1. ELARTU — Інституційний репозитарій ТНТУ імені Івана Пулюя
  2. Роботи студентів
  3. Магістр
  4. 126 — інформаційні системи та технології
Моля, използвайте този идентификатор за цитиране или линк към този публикация: http://elartu.tntu.edu.ua/handle/lib/51448
Заглавие: Machine Learning–Driven Weather Forecasting Integrating Advanced Data Analysis Methods
Автори: Valerie, Kasongo Bwanga
Affiliation: ТНТУ ім. І. Пулюя, Факультет комп’ютерно-інформаційних систем і програмної інженерії, Кафедра комп’ютерних наук, м. Тернопіль, Україна
Bibliographic description (Ukraine): Kasongo Bwanga V. Machine Learning–Driven Weather Forecasting Integrating Advanced Data Analysis Methods : Master’s qualification thesis in specialty 126 Information Systems and Technologies / supervisor O. Holotenko. — Ternopil : Ternopil Ivan Puluj National Technical University, 2026. — 97 p.
Bibliographic reference (2015): Kasongo Bwanga V. Machine Learning–Driven Weather Forecasting Integrating Advanced Data Analysis Methods: Master’s qualification thesis in specialty 126 Information Systems and Technologies / supervisor O. Holotenko. Ternopil: Ternopil Ivan Puluj National Technical University, 2026. 97 p.
Дата на Публикуване: 26-Яну-2026
Submitted date: 12-Яну-2026
Date of entry: 28-Яну-2026
Издател: ТНТУ ім. І.Пулюя, ФІС, м. Тернопіль, Україна
Country (code): UA
Place of the edition/event: Тернопіль
Supervisor: Голотенко, Олександр Сергійович
Holotenko, Olexander
Committee members: Ясній, Олег Петрович
Yasniy, Oleg
UDC: 004.85:551.509
Ключови Думи: 126
інформаційні системи та технології
аналіз даних
магістерська робота, машинне навчання, метеорологія, прогнозування погоди, часові ряди, data analysis, deep learning, machine learning, neural networks, time series forecasting, weather prediction
магістерська робота
машинне навчання
метеорологія
прогнозування погоди
часові ряди
data analysis
deep learning
machine learning
neural networks
time series forecasting
weather prediction
Page range: 97
Резюме: The qualification work is devoted to the development and research of machine learning models to improve the accuracy of short-term weather forecasting. The first chapter analyzes classical meteorological methods and modern approaches to processing large climate datasets. The second chapter focuses on the application of intelligent analysis methods, particularly time series analysis and dimensionality reduction techniques for input parameter preparation. The third chapter presents the results of training various neural network architectures, provides a comparative analysis of their effectiveness on real historical data, and estimates prediction errors. The findings demonstrate the high efficiency of machine learning methods in identifying complex non-linear patterns in meteorological data. The work also includes a section on occupational safety and the economic justification for the system's application
Описание: Роботу виконано на кафедрі комп'ютерних наук Тернопільського національного технічного університету імені Івана Пулюя. Захист відбудеться 26.01.2026р. на засіданні екзаменаційної комісії №37 у Тернопільському національному технічному університеті імені Івана Пулюя
Content: INTRODUCTION 1 THEORETICAL FOUNDATIONS OF WEATHER FORECASTING 1.1 Overview of traditional numerical weather prediction 1.2 Role of big data and AI in meteorology 1.3 Review of advanced data analysis techniques 2 METHODOLOGY AND DATA PREPROCESSING 2.1 Data sources and feature engineering for climate variables 2.2 Dimensionality reduction and time series decomposition 2.3 Selection of machine learning algorithms 3 MODEL IMPLEMENTATION AND COMPARATIVE ANALYSIS 3.1 Development of predictive models (SARIMA, Random Forest, Neural Networks) 3.2 Training, validation and error assessment 3.3 Discussion of results and accuracy improvements 4 ECONOMIC JUSTIFICATION OF THE PROPOSED SOLUTION 5 OCCUPATIONAL HEALTH AND SAFETY IN EMERGENCY SITUATIONS CONCLUSIONS REFERENCES
URI: http://elartu.tntu.edu.ua/handle/lib/51448
Copyright owner: © Valerie Kasongo Bwanga, 2026
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Content type: Master Thesis
Показва се в Колекции:126 — інформаційні системи та технології

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