Research on Indoor Environment Prediction of Pig House Based on OTDBO–TCN–GRU Algorithm
Temperature and humidity, along with concentrations of ammonia and hydrogen sulfide, are critical environmental factors that significantly influence the growth and health of pigs within porcine habitats. The ability to accurately predict these environmental variables in pig houses is pivotal, as it...
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| Format: | Article |
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MDPI AG
2024-03-01
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| Online Access: | https://www.mdpi.com/2076-2615/14/6/863 |
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| author | Zhaodong Guo Zhe Yin Yangcheng Lyu Yuzhi Wang Sen Chen Yaoyu Li Wuping Zhang Pengfei Gao |
| author_facet | Zhaodong Guo Zhe Yin Yangcheng Lyu Yuzhi Wang Sen Chen Yaoyu Li Wuping Zhang Pengfei Gao |
| author_sort | Zhaodong Guo |
| collection | DOAJ |
| description | Temperature and humidity, along with concentrations of ammonia and hydrogen sulfide, are critical environmental factors that significantly influence the growth and health of pigs within porcine habitats. The ability to accurately predict these environmental variables in pig houses is pivotal, as it provides crucial decision-making support for the precise and targeted regulation of the internal environmental conditions. This approach ensures an optimal living environment, essential for the well-being and healthy development of the pigs. The existing methodologies for forecasting environmental factors in pig houses are currently hampered by issues of low predictive accuracy and significant fluctuations in environmental conditions. To address these challenges in this study, a hybrid model incorporating the improved dung beetle algorithm (DBO), temporal convolutional networks (TCNs), and gated recurrent units (GRUs) is proposed for the prediction and optimization of environmental factors in pig barns. The model enhances the global search capability of DBO by introducing the Osprey Eagle optimization algorithm (OOA). The hybrid model uses the optimization capability of DBO to initially fit the time-series data of environmental factors, and subsequently combines the long-term dependence capture capability of TCNs and the non-linear sequence processing capability of GRUs to accurately predict the residuals of the DBO fit. In the prediction of ammonia concentration, the OTDBO–TCN–GRU model shows excellent performance with mean absolute error (MAE), mean square error (MSE), and coefficient of determination (<i>R</i><sup>2</sup>) of 0.0474, 0.0039, and 0.9871, respectively. Compared with the DBO–TCN–GRU model, OTDBO–TCN–GRU achieves significant reductions of 37.2% and 66.7% in MAE and MSE, respectively, while the <i>R</i><sup>2</sup> value is improved by 2.5%. Compared with the OOA model, the OTDBO–TCN–GRU achieved 48.7% and 74.2% reductions in the MAE and MSE metrics, respectively, while the <i>R</i><sup>2</sup> value improved by 3.6%. In addition, the improved OTDBO–TCN–GRU model has a prediction error of less than 0.3 mg/m<sup>3</sup> for environmental gases compared with other algorithms, and has less influence on sudden environmental changes, which shows the robustness and adaptability of the model for environmental prediction. Therefore, the OTDBO–TCN–GRU model, as proposed in this study, optimizes the predictive performance of environmental factor time series and offers substantial decision support for environmental control in pig houses. |
| first_indexed | 2024-04-24T18:37:38Z |
| format | Article |
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| institution | Directory Open Access Journal |
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| language | English |
| last_indexed | 2024-04-24T18:37:38Z |
| publishDate | 2024-03-01 |
