APA (7th ed.) Citation

Shin, J., Ro, Y., Cha, J., Kim, K., & Ha, J. Assessing the Applicability of Random Forest, Stochastic Gradient Boosted Model, and Extreme Learning Machine Methods to the Quantitative Precipitation Estimation of the Radar Data: A Case Study to Gwangdeoksan Radar, South Korea, in 2018. Wiley.

Chicago Style (17th ed.) Citation

Shin, Ju-Young, Yonghun Ro, Joo-Wan Cha, Kyu-Rang Kim, and Jong-Chul Ha. Assessing the Applicability of Random Forest, Stochastic Gradient Boosted Model, and Extreme Learning Machine Methods to the Quantitative Precipitation Estimation of the Radar Data: A Case Study to Gwangdeoksan Radar, South Korea, in 2018. Wiley.

MLA (9th ed.) Citation

Shin, Ju-Young, et al. Assessing the Applicability of Random Forest, Stochastic Gradient Boosted Model, and Extreme Learning Machine Methods to the Quantitative Precipitation Estimation of the Radar Data: A Case Study to Gwangdeoksan Radar, South Korea, in 2018. Wiley.