Cite
Deep learning and boosting framework for piping erosion susceptibility modeling: spatial evaluation of agricultural areas in the semi-arid region.
MLA
Chen, Yunzhi, et al. “Deep Learning and Boosting Framework for Piping Erosion Susceptibility Modeling: Spatial Evaluation of Agricultural Areas in the Semi-Arid Region.” Geocarto International, vol. 37, no. 16, Aug. 2022, pp. 4628–54. EBSCOhost, https://doi.org/10.1080/10106049.2021.1892212.
APA
Chen, Y., Chen, W., Janizadeh, S., Bhunia, G. S., Bera, A., Pham, Q. B., Linh, N. T. T., Balogun, A.-L., & Wang, X. (2022). Deep learning and boosting framework for piping erosion susceptibility modeling: spatial evaluation of agricultural areas in the semi-arid region. Geocarto International, 37(16), 4628–4654. https://doi.org/10.1080/10106049.2021.1892212
Chicago
Chen, Yunzhi, Wei Chen, Saeid Janizadeh, Gouri Sankar Bhunia, Amit Bera, Quoc Bao Pham, Nguyen Thi Thuy Linh, Abdul-Lateef Balogun, and Xiaojing Wang. 2022. “Deep Learning and Boosting Framework for Piping Erosion Susceptibility Modeling: Spatial Evaluation of Agricultural Areas in the Semi-Arid Region.” Geocarto International 37 (16): 4628–54. doi:10.1080/10106049.2021.1892212.