| 基于PCA-SVR-ARMA的狮头鹅养殖禽舍气温组合预测模型 |
| |
| 引用本文： | 刘双印,黄建德,徐龙琴,赵学华,李祥铜,曹亮,温宝琴,黄运茂.基于PCA-SVR-ARMA的狮头鹅养殖禽舍气温组合预测模型[J].农业工程学报,2020,36(11):225-233. |
| |
| 作者姓名： | 刘双印 黄建德 徐龙琴 赵学华 李祥铜 曹亮 温宝琴 黄运茂 |
| |
| 作者单位： | 广州市农产品质量安全溯源信息技术重点实验室,广州 510225;石河子大学机械电气工程学院,石河子 832000;仲恺农业工程学院智慧农业创新研究院,广州 510225;广东省高校智慧农业工程技术研究中心,广州 510225;广东省水禽健康养殖重点实验室,广州 510225;广东省农产品安全大数据工程技术研究中心,广州 510225;广州市农产品质量安全溯源信息技术重点实验室,广州 510225;仲恺农业工程学院智慧农业创新研究院,广州 510225;广东省高校智慧农业工程技术研究中心,广州 510225;广东省农产品安全大数据工程技术研究中心,广州 510225;深圳信息职业技术学院数字媒体学院,深圳 518172;石河子大学机械电气工程学院,石河子 832000;广东省水禽健康养殖重点实验室,广州 510225 |
| |
| 基金项目： | 国家自然科学基金项目（61871475，61471133，61571444）；广州市创新平台建设计划项目（201905010006）；广东省科技计划项目（2019B020215003，2017B0101260016，2017A070712019，2016A070712020）；广东普通高校工程技术研究中心（2017GCZX0014）；广东省普通高校省级重大科研项目（2016KZDXM0013）；广东省教育厅特色创新项目（2017KTSCX094，2017KQNCX098）；北京市自然科学基金项目（4182023）；广东省农业技术研发项目（2018LM2168） |
| |
| 摘 要： | 为提高狮头鹅养殖禽舍气温预测精度,提出了基于主成分分析(Principal Component Analysis,PCA)、支持向量回归机(Support Vector Regression,SVR)融合自回归滑动平均(Autoregressive Moving Average,ARMA)模型的狮头鹅养殖禽舍气温组合预测模型。在建模过程中,运用主成分分析法筛选狮头鹅养殖禽舍气温的关键影响因子,消除变量之间冗余信息,约简预测模型结构;采用SVR-ARMA构建狮头鹅禽养殖舍气温组合预测模型,先通过SVR对气温进行预测,再由基于ARMA模型的残差预测值修正气温预测结果。利用该模型对广东省汕尾市2018年7月21日至2018年7月30日期间的狮头鹅养殖禽舍气温进行预测。结果表明,该组合预测模型取得了良好的预测性能,与标准BP神经网络、标准SVR、PCA-BPNN(反向传播神经网络,BackPropagationNeuralNetwork)、PCA-SVR和PCA-BPNN-ARMA等模型对比分析,其评价指标平均绝对误差、均方根误差和平均绝对百分比误差分别为0.183 2℃、0.454 0℃和0.005 9,均表明所提出的组合模型具有更高的预测效果,不仅能够满足狮头鹅养殖禽舍气温实际精准调控的需要,还为狮头鹅健康养殖和种苗繁育环境精细化管理提供决策。
|
| 关 键 词： | 畜禽舍 温度 模型 预测 支持向量回归机 残差修正 狮头鹅养殖 ARMA模型 |
| 收稿时间： | 2020/3/2 0:00:00 |
| 修稿时间： | 2020/4/13 0:00:00 |
Combined model for prediction of air temperature in poultry house for lion-head goose breeding based on PCA-SVR-ARMA |
| |
| Liu Shuangyin,Huang Jiande,Xu Longqin,Zhao Xuehu,Li Xiangtong,Cao Liang,Wen Baoqin,Huang Yunmao.Combined model for prediction of air temperature in poultry house for lion-head goose breeding based on PCA-SVR-ARMA[J].Transactions of the Chinese Society of Agricultural Engineering,2020,36(11):225-233. |
| |
| Authors: | Liu Shuangyin Huang Jiande Xu Longqin Zhao Xuehu Li Xiangtong Cao Liang Wen Baoqin Huang Yunmao |
| |
| Institution: | 1. Guangzhou Key Laboratory of Agricultural Products Quality & Safety Traceability Information Technology, Guangzhou 510225, China; 2. College of Mechanical and Electric Engineering, Shihezi University, Shihezi 832000, China; 3. Academy of Smart Agricultural Engineering Innovations, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China; 4. Smart Agriculture Engineering Research Center of Guangdong Higher Education Institutes, Guangzhou 510225, China; 6. Guangdong Key Laboratory of Waterflow Health Breeding, Guangzhou 510225, China; 7. Guangdong Provincial Agricultural Products Safety Big Data Engineering Technology Research Center, Guangzhou 510225, China;1. Guangzhou Key Laboratory of Agricultural Products Quality & Safety Traceability Information Technology, Guangzhou 510225, China; 3. Academy of Smart Agricultural Engineering Innovations, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China; 4. Smart Agriculture Engineering Research Center of Guangdong Higher Education Institutes, Guangzhou 510225, China; 7. Guangdong Provincial Agricultural Products Safety Big Data Engineering Technology Research Center, Guangzhou 510225, China;1. Guangzhou Key Laboratory of Agricultural Products Quality & Safety Traceability Information Technology, Guangzhou 510225, China; 3. Academy of Smart Agricultural Engineering Innovations, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China; 4. Smart Agriculture Engineering Research Center of Guangdong Higher Education Institutes, Guangzhou 510225, China;7. Guangdong Provincial Agricultural Products Safety Big Data Engineering Technology Research Center, Guangzhou 510225, China;5.School of Digital Media, Shenzhen Institute of Information Technology, Shenzhen 518172, China;;1.Guangzhou Key Laboratory of Agricultural Products Quality & Safety Traceability Information Technology, Guangzhou 510225, China; 3. Academy of Smart Agricultural Engineering Innovations, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China; 4. Smart Agriculture Engineering Research Center of Guangdong Higher Education Institutes, Guangzhou 510225, China; 7. Guangdong Provincial Agricultural Products Safety Big Data Engineering Technology Research Center, Guangzhou 510225, China;2.College of Mechanical and Electric Engineering, Shihezi University, Shihezi 832000, China;; 6.Guangdong Key Laboratory of Waterflow Health Breeding, Guangzhou 510225, China; 7. Guangdong Provincial Agricultural Products Safety Big Data Engineering Technology Research Center, Guangzhou 510225, China |
