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An LSTM-SARIMA Based Forecasting Method for Environmental Quality in Enclosed Poultry House
Corresponding Author(s) : Genta Garuda Bimasakti
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 11, No. 2, May 2026
Abstract
Closed-type poultry houses support stable production performance by maintaining a controlled microenvironment that promotes optimal poultry growth. However, many farms still rely on manual monitoring of environmental parameters such as temperature, humidity, and ammonia concentration, resulting in delayed responses, reduced productivity, and increased environmental stress on poultry. These limitations highlight the need for predictive and automated systems that can monitor and forecast environmental conditions in real time. Previous studies have shown that LSTM networks are effective for nonlinear time-series forecasting. However, when applied independently, LSTM models often face difficulties in capturing linear seasonal patterns and long-term trends inherent in poultry house environmental data. Therefore, this study proposes a hybrid forecasting framework that integrates LSTM and SARIMA models to simultaneously capture nonlinear temporal dependencies and linear seasonal components. Environmental parameters, including temperature, litter moisture, and ammonia concentration, were collected using SHT31, Soil Moisture, and MQ137 sensors. The collected data were processed using a Python-Flask backend system, stored in MongoDB, and visualized through a cross-platform web interface developed using Flutter. Experimental results demonstrate that the proposed LSTM–SARIMA model achieves strong predictive performance, with MAE = 0.62, MSE = 0.55, RMSE = 0.58, MAPE = 7.89%, and R² = 0.86. These findings indicate that the proposed method effectively supports early warning systems and real-time microclimate monitoring, enabling faster environmental control responses and reducing production losses caused by unstable poultry house conditions.
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- C. Okinda et al., “A review on computer vision systems in monitoring of poultry: A welfare perspective,” Jan. 01, 2020, KeAi Communications Co. https://doi.org/10.1016/j.aiia.2020.09.002
- R. Budiarto, N. K. Gunawan, and B. A. Nugroho, “Smart chicken farming: Monitoring system for temperature, ammonia levels, feed in chicken farms,” in IOP Conference Series: Materials Science and Engineering, Institute of Physics Publishing, Jul. 2020. https://doi.org/10.1088/1757-899X/852/1/012175
- D. Hofstetter, E. Fabian, and A. G. Lorenzoni, “Ammonia generation system for poultry health research using arduino,” Sensors, vol. 21, no. 19, Oct. 2021. https://doi.org/10.3390/s21196664
- N. K. Krishna Panicker and J. Valarmathi, “A Hybrid SARIMA-LSTM Approach for Improved Time Series Prediction Of Aerosol Optical Depth Across Delhi,India,” J. Theor. Appl. Inf. Technol., vol. 15, no. 11, 2024.
- D. Naidu and S. K. Chandniha, “Hybrid SARIMA–Bi-LSTM model for monthly rainfall forecasting in the agroclimatic zones of Chhattisgarh,” Journal of Agrometeorology, vol. 27, no. 3, pp. 332–337, Sep. 2025. https://doi.org/10.54386/jam.v27i3.3010
- H. Khodakhah, P. Aghelpour, and Z. Hamedi, “Comparing linear and non-linear data-driven approaches in monthly river flow prediction, based on the models SARIMA, LSSVM, ANFIS, and GMDH,” Environmental Science and Pollution Research, vol. 29, no. 15, pp. 21935–21954, Mar. 2022. https://doi.org/10.1007/s11356-021-17443-0
