인공신경망 기반 양돈시설 암모니아 농도 예측

다국어 초록 (Multilingual Abstract)

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.

목차 (Table of Contents)

• 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

참고문헌 (Reference)

1 이웅섭, "축사에서 딥러닝을 이용한 질병개체 파악방안" 한국정보통신학회 21 (21): 1009-1015, 2017

2 조광곤, "대기 중 암모니아 측정을 위한 분석 방법별 비교" 한국냄새환경학회 19 (19): 137-148, 2020

3 조광곤, "강제환기식 육성돈사의 암모니아 배출 특성 연구" 한국냄새환경학회 19 (19): 29-38, 2020

4 조광곤, "강제환기식 돈사의 환기량 추정을 위한 회귀모델의 비교" 한국농공학회 62 (62): 61-70, 2020

5 김선태, "가축분뇨 이동과 처리를 고려한 국가 암모니아 인벤토리 개선 연구" 한국냄새환경학회 14 (14): 182-189, 2015

6 Hochreiter, S., "Long short-term memory" 9 (9): 1735-1780, 1997

7 Cho, K. H., "Learning phrase representations using RNN encoder-decoder for statistical machine translation" 1724-1734, 2014

8 Hempel, S., "How the selection of training data and modeling approach affects the estimation of ammonia emissions from a naturally ventilated dairy barn-Classical statistics versus machine learning" 12 (12): 1030-, 2020

9 Géron, A., "Hands-on machine learning with ScikitLearn, Keras and TensorFlow : concepts, tools, and techniques to build intelligent systems" O’Reilly Media 851-, 2019

10 Blanes-Vidal, V., "Emissions of ammonia, methane and nitrous oxide from pig houses and slurry: Effects of rooting material, animal activity and ventilation flow" 124 (124): 237-244, 2008

11 Aarnink, A. J. A., "Effect of slatted floor area on ammonia emission and on the excretory and lying behaviour of growing pigs" 64 (64): 299-310, 1996

12 Jeppsson, K. H., "Diurnal variation in ammonia, carbon dioxide and water vapour emission from an uninsulated, deep litter building for growing/finishing pigs" 81 (81): 213-223, 2002

13 Liao, Q., "Deep Learning for Air Quality Forecasts : a Review" 6 (6): 399-409, 2020

14 Ahmed, N. K., "An empirical comparison of machine learning models for time series forecasting" 29 (29): 594-621, 2010

15 Aarnink, A. J. A., "Ammonia emission patterns during the growing periods of pigs housed on partially slatted floors" 62 (62): 105-116, 1995

16 Jo, G., "Ammonia emission characteristics of a mechanically ventilated swine finishing facility in korea" 11 (11): 1088-, 2020

17 Costa, A., "Ammonia Concentrations and Emissions from Finishing Pigs Reared in Different Growing Rooms" 46 (46): 255-260, 2017

18 Kingma, D. P., "Adam : A method for stochastic optimization" 2015

19 Xie, Q., "A prediction model of ammonia emission from a fattening pig room based on the indoor concentration using adaptive neuro fuzzy inference system" 325 : 301-309, 2017

20 Sit, M., "A comprehensive review of deep learning applications in hydrology and water resources" 82 (82): 2635-2670, 2020

21 Minitstry of environment (ME), "2017 national air pollutants emission"