Machine Learning for Automatic Weather Stations: A Case Study
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-38344-1_6
Reference18 articles.
1. Molero, F., Barragán, R., Artíñano, B.: Estimation of the atmospheric boundary layer height by means of machine learning techniques using ground-level meteorological data. Atmos. Res. 279, 106401 (2022). https://doi.org/10.1016/j.atmosres.2022.106401
2. Feng, Y., Cui, N., Hao, W., Gao, L., Gong, D.: Estimation of soil temperature from meteorological data using different machine learning models. Geoderma 338, 67–77 (2019). https://doi.org/10.1016/j.geoderma.2018.11.044
3. Bouras, E.H., et al.: Cereal yield forecasting with satellite drought-based indices, weather data and regional climate indices using machine learning in Morocco. Remote Sens. 13(16), 3101 (2021). https://doi.org/10.3390/rs13163101
4. Algorithms for Intelligent Systems;SA Shetty,2021
5. Maulud, D., Abdulazeez, A.M.: A review on linear regression comprehensive in machine learning. J. Appl. Sci. Technol. Trends 1(4), 140–147 (2020). https://doi.org/10.38094/jastt1457
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