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Comparison of Regression Models for Estimating Ventilation Rate of Mechanically Ventilated Swine Farm

강제환기식 돈사의 환기량 추정을 위한 회귀모델의 비교

  • Jo, Gwanggon (Division of Animal Environment, National Institute of Animal Science (NIAS)) ;
  • Ha, Taehwan (Division of Animal Environment, National Institute of Animal Science (NIAS)) ;
  • Yoon, Sanghoo (Division of Mathematics and Big Data Science, Daegu University) ;
  • Jang, Yuna (Division of Animal Environment, National Institute of Animal Science (NIAS)) ;
  • Jung, Minwoong (Division of Animal Environment, National Institute of Animal Science (NIAS))
  • Received : 2019.11.20
  • Accepted : 2019.12.02
  • Published : 2020.01.31

Abstract

To estimate the ventilation volume of mechanically ventilated swine farms, various regression models were applied, and errors were compared to select the regression model that can best simulate actual data. Linear regression, linear spline, polynomial regression (degrees 2 and 3), logistic curve, generalized additive model (GAM), and gompertz curve were compared. Overfitting models were excluded even when the error rate was small. The evaluation criteria were root mean square error (RMSE) and mean absolute percentage error (MAPE). The evaluation results indicated that degree 3 exhibited the lowest error rate; however, an overestimation contradiction was observed in a certain section. The logistic curve was the most stable and superior to all the models. In the estimation of ventilation volume by all of the models, the estimated ventilation volume of the logistic curve was the smallest except for the model with a large error rate and the overestimated model.

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