A Novel Combined Model for Predicting Humidity in Sheep Housing Facilities

SIMPLE SUMMARY: The hybrid model is proposed to predict humidity in sheep barns, based on a machine learning model combining a light gradient boosting machine with gray wolf optimization and support-vector regression (LightGBM–CGWO–SVR). Influencing factors with a high contribution to humidity were extracted using LightGBM to reduce the complexity of the model, and required hyperparameters in SVR were optimized, adopting the CGWO algorithm to avoid the local extremum problem. The experimental results indicated that the proposed LightGBM–CGWO–SVR model outperformed eight existing models used for comparison on all evaluation metrics; it achieved minimum values of 0.0662, 0.2284, 0.0521, and 0.0083, in terms of MAE, RMSE, MSE, and NRMSE, respectively, and a maximum value of 0.9973, in terms of the R(2) index. ABSTRACT: Accurately predicting humidity changes in sheep barns is important to ensure the healthy growth of the animals and to improve the economic returns of sheep farming. In this study, to address the limitations of conventional methods in establishing accurate mathematical models of dynamic changes in humidity in sheep barns, we propose a method to predict humidity in sheep barns based on a machine learning model combining a light gradient boosting machine with gray wolf optimization and support-vector regression (LightGBM–CGWO–SVR). Influencing factors with a high contribution to humidity were extracted using LightGBM to reduce the complexity of the model. To avoid the local extremum problem, the CGWO algorithm was used to optimize the required hyperparameters in SVR and determine the optimal hyperparameter combination. The combined algorithm was applied to predict the humidity of an intensive sheep-breeding facility in Manas, Xinjiang, China, in real time for the next 10 min. The experimental results indicated that the proposed LightGBM–CGWO–SVR model outperformed eight existing models used for comparison on all evaluation metrics. It achieved minimum values of 0.0662, 0.2284, 0.0521, and 0.0083 in terms of mean absolute error, root mean square error, mean squared error, and normalized root mean square error, respectively, and a maximum value of 0.9973 in terms of the R(2) index.