| |
| Abstract: | Abstract: China has the largest waterfowl industry, accounting for almost 75% of the waterfowl production in the world. The air temperature in the lion-head goose breeding environment is crucial to the survival of lion-head goose. The air temperature is susceptible to many factors such as relative humidity, illumination intensity, total suspended particulates, etc. So, it is significant to understand timely and accurately the change of the air temperature which can prevent environment deterioration, disease outbreaks, and optimize breeding management. Grasping the trend of the air temperature value timely and accurately is the key to the high-density healthy lion-head goose culture. The traditional prediction methods in temperature have problems such as low prediction accuracy, poor robustness, and poor generalization ability, etc. To improve the prediction accuracy of the air temperature of the lion-head goose breeding environment, a predicting model of air temperature fusing Principal Component Analysis (PCA), Support Vector Regression (SVR) machine, and Autoregressive Moving Average (ARMA) model was proposed for lion-head goose breeding environment. First, the key impact factors of air temperature in lion-head goose breeding were extracted by principal component analysis, which eliminated redundant information between variables and reduced the complexity of the prediction model structure. Therefore, the key impact factors were selected air temperature, relative humidity, illumination intensity, total suspended particulates, respectively. Then, SVR-ARMA model was used to build the nonlinear prediction model between the temperature and these key impact factors for lion-head goose breeding, in which, first, the air temperature was predicted by SVR, then the air temperature prediction values were corrected by the residual prediction value of ARMA model. Because the kernel parameter and the regularization parameter in the SVR training procedure significantly influence forecasting accuracy, a leave-one-out cross-validation method was utilized to optimize the SVR model parameters and biases under a parameter space design. With this model, the air temperature change had been predicted for the lion-head goose breeding environment from July 21st, 2018 to July 30th, 2018 in Shanwei city, Guangdong province. The experimental results showed that the proposed combination prediction model of PCA-SVR-ARMA had a better prediction effect than the standard BP neural network, standard support vector regression machine, PCA-BPNN, PCA-SVR, and PCA-BPNN-ARMA method. And the relative Mean Absolute Percent Error (MAPE), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) for the temperature between the PCA-SVR-ARMA and PCA-SVR models were 29.59%, 40.37%, and 60.75% respectively under the same experimental conditions. The relative MAPE, RMSE, and MAE for the temperature between the PCA-SVR and standard SVR models were 31.78%, 15.89%, and 29.45% respectively. The relative MAPE, RMSE, and MAE for the temperature between the PCA-SVR-ARMA and PCA-BPNN-ARMA models were 55.64%, 35.66%, and 55.26% respectively. The relative MAPE, RMSE, and MAE for the temperature between the PCA-BPNN-ARMA and PCA-BPNN models were 43.16%, 30.63%, and 44.16% respectively. The relative MAPE, RMSE, and MAE for the temperature between the PCA-BPNN and BPNN models were 10.34%, 4.80%, and 7.98% respectively. To sum up, the SVR model had a better prediction performance under the condition of small samples, while the residual error correction method based on the ARMA model effectively improved prediction performance. The temperature prediction model based on PCA-SVR-ARMA exhibited best prediction accuracy and generalization performance when compared with other traditional forecasting models. Therefore, the presented model based on PCA-SVR-ARMA could meet the actual demand for accurate forecasting of temperature and provide a reference for environment control in healthy breeding and seedling breeding of lion-head goose. As well as it also could help farmers make decisions and reduce farming risks. |
| |
| Keywords: | animal building temperature models prediction support vector regression error correcting lion-head goose breeding ARMA model |
| 本文献已被 CNKI 万方数据 等数据库收录！ |
| 点击此处可从《农业工程学报》浏览原始摘要信息 |
| 点击此处可从《农业工程学报》下载免费的PDF全文 |
|