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| spelling | doaj.art-571013c2ca12453bbd5fa89fa0a7e83c2024-03-27T13:17:41ZengMDPI AGAnimals2076-26152024-03-0114686310.3390/ani14060863Research on Indoor Environment Prediction of Pig House Based on OTDBO–TCN–GRU AlgorithmZhaodong Guo0Zhe Yin1Yangcheng Lyu2Yuzhi Wang3Sen Chen4Yaoyu Li5Wuping Zhang6Pengfei Gao7College of Software, Shanxi Agricultural University, Jinzhong 030801, ChinaCollege of Software, Shanxi Agricultural University, Jinzhong 030801, ChinaCollege of Software, Shanxi Agricultural University, Jinzhong 030801, ChinaCollege of Software, Shanxi Agricultural University, Jinzhong 030801, ChinaCollege of Software, Shanxi Agricultural University, Jinzhong 030801, ChinaCollege of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, ChinaCollege of Software, Shanxi Agricultural University, Jinzhong 030801, ChinaCollege of Animal Science, Shanxi Agricultural University, Jinzhong 030801, ChinaTemperature and humidity, along with concentrations of ammonia and hydrogen sulfide, are critical environmental factors that significantly influence the growth and health of pigs within porcine habitats. The ability to accurately predict these environmental variables in pig houses is pivotal, as it provides crucial decision-making support for the precise and targeted regulation of the internal environmental conditions. This approach ensures an optimal living environment, essential for the well-being and healthy development of the pigs. The existing methodologies for forecasting environmental factors in pig houses are currently hampered by issues of low predictive accuracy and significant fluctuations in environmental conditions. To address these challenges in this study, a hybrid model incorporating the improved dung beetle algorithm (DBO), temporal convolutional networks (TCNs), and gated recurrent units (GRUs) is proposed for the prediction and optimization of environmental factors in pig barns. The model enhances the global search capability of DBO by introducing the Osprey Eagle optimization algorithm (OOA). The hybrid model uses the optimization capability of DBO to initially fit the time-series data of environmental factors, and subsequently combines the long-term dependence capture capability of TCNs and the non-linear sequence processing capability of GRUs to accurately predict the residuals of the DBO fit. In the prediction of ammonia concentration, the OTDBO–TCN–GRU model shows excellent performance with mean absolute error (MAE), mean square error (MSE), and coefficient of determination (<i>R</i><sup>2</sup>) of 0.0474, 0.0039, and 0.9871, respectively. Compared with the DBO–TCN–GRU model, OTDBO–TCN–GRU achieves significant reductions of 37.2% and 66.7% in MAE and MSE, respectively, while the <i>R</i><sup>2</sup> value is improved by 2.5%. Compared with the OOA model, the OTDBO–TCN–GRU achieved 48.7% and 74.2% reductions in the MAE and MSE metrics, respectively, while the <i>R</i><sup>2</sup> value improved by 3.6%. In addition, the improved OTDBO–TCN–GRU model has a prediction error of less than 0.3 mg/m<sup>3</sup> for environmental gases compared with other algorithms, and has less influence on sudden environmental changes, which shows the robustness and adaptability of the model for environmental prediction. Therefore, the OTDBO–TCN–GRU model, as proposed in this study, optimizes the predictive performance of environmental factor time series and offers substantial decision support for environmental control in pig houses.https://www.mdpi.com/2076-2615/14/6/863pig barndung beetle optimization algorithmTCNGRUenvironmental prediction modelssensor |
| spellingShingle | Zhaodong Guo Zhe Yin Yangcheng Lyu Yuzhi Wang Sen Chen Yaoyu Li Wuping Zhang Pengfei Gao Research on Indoor Environment Prediction of Pig House Based on OTDBO–TCN–GRU Algorithm Animals pig barn dung beetle optimization algorithm TCN GRU environmental prediction models sensor |
| title | Research on Indoor Environment Prediction of Pig House Based on OTDBO–TCN–GRU Algorithm |
| title_full | Research on Indoor Environment Prediction of Pig House Based on OTDBO–TCN–GRU Algorithm |
| title_fullStr | Research on Indoor Environment Prediction of Pig House Based on OTDBO–TCN–GRU Algorithm |
| title_full_unstemmed | Research on Indoor Environment Prediction of Pig House Based on OTDBO–TCN–GRU Algorithm |
| title_short | Research on Indoor Environment Prediction of Pig House Based on OTDBO–TCN–GRU Algorithm |
| title_sort | research on indoor environment prediction of pig house based on otdbo tcn gru algorithm |
| topic | pig barn dung beetle optimization algorithm TCN GRU environmental prediction models sensor |
| url | https://www.mdpi.com/2076-2615/14/6/863 |
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