Development of Soil Moisture Model Based on Deep Learning
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-51904-8_105
Reference11 articles.
1. Brownlee, J. (2018). Deep learning for time series forecasting, 572.
2. Cai, Y., Zheng, W., Zhang, X., Zhangzhong, L., & Xue, X. (2019). Research on soil moisture prediction model based on deep learning. PLoS ONE, 14, e0214508. https://doi.org/10.1371/journal.pone.0214508
3. Carranza, C., Nolet, C., Pezij, M., & van der Ploeg, M. (2021). Root zone soil moisture estimation with Random Forest. Journal of Hydrology, 593, 125840. https://doi.org/10.1016/j.jhydrol.2020.125840
4. Cid-Liccardi, C. D., Kramer, T., Ashton, M. S., & Griscom, B. (2012). Managing carbon sequestration in tropical forests. In: Ashton, M. S., Tyrrell, M. L., Spalding, D., & Gentry, B. (Eds.), Managing forest carbon in a changing climate (pp. 183–204). Springer . https://doi.org/10.1007/978-94-007-2232-3_9
5. Civeira, G. (2019). Introductory chapter: Soil moisture. In: Civeira, G. (Ed.) Soil moisture. IntechOpen. https://doi.org/10.5772/intechopen.83603
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