- A. Kumar Dubey, A. Kumar, V. García-Díaz, A. Kumar Sharma, and K. Kanhaiya, “Study and analysis of SARIMA and LSTM in forecasting time series data,” Sustainable Energy Technologies and Assessments, vol. 47, Oct. 2021. https://doi.org/10.1016/j.seta.2021.101474
- A. Parasyris, G. Alexandrakis, G. V. Kozyrakis, K. Spanoudaki, and N. A. Kampanis, “Predicting Meteorological Variables on Local Level with SARIMA, LSTM and Hybrid Techniques,” Atmosphere (Basel)., vol. 13, no. 6, Jun. 2022. https://doi.org/10.3390/atmos13060878
- C. Sun, M. Pei, B. Cao, S. Chang, and H. Si, “A Study on Agricultural Commodity Price Prediction Model Based on Secondary Decomposition and Long Short-Term Memory Network,” Agriculture (Switzerland), vol. 14, no. 1, Jan. 2024. https://doi.org/10.3390/agriculture14010060
- Y. Dai, Z. Li, and J. Lu, “Landslide hazard early warning method for rock slopes using a hybrid LSTM-SARIMA data-driven model,” PLoS One, vol. 20, no. 5 May, May 2025. https://doi.org/10.1371/journal.pone.0323650
- R. Killick, M. I. Knight, G. P. Nason, and I. A. Eckley, “The local partial autocorrelation function and some applications,” Electron. J. Stat., vol. 14, no. 2, pp. 3268–3314, 2020. https://doi.org/10.1214/20-EJS1748
- S. Kumari and P. Muthulakshmi, “SARIMA Model: An Efficient Machine Learning Technique for Weather Forecasting,” in Procedia Computer Science, Elsevier B.V., 2024, pp. 656–670. https://doi.org/10.1016/j.procs.2024.04.064
- A. W. Saputra, A. P. Wibawa, U. Pujianto, A. B. Putra Utama, and A. Nafalski, “LSTM-based Multivariate Time-Series Analysis: A Case of Journal Visitors Forecasting,” ILKOM Jurnal Ilmiah, vol. 14, no. 1, pp. 57–62, Apr. 2022. https://doi.org/10.33096/ilkom.v14i1.1106.57-62
- C. Tjahyadi, N. Sutarna, and P. Oktivasari, “Optimized BiLSTM-Dense Model for Ultra-Short-Term PV Power Forecasting,” Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, May 2025. https://doi.org/10.22219/kinetik.v10i2.2127
- R. Herilala Tafitasoloniaina, R. Malanto Miangaly, and R. Roger, “Hybrid LSTM-RNN And Sarima Modeling For Time Series Temperature Prediction: The Case Of Antananarivo, Madagascar,” International Journal of Progressive Sciences and Technologies (IJPSAT, vol. 50, no. 1, pp. 69–96, 2025. http://dx.doi.org/10.52155/ijpsat.v50.1.7123
- M. L. Hossain, S. M. N. Shams, and S. M. Ullah, “Time-series and deep learning approaches for renewable energy forecasting in Dhaka: a comparative study of ARIMA, SARIMA, and LSTM models,” Discover Sustainability, vol. 6, no. 1, Dec. 2025. https://doi.org/10.1007/s43621-025-01733-5
- X. T. Jing, Y. L. Bai, B. Y. Hou, and C. L. Huang, “Physics-informed neural networks coupled with a residual-driven dynamic weighted Huber loss function,” New J. Phys., vol. 27, no. 9, Sep. 2025. https://doi.org/10.1088/1367-2630/adfd78
- A. Mahmoud and A. Mohammed, “Leveraging Hybrid Deep Learning Models for Enhanced Multivariate Time Series Forecasting,” Neural Process. Lett., vol. 56, no. 5, Oct. 2024. https://doi.org/10.1007/s11063-024-11656-3
- J. Rocha, C. M. Viana, and S. Oliveira, Time Series Analysis: Recent Advances, New Perspectives and Applications, IntechOpen, 2024. https://doi.org/10.5772/intechopen.111223
- B. Kholifah, I. Syarif, and T. Badriyah, “Mental Disorder Detection via Social Media Mining using Deep Learning,” Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, pp. 309–316, Nov. 2020. https://doi.org/10.22219/kinetik.v5i4.1120
