This ammonia prediction study was performed using the time-series artificial neural network model, Long-short term memory (LSTM), after long-term monitoring of ammonia and environmental factors (ventilation rate (V), temperature (T), humidity (RH)) from a slurry finishing pig farm on mechanical ventilation system. The difference with the actual ammonia concentration was compared through prediction of the last three days of the entire breeding period. As a result of the analysis, the model which had a low correlation (ammonia concentration and humidity) was confirmed to have less error values than the models that did not. In addition, the combination of two or more input values [V, RH] and [T, V, RH] showed the lowest error value. In this study, the sustainability period of the model trained by multivariate input values was analyzed for about two days. In addition, [T, V, RH] showed the highest predictive performance with regard to the actual time of the occurrence of peak concentration compared to other models . These results can be useful in providing highly reliable information to livestock farmers regarding the management of concentrations through artificial neural network-based prediction models.

Abstract
1. 서 론
2. 연구 방법
    2.1 Time-Series Data
    2.2 Long Short-Term Memory (LSTM)
3. 연구 결과 및 고찰
4. 결 론
References

• 장유나(국립축산과학원 축산환경과) | Yu-Na Jang (Animal Environment Division, National Institute of Animal Science (NIAS))
• 임종국(㈜제이마플) | Jongkuk Lim (J.MARPLE, Inc.)
• 정민웅(국립축산과학원 축산환경과) | Min-Woong Jung (Animal Environment Division, National Institute of Animal Science (NIAS))
• 조광곤(금강유역환경청 측정분석과) | Gwanggon Jo (Monitoring and Analysis Division, Geum River Basin Environment Office) Corresponding author