Cite
Evaluation of the Potential of Using Machine Learning and the Savitzky–Golay Filter to Estimate the Daily Soil Temperature in Gully Regions of the Chinese Loess Plateau.
MLA
Deng, Wei, et al. “Evaluation of the Potential of Using Machine Learning and the Savitzky–Golay Filter to Estimate the Daily Soil Temperature in Gully Regions of the Chinese Loess Plateau.” Agronomy, vol. 14, no. 4, Apr. 2024, p. 703. EBSCOhost, https://doi.org/10.3390/agronomy14040703.
APA
Deng, W., Liu, D., Guo, F., Zhang, L., Ma, L., Huang, Q., Li, Q., Ming, G., & Meng, X. (2024). Evaluation of the Potential of Using Machine Learning and the Savitzky–Golay Filter to Estimate the Daily Soil Temperature in Gully Regions of the Chinese Loess Plateau. Agronomy, 14(4), 703. https://doi.org/10.3390/agronomy14040703
Chicago
Deng, Wei, Dengfeng Liu, Fengnian Guo, Lianpeng Zhang, Lan Ma, Qiang Huang, Qiang Li, Guanghui Ming, and Xianmeng Meng. 2024. “Evaluation of the Potential of Using Machine Learning and the Savitzky–Golay Filter to Estimate the Daily Soil Temperature in Gully Regions of the Chinese Loess Plateau.” Agronomy 14 (4): 703. doi:10.3390/agronomy14040703.