- Rajeev, S., Ashoka, K. M., & Tiparaddi, A. M., “Hybrid SARIMA-LSTM Model for Local Weather Forecasting: A Residual-Learning Approach for Data-Driven Meteorological Prediction,” arXiv preprint arXiv:2601.07951, Jan. 12, 2026.” https://doi.org/10.48550/arXiv.2601.07951
- X. Xu, X. Li, N. Chen, D. Zhao, and C. Chen, “Autonomous Obstacle Avoidance with Improved Deep Reinforcement Learning Based on Dynamic Huber Loss,” Applied Sciences (Switzerland), vol. 15, no. 5, Mar. 2025. https://doi.org/10.3390/app15052776
- A. A. Dzulfiqar, J. E. Chrisnanto, and B. Adiperdana, “JIIF (Jurnal Ilmu dan Inovasi Fisika) UNPAD Hybrid SARIMA-LSTM Model For Predicting Erosion In Butterfly Valve,” PT Padjadjaran Information Techology (PLABS.ID) Jl. Setrasari Tengah, vol. 09, no. 01, p. 40512, 2025. https://doi.org/10.24198/jiif.v9i1
- P. Piotrowski, I. Rutyna, D. Baczyński, and M. Kopyt, “Evaluation Metrics for Wind Power Forecasts: A Comprehensive Review and Statistical Analysis of Errors,” Dec. 01, 2022, MDPI. https://doi.org/10.3390/en15249657
- S. T. Aung, N. Funabiki, L. H. Aung, S. A. Kinari, M. Mentari, and K. H. Wai, “A Study of Learning Environment for Initiating Flutter App Development Using Docker,” Information (Switzerland), vol. 15, no. 4, Apr. 2024. https://doi.org/10.3390/info15040191
- S. Y. Ameen and D. Y. Mohammed, “Developing Cross-Platform Library Using Flutter,” European Journal of Engineering and Technology Research, vol. 7, no. 2, pp. 18–21, Mar. 2022. https://doi.org/10.24018/ejeng.2022.7.2.2740
- N. Idris, C. F. Mohd Foozy, and P. Shamala, “A Generic Review of Web Technology: Django and Flask,” International Journal of Advanced Science Computing and Engineering, vol. 2, no. 1, pp. 34–40, Apr. 2020. https://doi.org/10.62527/ijasce.2.1.29
- M. M. Eyada, W. Saber, M. M. El Genidy, and F. Amer, “Performance Evaluation of IoT Data Management Using MongoDB Versus MySQL Databases in Different Cloud Environments,” IEEE Access, vol. 8, pp. 110656–110668, 2020. https://doi.org/10.1109/ACCESS.2020.3002164
- A. John, E. B. Nkemnole, J. S. Adeyeye, and E. B. Nkemnole, “Predicting Malaria Incident Using Hybrid SARIMA-LSTM Model,” International Journal of Mathematical Sciences and Optimization: Theory and Applications, vol. 9, no. 1, pp. 123–137. https://doi.org/10.5281/zenodo.8269739
- W. Fang, Z. Yu, Y. Chen, T. Huang, T. Masquelier, and Y. Tian, “Deep residual learning in spiking neural networks,” Advances in Neural Information Processing Systems (NeurIPS), vol. 34, pp. 21056–21069, 2021. https://doi.org/10.48550/arXiv.2102.04159
References
C. Okinda et al., “A review on computer vision systems in monitoring of poultry: A welfare perspective,” Jan. 01, 2020, KeAi Communications Co. https://doi.org/10.1016/j.aiia.2020.09.002
R. Budiarto, N. K. Gunawan, and B. A. Nugroho, “Smart chicken farming: Monitoring system for temperature, ammonia levels, feed in chicken farms,” in IOP Conference Series: Materials Science and Engineering, Institute of Physics Publishing, Jul. 2020. https://doi.org/10.1088/1757-899X/852/1/012175
D. Hofstetter, E. Fabian, and A. G. Lorenzoni, “Ammonia generation system for poultry health research using arduino,” Sensors, vol. 21, no. 19, Oct. 2021. https://doi.org/10.3390/s21196664
N. K. Krishna Panicker and J. Valarmathi, “A Hybrid SARIMA-LSTM Approach for Improved Time Series Prediction Of Aerosol Optical Depth Across Delhi,India,” J. Theor. Appl. Inf. Technol., vol. 15, no. 11, 2024.
