of the research is on the application of technologies such as polynomial regression, gradient boosting, neural prophet, and recurrent neural network models to the process of weather forecasting. The study applied quantitative methodology and used an open-source dataset from Finnish Meteorological Institute which is a weather record collect-ed from the city of Vaasa. The comparative analysis involves employing those techniques to capture nonlinear relationships between weather variables and the pattern within the dataset. Moreover, the study investigates the performance of each technology and evaluates its effectiveness in forecasting weather conditions over different interval of time using performance evaluation matrices.

The outcomes of the comparative analysis provide valuable insights into the application of recent machine learning and deep learning methods with regard to the quality and the amount of data applied for the process. This includes proper implementation of data pre-processing techniques, that significantly impact the accuracy of models.