1. Adnan, R.M., Liang, Z., Kuriqi, A., Kisi, O., Malik, A., Li, B. and Mortazavizadeh, F. (2021) - Air temperature prediction using different machine learning models, Indonesian Journal of Electrical Engineering and Computer Science 22 (1), 534-541. DOI: 10.11591/ijeecs.v22.i1
2. Antor, A.F. and Wollega, E.D. (2020) - Comparison of machine learning algorithms for wind speed prediction, Proceedings of the 5th NA International Conference on Industrial Engineering and Operations Management, Detroit, Michigan, USA, August 10 - 14, 2020, pp. 1-8. © IEOM Society International.
3. Aronica, S., et al. (2022) - The i-waveNet project and the integrated sea wave measurements in the Mediterranean sea, 2022 IEEE International Workshop on Metrology for the Sea; Learning to Measure Sea Health Parameters (MetroSea), Milazzo, Italy, 2022, pp. 484-487. DOI: 10.1109/MetroSea55331.2022.9950876
4. Azzopardi, J., Zammit, A. and Gauci, A. (2024) - The i-waveNET Decision Support System – user-driven aggregations and analysis of forecasts and observations, Proceedings of the International Conference on Marine Data and Information Systems: IMDIS 2024, pp. 137-138. DOI: 10.13127/MISC/80
5. Bochenek, B. and Ustrnul, Z. (2022) - Machine learning in weather prediction and climate analyses—Applications and perspectives, Atmosphere 13 (2), 180. DOI: 10.3390/atmos13020180
6. Copernicus Climate Change Service (C3S) (2017) - ERA5 Reanalysis (Hourly Data on Single Levels) from 1979 to present, Climate Data Store (CDS), European Centre for Medium-Range Weather Forecasts (ECMWF). Retrieved from https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels.
7. Frnda, J., Ďurica, M., Nedoma, J., Žabka, S., Martínek, R. and Kostelanský, M. (2019) - A weather forecast model accuracy analysis and ECMWF enhancement proposal by neural network, Sensors 19 (23), 5144. DOI: 10.3390/s19235144
8. Kreuzer, D., Münz, M. and Schlüter, S. (2020) - Short-term temperature forecasts using a convolutional neural network—An application to different weather stations in Germany, Machine Learning with Applications 1, 100007. DOI: 10.1016/j.mlwa.2020.100007
9. Nezhad, E.F., Ghalhari, G.F. and Bayatani, F. (2019) - Forecasting maximum seasonal temperature using artificial neural networks: Tehran case study, Asia-Pacific Journal of Atmospheric Sciences 55 (1), 97-109. DOI: 10.1007/s13143-018-0051-x
10. Pati, N., Gourisaria, M.K., Das, H. and Banik, D. (2023) - Wind speed prediction using machine learning techniques, Proceedings of the 2023 11th International Conference on Emerging Trends in Engineering & Technology - Signal and Information Processing (ICETET - SIP), Nagpur, India, 2023, pp. 1-6. DOI: 10.1109/ICETET-SIP58143.2023.10151597
11. Roy, D.S. (2020) - Forecasting the air temperature at a weather station using deep neural networks, Procedia Computer Science 170, 392-399. DOI: 10.1016/j.procs.2020.11.005
12. Salman, A.G., Kanigoro, B. and Heryadi, Y. (2015) - Weather forecasting using deep learning techniques, Proceedings of the International Conference on Advanced Computer Science and Information Systems (ICACSIS), IEEE, pp. 281-285. DOI: 10.1109/icacsis.2015.7415154
13. Samuel, G.G., Sankar, P., Samuel, A., Edwin, P. and Manikandan, J. (2021) - Improved prediction of wind speed using machine learning, Journal of Physics: Conference Series 1964, 052005. DOI: 10.1088/1742-6596/1964/5/052005
14. Şener, U., Kılıç, B.İ., Tokgözlü, A., Aslan, Z. (2023). Prediction of Wind Speed by Using Machine Learning. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2023 Workshops. ICCSA 2023. Lecture Notes in Computer Science, vol 14104. Springer, Cham. DOI: 10.1007/978-3-031-37105-9_6
15. Singh, Nitin, Saurabh Chaturvedi and Shamim Akhter. “Weather Forecasting Using Machine Learning Algorithm.” 2019 International Conference on Signal Processing and Communication (ICSC) (2019): 171-174.
16. Singh, S., Kaushik, M., Gupta, A. and Malviya, A.K. (2019) - Weather forecasting using machine learning techniques, Proceedings of the 2nd International Conference on Advanced Computing and Software Engineering (ICACSE), 2019. DOI: 10.2139/ssrn.3350281
17. Schiller, J., Prokhorenkova, L., Seleznev, S., and Bischofberger, J. (2023) - Weather forecasting using deep learning methods: A comprehensive review, arXiv preprint arXiv:2310.08278. DOI: 10.48550/arXiv.2310.08278
18. Zhang, Z. and Dong, Y. (2020) - Temperature forecasting via convolutional recurrent neural networks based on time-series data, Complexity 2020, 3536572. DOI: 10.1155/2020/3536572

Chapter Information

Bibliographic Information