D. Naidu and S. K. Chandniha, “Hybrid SARIMA–Bi-LSTM model for monthly rainfall forecasting in the agroclimatic zones of Chhattisgarh,” Journal of Agrometeorology, vol. 27, no. 3, pp. 332–337, Sep. 2025. https://doi.org/10.54386/jam.v27i3.3010
H. Khodakhah, P. Aghelpour, and Z. Hamedi, “Comparing linear and non-linear data-driven approaches in monthly river flow prediction, based on the models SARIMA, LSSVM, ANFIS, and GMDH,” Environmental Science and Pollution Research, vol. 29, no. 15, pp. 21935–21954, Mar. 2022. https://doi.org/10.1007/s11356-021-17443-0
A. Kumar Dubey, A. Kumar, V. García-Díaz, A. Kumar Sharma, and K. Kanhaiya, “Study and analysis of SARIMA and LSTM in forecasting time series data,” Sustainable Energy Technologies and Assessments, vol. 47, Oct. 2021. https://doi.org/10.1016/j.seta.2021.101474
A. Parasyris, G. Alexandrakis, G. V. Kozyrakis, K. Spanoudaki, and N. A. Kampanis, “Predicting Meteorological Variables on Local Level with SARIMA, LSTM and Hybrid Techniques,” Atmosphere (Basel)., vol. 13, no. 6, Jun. 2022. https://doi.org/10.3390/atmos13060878
C. Sun, M. Pei, B. Cao, S. Chang, and H. Si, “A Study on Agricultural Commodity Price Prediction Model Based on Secondary Decomposition and Long Short-Term Memory Network,” Agriculture (Switzerland), vol. 14, no. 1, Jan. 2024. https://doi.org/10.3390/agriculture14010060
Y. Dai, Z. Li, and J. Lu, “Landslide hazard early warning method for rock slopes using a hybrid LSTM-SARIMA data-driven model,” PLoS One, vol. 20, no. 5 May, May 2025. https://doi.org/10.1371/journal.pone.0323650
R. Killick, M. I. Knight, G. P. Nason, and I. A. Eckley, “The local partial autocorrelation function and some applications,” Electron. J. Stat., vol. 14, no. 2, pp. 3268–3314, 2020. https://doi.org/10.1214/20-EJS1748
S. Kumari and P. Muthulakshmi, “SARIMA Model: An Efficient Machine Learning Technique for Weather Forecasting,” in Procedia Computer Science, Elsevier B.V., 2024, pp. 656–670. https://doi.org/10.1016/j.procs.2024.04.064
A. W. Saputra, A. P. Wibawa, U. Pujianto, A. B. Putra Utama, and A. Nafalski, “LSTM-based Multivariate Time-Series Analysis: A Case of Journal Visitors Forecasting,” ILKOM Jurnal Ilmiah, vol. 14, no. 1, pp. 57–62, Apr. 2022. https://doi.org/10.33096/ilkom.v14i1.1106.57-62
C. Tjahyadi, N. Sutarna, and P. Oktivasari, “Optimized BiLSTM-Dense Model for Ultra-Short-Term PV Power Forecasting,” Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, May 2025. https://doi.org/10.22219/kinetik.v10i2.2127
R. Herilala Tafitasoloniaina, R. Malanto Miangaly, and R. Roger, “Hybrid LSTM-RNN And Sarima Modeling For Time Series Temperature Prediction: The Case Of Antananarivo, Madagascar,” International Journal of Progressive Sciences and Technologies (IJPSAT, vol. 50, no. 1, pp. 69–96, 2025. http://dx.doi.org/10.52155/ijpsat.v50.1.7123
M. L. Hossain, S. M. N. Shams, and S. M. Ullah, “Time-series and deep learning approaches for renewable energy forecasting in Dhaka: a comparative study of ARIMA, SARIMA, and LSTM models,” Discover Sustainability, vol. 6, no. 1, Dec. 2025. https://doi.org/10.1007/s43621-025-01733-5
X. T. Jing, Y. L. Bai, B. Y. Hou, and C. L. Huang, “Physics-informed neural networks coupled with a residual-driven dynamic weighted Huber loss function,” New J. Phys., vol. 27, no. 9, Sep. 2025. https://doi.org/10.1088/1367-2630/adfd78
