A Comparative Analysis of the Use of Deep Learning and Machine Learning in Weather Forecasting - Using Meteorological Dataset on Vaasa - Master’s Thesis

Aug 3, 2026

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This study presents a comparative analysis of deep learning and machine learning methods for weather forecasting using a meteorological dataset from the city of Vaasa. The research asks how machine learning and deep learning algorithms can be implemented to achieve near-accurate forecasts. It identifies key differences in handling weather-related data, reviews core principles, and applies polynomial regression, gradient boosting, NeuralProphet, and recurrent neural network models. A quantitative methodology evaluates performance across different time intervals using standard evaluation matrices, emphasizing the influence of data pre-processing on model accuracy.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages49

Reading Time2 h 3 min

AccessFree to download

Introduction

Background of the study

Research Gap, Questions and Objectives

Definitions and Limitations

Research process

Structure of the study

Review Literatures

Machine Learning for Weather forecasting

Deep Learning for weather forecasting

Methodology

Research Design

Data Preprocessing

Model Selection

Performance Evaluation

Research result and Analysis

Analysis of the dataset

Analysis of Model results

Conclusion

Key findings

Conclusions

Wubshet Solomon A Comparative Analysis of the Use of Deep Learning and Machine Learning in Weather Forecasting: Using Meteorological Dataset on Vaasa School of Technology and Innovations Master’s thesis in Industrial Systems Analytics UNIVERSITY OF VAASA School of Technology and Innovation Author: Wubshet Solomon Title of the thesis: A Comparative Analysis of the Use of Deep Learning and Machine Learning in Weather Forecasting: Using Meteorological Dataset on Vaasa Degree: Master of Science in Technology Programme: Industrial System Analytics Supervisor: Assistant Professor Emmanuel Ndzibah Year: 2023 Pages: 50 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 nearaccurate 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 implementation 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 collected 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. KEYWORDS: Deep Learning, Machine Learning, Weather Forecasting, Contents 1 Introduction 6 1.1 Background of the study 6 1.2 Research Gap, Questions and Objectives 7 1.3 Definitions and Limitations 9 1.4 Research process 11 1.5 Structure of the study 13 2 Review Literatures 15 2.1 Machine Learning for Weather forecasting 16 2.1.1 Supervised Learning for weather for forcastng 17 2.1.2 Unsupervised Learning for weather forecasting 19 2.2 Deep Learning for weather forecasting 21 2.2.1 Neural Networks applications in weather forecasting 22 3 Methodology 26 3.1 Research Design 26 3.2 Data Preprocessing 27 3.3 Model Selection 29 3.3.1 Polynomial Regression for weather forecasting 29 3.3.2 Gradient Boosting for weather forecasting 30 3.3.3 Recurrent NN (RNN) for weather forecasting 31 3.3.3 NeuralProphet application in weather forecastng 33 3.4 Performance Evaluation 35 4 Research result and Analysis 38 4.1 Analysis of the dataset 38 4.2 Analysis of of Model results 41 5.Conclusion 45 5.1 Key findings 45 5.2 Conclusions 46 Reference 47 List of Figures Figure 1. Research Process ............................................................................................. 12 Figure 2 Study Structure ................................................................................................. 14 Figure 3 Flow chart ..................................................................

FAQs

What is the main research question of the thesis?

How can machine learning and deep learning algorithms be implemented to obtain near-accurate weather forecasting?

Which machine learning and deep learning models are used in the study?

The study applies polynomial regression, gradient boosting, recurrent neural network (RNN) models, and NeuralProphet for weather forecasting.

How is model performance evaluated in the research?

Performance is assessed using evaluation matrices while comparing forecasting effectiveness over different time intervals, with emphasis on the impact of data pre-processing on accuracy.

Aug 3, 2026

Upload by Patrick