Preprocessing of Meteorological Data for Training an Artificial Intelligence Model ; Підготовка метеорологічних даних для навчання моделі штучного інтелекту
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| Název: | Preprocessing of Meteorological Data for Training an Artificial Intelligence Model ; Підготовка метеорологічних даних для навчання моделі штучного інтелекту |
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| Autoři: | Петренко, Тетяна Григорівна, Задорожний, Антон Юрійович |
| Zdroj: | Information and control systems at railway transport; Vol. 29 No. 4 (2024): Information and control systems at railway transport; 39-48 ; Информационно-управляющие системы на железнодорожном транспорте; Том 29 № 4 (2024): Інформаційно-керуючі системи на залізничному транспорті; 39-48 ; Інформаційно-керуючі системи на залізничному транспорті; Том 29 № 4 (2024): Інформаційно-керуючі системи на залізничному транспорті; 39-48 ; 2413-3833 ; 1681-4886 |
| Informace o vydavateli: | Ukrainian State University of Railway Transport |
| Rok vydání: | 2024 |
| Sbírka: | Scientific Periodicals of Ukraine (Ukrainian Research and Academic Network) / Наукова періодика України |
| Témata: | API метеоресурсів, попередня обробка метеоданих, статистичний аналіз, бібліотеки Python, MongoDB Atlas, weather API, weather data preprocessing, statistical analysis, Python libraries |
| Popis: | Forecasting weather conditions by classical methods is now successfully supplemented by artificial intelligence methods that allow processing unstructured, semi-structured and structured data. The article considers and analyzes such sources of semistructured weather data as open APIs of weather resources. An approach to preparing weather data as data for training a graph neural network for forecasting weather data is proposed. The ability to obtain JSON objects with weather parameters (temperature, humidity, pressure, wind speed) by processing HTTP requests using the created Python program and the Requests library ensured the execution of the first stage of data preprocessing. The properties of the selected weather resources and the approach to preparing data for use are described. After receiving weather data from 10 different weather resource APIs, the data are combined and structured. Cleaning, normalization, aggregation and analysis of additional properties of the collected weather data are performed. For example, the calculation of statistical characteristics of the weather indicator "temperature" using Python tools is given. The article justifies the use of the MongoDB Atlas database to store unstructured meteorological data as objects such as images, audio, video, document files, and other file formats. MongoDB Atlas supports a document-oriented format that increases the flexibility and scalability of data management, which is an advantage for processing large and complex meteorological datasets used in training a graph neural network. The proposed approach combines preprocessing and data storage into a single structure, ensuring the completeness and representativeness of meteorological data. This integration increases the reliability of weather forecasts by using a variety of data. Research confirms the advantages of using MongoDB Atlas and a graph neural network together in capturing spatial and temporal relationships in meteorological data. ; Прогнозування метеоумов за допомогою сучасних моделей ... |
| Druh dokumentu: | article in journal/newspaper |
| Popis souboru: | application/pdf |
| Jazyk: | Ukrainian |
| Relation: | http://jiks.kart.edu.ua/article/view/320375/310946; http://jiks.kart.edu.ua/article/view/320375 |
| Dostupnost: | http://jiks.kart.edu.ua/article/view/320375 |
| Rights: | http://creativecommons.org/licenses/by-nc-nd/4.0 |
| Přístupové číslo: | edsbas.BC66092D |
| Databáze: | BASE |
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