A. Mahmoud and A. Mohammed, “Leveraging Hybrid Deep Learning Models for Enhanced Multivariate Time Series Forecasting,” Neural Process. Lett., vol. 56, no. 5, Oct. 2024. https://doi.org/10.1007/s11063-024-11656-3
J. Rocha, C. M. Viana, and S. Oliveira, Time Series Analysis: Recent Advances, New Perspectives and Applications, IntechOpen, 2024. https://doi.org/10.5772/intechopen.111223
B. Kholifah, I. Syarif, and T. Badriyah, “Mental Disorder Detection via Social Media Mining using Deep Learning,” Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, pp. 309–316, Nov. 2020. https://doi.org/10.22219/kinetik.v5i4.1120
Rajeev, S., Ashoka, K. M., & Tiparaddi, A. M., “Hybrid SARIMA-LSTM Model for Local Weather Forecasting: A Residual-Learning Approach for Data-Driven Meteorological Prediction,” arXiv preprint arXiv:2601.07951, Jan. 12, 2026.” https://doi.org/10.48550/arXiv.2601.07951
X. Xu, X. Li, N. Chen, D. Zhao, and C. Chen, “Autonomous Obstacle Avoidance with Improved Deep Reinforcement Learning Based on Dynamic Huber Loss,” Applied Sciences (Switzerland), vol. 15, no. 5, Mar. 2025. https://doi.org/10.3390/app15052776
A. A. Dzulfiqar, J. E. Chrisnanto, and B. Adiperdana, “JIIF (Jurnal Ilmu dan Inovasi Fisika) UNPAD Hybrid SARIMA-LSTM Model For Predicting Erosion In Butterfly Valve,” PT Padjadjaran Information Techology (PLABS.ID) Jl. Setrasari Tengah, vol. 09, no. 01, p. 40512, 2025. https://doi.org/10.24198/jiif.v9i1
P. Piotrowski, I. Rutyna, D. Baczyński, and M. Kopyt, “Evaluation Metrics for Wind Power Forecasts: A Comprehensive Review and Statistical Analysis of Errors,” Dec. 01, 2022, MDPI. https://doi.org/10.3390/en15249657
S. T. Aung, N. Funabiki, L. H. Aung, S. A. Kinari, M. Mentari, and K. H. Wai, “A Study of Learning Environment for Initiating Flutter App Development Using Docker,” Information (Switzerland), vol. 15, no. 4, Apr. 2024. https://doi.org/10.3390/info15040191
S. Y. Ameen and D. Y. Mohammed, “Developing Cross-Platform Library Using Flutter,” European Journal of Engineering and Technology Research, vol. 7, no. 2, pp. 18–21, Mar. 2022. https://doi.org/10.24018/ejeng.2022.7.2.2740
N. Idris, C. F. Mohd Foozy, and P. Shamala, “A Generic Review of Web Technology: Django and Flask,” International Journal of Advanced Science Computing and Engineering, vol. 2, no. 1, pp. 34–40, Apr. 2020. https://doi.org/10.62527/ijasce.2.1.29
M. M. Eyada, W. Saber, M. M. El Genidy, and F. Amer, “Performance Evaluation of IoT Data Management Using MongoDB Versus MySQL Databases in Different Cloud Environments,” IEEE Access, vol. 8, pp. 110656–110668, 2020. https://doi.org/10.1109/ACCESS.2020.3002164
A. John, E. B. Nkemnole, J. S. Adeyeye, and E. B. Nkemnole, “Predicting Malaria Incident Using Hybrid SARIMA-LSTM Model,” International Journal of Mathematical Sciences and Optimization: Theory and Applications, vol. 9, no. 1, pp. 123–137. https://doi.org/10.5281/zenodo.8269739
W. Fang, Z. Yu, Y. Chen, T. Huang, T. Masquelier, and Y. Tian, “Deep residual learning in spiking neural networks,” Advances in Neural Information Processing Systems (NeurIPS), vol. 34, pp. 21056–21069, 2021. https://doi.org/10.48550/arXiv.2102.04159