Genetic algorithm optimized a deep learning method with attention mechanism for soil moisture prediction
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-023-09168-7.pdf
Reference36 articles.
1. Yu Y, Wei W, Chen LD et al (2015) Responses of vertical soil moisture to rainfall pulses and land uses in a typical loess hilly area, China. Solid Earth 6:595–608. https://doi.org/10.5194/se-6-595-2015
2. Augenstein M, Goeppert N, Goldscheider N (2015) Characterizing soil water dynamics on steep hillslopes from long-term lysimeter data. J Hydrol Amst 529:795–804. https://doi.org/10.1016/j.jhydrol.2015.08.053
3. Roberts G, Penwell A, Peurou F, Sharpe A (2010) The effect of soil moisture content on nitrogen transformation using OECD test guideline 216. Appl Soil Ecol 46:478–482. https://doi.org/10.1016/j.apsoil.2010.09.003
4. Pacheco A, McNairn H, Mahmoodi A et al (2015) The impact of national land cover and soils data on SMOS soil moisture retrieval over canadian agricultural landscapes. IEEE J Sel Top Appl Earth Obs Remote Sens 8:5281–5293. https://doi.org/10.1109/JSTARS.2015.2417832
5. Fang K, Pan M, Shen C (2019) The value of SMAP for long-term soil moisture estimation with the help of deep learning. IEEE Trans Geosci Remote Sens 57:2221–2233. https://doi.org/10.1109/TGRS.2018.2872131
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