This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
LSTM-SARIMA Based Prediction 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 (Article in Progress)
Abstract
Closed-type poultry houses facilitate consistent output by ensuring a steady microenvironment conducive to optimal avian growth. Nevertheless, numerous farms continue to depend on manual oversight of temperature, humidity, and ammonia levels, resulting in delayed reactions, diminished productivity, and heightened environmental stress on poultry. These constraints underscore the necessity for predictive and automated systems that can monitor and forecast environmental variables in real time. Prior research indicates that LSTM networks are proficient in nonlinear time-series forecasting nonetheless, when used in isolation, LSTM models encounter difficulties in capturing linear seasonal patterns and long-term trends present in chicken house environmental data. This research presents a hybrid forecasting framework that combines LSTM and SARIMA models to concurrently represent nonlinear temporal dependencies and linear seasonal components. Environmental metrics such as temperature, soil moisture, and ammonia concentration were acquired using SHT31, Soil Moisture, and MQ137 sensors, processed using a Python-Flask backend, saved in MongoDB, and visualized through a cross-platform Flutter-based web interface. Experimental findings indicate that the proposed LSTM–SARIMA model exhibits robust predictive efficacy, with MAE = 0.62, MSE = 0.55, RMSE = 0.58, MAPE = 7.89%, and R² = 0.86. The findings demonstrate that the suggested method efficiently facilitates early warning systems and real-time microclimate evaluation, allowing for expedited environmental management measures and minimizing production losses due to unstable poultry house conditions.
Keywords
Download Citation
Endnote/Zotero/Mendeley (RIS)BibTeX
- C. Okinda et al., “A review on computer vision systems in monitoring of poultry: A welfare perspective,” Jan. 01, 2020, KeAi Communications Co. doi: 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. doi: 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, doi: 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, [Online]. Available: www.jatit.org
- 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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. doi: 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, doi: 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, doi: 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, [Online]. Available: https://ijpsat.org/
- 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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.”
- 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, doi: 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, doi: 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. doi: 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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
References
C. Okinda et al., “A review on computer vision systems in monitoring of poultry: A welfare perspective,” Jan. 01, 2020, KeAi Communications Co. doi: 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. doi: 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, doi: 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, [Online]. Available: www.jatit.org
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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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. doi: 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, doi: 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, doi: 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, [Online]. Available: https://ijpsat.org/
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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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.”
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, doi: 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, doi: 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. doi: 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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