Temperature Forecast Using Machine Learning
Author:
Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-47672-3_2
Reference18 articles.
1. Yuan, B., Cui, Y., Wang, X., Xu, H.: Study on the heterogeneity of China’s agricultural economic growth in the context of temperature shocks. Sci. Rep. 12(1), 10511 (2022). https://doi.org/10.1038/s41598-022-11877-3
2. Wu, J., Xiong, J., Dai, H., Wang, Y., Xu, C.: MIX-RS: a multi-indexing system based on HDFS for remote sensing data storage. Tsinghua Sci. Technol. 27(6), 881–893 (2022). https://doi.org/10.26599/TST.2021.9010082
3. Papadopoulos, T., Balta, M.E.: Climate change and big data analytics: challenges and opportunities. Int. J. Inf. Manag. 63, 102448 (2022). https://doi.org/10.1016/j.ijinfomgt.2021.102448
4. Jdi, H., Falih, N.: Weather forecast using sliding window algorithm based on Hadoop and MapReduce. In: Advances in Information, Communication and Cybersecurity, pp. 122–132. Cham (2022). https://doi.org/10.1007/978-3-030-91738-8_12
5. Peng, H., Qian, J., Kong, F., Fan, D., Shao, P., Wu, Z.: Enhancing firefly algorithm with sliding window for continuous optimization problems. Neural Comput. Appl. 34(16), 13733–13756 (2022). https://doi.org/10.1007/s00521-022-07193-6
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