Application of fractional-order differential and ensemble learning to predict soil organic matter from hyperspectra-Reference-Cited by-同舟云学术

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Application of fractional-order differential and ensemble learning to predict soil organic matter from hyperspectra

Published:2023-09-09 Issue:1 Volume:24 Page:361-372
ISSN:1439-0108
Container-title:Journal of Soils and Sediments
language:en
Short-container-title:J Soils Sediments

Author:

Liu Ya,Zhang Yuewen,Lu Huizhong,Yang Yong,Xie Jingying,Chen Danyan^ORCID

Funder

High-level Scientific Research Foundation for the introduction of talent of Jinling Institute of Technology

Foundation of Jiangsu College students Provincial Normal Innovation Training Program

Research Incubation Project of the Jinling Institute of Technology

Research Foundation of Nanjing Hydraulic Research Institute

Key Research and Development Project of Jiangsu Province

Publisher

Springer Science and Business Media LLC

Subject

Stratigraphy,Earth-Surface Processes

Link

https://link.springer.com/content/pdf/10.1007/s11368-023-03647-z.pdf

Reference48 articles.

1. Abulaiti Y, Sawut M, Maimaitiaili B, Ma CY (2020) A possible fractional order derivative and optimized spectral indices for assessing total nitrogen content in cotton. Comput Electron Agr 171:105275. https://doi.org/10.1016/j.compag.2020.105275

2. Breiman L (2001) Random forests. Machine Learning 45:5–32. https://link.springer.com/article/10.1023/A:1010933404324

3. Chen TQ, Guestrin C (2016) XGBoost: a scalable tree boosting system. arXiv - CS - Machine Learning. 785–794. https://doi.org/10.48550/arXiv.1603.02754

4. Diwu PY, Bian XH, Wang ZF, Liu W (2019) Study on the selection of spectral preprocessing methods. Spectrosc Spect Anal 39(09):2800–2806. https://doi.org/10.3964/j.issn.1000-0593(2019)09-2800-07

5. Dong Z, Wang N, Liu J, Xie J, Han J (2021) Combination of machine learning and VIRS for predicting soil organic matter. J Soils Sediments 21:2578–2588. https://doi.org/10.1007/s11368-021-02977-0

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