摘要
新疆是我国第二大牧区,牲畜饲养量大,同时也是传统的布鲁氏菌病(布病)流行区。以2017年1月—2023年2月牛布病发病率数据作为训练样本集,利用ARIMA模型与BP神经网络方法分别基于训练样本集建模,评价并分析两种方法的拟合效果。结果表明:时间序列ARIMA模型适合线性预测,在序列波动较大和存在异常值情况下拟合效果不佳;BP神经网络在拟合非线性波动规律方面有很强的说服力,同时在处理异常值方面具有明显优势。因此,汲取ARIMA模型与ARIMA-BP神经网络两种方法在新疆牛布病发病率拟合方面的优势,构建ARIMA-BP神经网络组合模型并拟合训练样本集,进一步应用以上3种模型预测2023年3月—2024年2月新疆养殖环节牛布病发病率,将结果与真实值作比较。研究表明,无论对训练样本集的拟合还是对未来发病率的预测,ARIMA-BP神经网络组合模型的模拟效果均优于两种单一模型,在预测新疆牛布病未来的发展趋势方面具有可行性。
Xinjiang is the second largest pastoral area in China with high livestock rearing,and also a traditional endemic area for brucellosis.Data on the incidence rate of bovine brucellosis from January 2017 to February 2023 were selected as the training sample set.The ARIMA model and BP neural network method were used to model the training sample set,respectively,and their fitting effects were evaluated and analyzed.The results showed that the time series ARIMA model was suitable for linear prediction,but the fitting effect was poor in case of large fluctuations in the series and in the presence of outliers.The BP neural network demonstrated strong persuasive power in fitting nonlinear fluctuation patterns and had a clear advantage in handling outliers.Therefore,drawing on the advantages of the ARIMA model and ARIMA-BP neural network in fitting the incidence rate of bovine brucellosis in Xinjiang,the combined ARIMA-BP neural network model was constructed and fitted to the training sample set.Furthermore,the above three models were applied to predict the incidence rate of bovine brucellosis in Xinjiang's breeding sector from March 2023 to February 2024,and the results were compared with the actual values.The results demonstrated that,the combined ARIMA-BP neural network model was better than the two single models in both the fitting of the training sample set and the prediction of the future incidence rate,and was feasible in predicting the future development trend of bovine brucellosis in Xinjiang.
作者
徐刚刚
李盈科
高梦洁
彭叶龙
丁剑
XU Ganggang;LI Yingke;GAO Mengjie;PENG Yelong;DING Jian(College of Mathematics and Physics,Xinjiang Agricultural University,Urumqi 830052,China;Xinjiang Uygur Autonomous Region Center for Animal Disease Prevention and Control,Urumqi 830000,China;College of Veterinary Medicine,Xinjiang Agricultural University,Urumqi 830052,China)
出处
《内蒙古农业大学学报（自然科学版）》
CAS
北大核心
2024年第3期68-73,共6页
Journal of Inner Mongolia Agricultural University(Natural Science Edition)
基金
新疆维吾尔自治区高校基本科研业务费科研项目(XJEDU2022P040)
新疆农业大学大学生创新创业训练计划项目(S202310758076)。
