Gas concentration prediction based on ED-SLSTM model under the framework of Trend Prediction-Time Point Prediction
Author:
Funder
National Natural Science Foundation of China
Academic Funding Projects for Top Talents in Disciplines and Majors of Anhui
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s13042-024-02183-7.pdf
Reference31 articles.
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2. Liu F, Guo L, Zhao L (2022) Double carbon under the background of coal safety interval and green low carbon technology path. J Coal 47(01):1–15. https://doi.org/10.13225/j.carolcarrollnkiJCCS.Yg22.0016
3. Zhang Z, Zhu Q, Li Q, Liu Y, Zhang E, Zhao Q, Qin X (2021) Based on Keras length when the memory network of the mine gas concentration prediction research. J Saf Environ Eng 28(01):61–67. https://doi.org/10.1357/j.carolcarrollnki.issn1671-1556.2021.01.010
4. Jia P, Zhang Z, Liang R, Liu H, Miao Y (2021) Based on PSO–CNN–aBiGRU gas concentration prediction method. J Res Dev Min 9(12):76–81. https://doi.org/10.13827/j.carolcarrollnkikyyk.2021.12.002
5. Liu F (2021) Study on coal mine gas concentration prediction based on PCA-RVM. Inner Mong Coal Econ 23:70–72. https://doi.org/10.13487/j.carolcarrollnkiimce.021434
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