基于XGBoost的水禽养殖粉尘预测研究

为了提高水禽养殖中粉尘预测精度，提出基于XGBoost的水禽养殖粉尘预测模型.通过对粉尘相关参数进行相关性分析，提取出更重要的参数进行预测，简化了模型，降低计算难度，然后将归一化后的数据输入模型进行训练优化，最后通过与其他传统模型进行对比分析，提出的预测模型评价指标平均绝对百分比误差、平均绝对误差和均方根误差分别为0.010 4、0.190 2、0.240 6，均低于对比模型，验证了提出的XGBoost模型对于水禽养殖粉尘预测具有很好的预测精度与鲁棒性能，为水禽养殖智能化提供一种新的有效方法.

XGBoost based dust prediction for waterfowl breeding

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.