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Abstract In order to improve the accuracy of dust prediction in waterfowl breeding, a dust prediction model for waterfowl breeding based on XGBoost was proposed. More important parameters were extracted for prediction, which simplified the model to reduce the calculation difficulty. Then, the normalized data, which was inputted into the model for training and optimization, were compared with that of other traditional models finally. The average absolute percentage error, the average absolute error, and the root mean square error of the prediction model evaluation indicators were 0.010 4, 0.190 2, and 0.240 6, respectively, which were lower than that of the comparative model. The results verified that the XGBoost model proposed had better prediction accuracy and robust performance, which could provide a novel effective method for the intelligentization of waterfowl breeding.
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Received: 15 July 2021
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2021, Vol. 34 
