A hybrid intelligent soft computing method for ammonia nitrogen prediction in aquaculture
Ammonia nitrogen is one of the key parameters in determining the aquaculture water quality condition in pond. The high level of ammonia nitrogen is likely to cause deterioration of water quality and mass death of cultured subjects. Therefore, accurate detection of the cultured water ammonia nitrogen...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-03-01
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| Series: | Information Processing in Agriculture |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214317320300342 |
| _version_ | 1797706660617125888 |
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| author | Huihui Yu Ling Yang Daoliang Li Yingyi Chen |
| author_facet | Huihui Yu Ling Yang Daoliang Li Yingyi Chen |
| author_sort | Huihui Yu |
| collection | DOAJ |
| description | Ammonia nitrogen is one of the key parameters in determining the aquaculture water quality condition in pond. The high level of ammonia nitrogen is likely to cause deterioration of water quality and mass death of cultured subjects. Therefore, accurate detection of the cultured water ammonia nitrogen content is crucially important for aquaculture management. While, at present, the accuracy of equipment for measuring the ammonia nitrogen content of aquaculture water in real time cannot meet the requirements for aquaculture. In this paper, the soft computing method is firstly proposed to predict the ammonia nitrogen content in aquaculture water in real time. This method includes empirical mode decomposition (EMD), improved particle swarm optimization (IPSO) and extreme learning machine (ELM). To evaluate the performance of the soft computing techniques, three different statistic indicators were used, including root mean square error (RMSE), the mean absolute error (MAE), and the mean absolute percentage error (MAPE) to compare three artificial soft computing methods. Results showed that the EMD-IPSO-ELM soft computing method showed the best performance among other studied methods in the ammonia nitrogen real time prediction. The EMD-IPSO-ELM model provides moderately and roughly accurately real time prediction value of ammonia nitrogen in aquaculture water. |
| first_indexed | 2024-03-12T05:55:35Z |
| format | Article |
| id | doaj.art-8a6a7e26bde14058827da61d4f040042 |
| institution | Directory Open Access Journal |
| issn | 2214-3173 |
| language | English |
| last_indexed | 2024-03-12T05:55:35Z |
| publishDate | 2021-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Information Processing in Agriculture |
| spelling | doaj.art-8a6a7e26bde14058827da61d4f0400422023-09-03T04:39:47ZengElsevierInformation Processing in Agriculture2214-31732021-03-01816474A hybrid intelligent soft computing method for ammonia nitrogen prediction in aquacultureHuihui Yu0Ling Yang1Daoliang Li2Yingyi Chen3School of Information Science and Technology, Beijing Forestry University, Beijing 100083, PR ChinaCollege of Information and Electrical Engineering, China Agricultural University, Beijing 100083, PR China; Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing 100083, PR China; Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing 100083, PR ChinaCollege of Information and Electrical Engineering, China Agricultural University, Beijing 100083, PR China; Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing 100083, PR China; Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing 100083, PR ChinaCollege of Information and Electrical Engineering, China Agricultural University, Beijing 100083, PR China; Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing 100083, PR China; Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing 100083, PR China; Corresponding author at: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, PR China.Ammonia nitrogen is one of the key parameters in determining the aquaculture water quality condition in pond. The high level of ammonia nitrogen is likely to cause deterioration of water quality and mass death of cultured subjects. Therefore, accurate detection of the cultured water ammonia nitrogen content is crucially important for aquaculture management. While, at present, the accuracy of equipment for measuring the ammonia nitrogen content of aquaculture water in real time cannot meet the requirements for aquaculture. In this paper, the soft computing method is firstly proposed to predict the ammonia nitrogen content in aquaculture water in real time. This method includes empirical mode decomposition (EMD), improved particle swarm optimization (IPSO) and extreme learning machine (ELM). To evaluate the performance of the soft computing techniques, three different statistic indicators were used, including root mean square error (RMSE), the mean absolute error (MAE), and the mean absolute percentage error (MAPE) to compare three artificial soft computing methods. Results showed that the EMD-IPSO-ELM soft computing method showed the best performance among other studied methods in the ammonia nitrogen real time prediction. The EMD-IPSO-ELM model provides moderately and roughly accurately real time prediction value of ammonia nitrogen in aquaculture water.http://www.sciencedirect.com/science/article/pii/S2214317320300342Ammonia nitrogen predictionExtreme learning machineSoft computingAquaculture |
| spellingShingle | Huihui Yu Ling Yang Daoliang Li Yingyi Chen A hybrid intelligent soft computing method for ammonia nitrogen prediction in aquaculture Information Processing in Agriculture Ammonia nitrogen prediction Extreme learning machine Soft computing Aquaculture |
| title | A hybrid intelligent soft computing method for ammonia nitrogen prediction in aquaculture |
| title_full | A hybrid intelligent soft computing method for ammonia nitrogen prediction in aquaculture |
| title_fullStr | A hybrid intelligent soft computing method for ammonia nitrogen prediction in aquaculture |
| title_full_unstemmed | A hybrid intelligent soft computing method for ammonia nitrogen prediction in aquaculture |
| title_short | A hybrid intelligent soft computing method for ammonia nitrogen prediction in aquaculture |
| title_sort | hybrid intelligent soft computing method for ammonia nitrogen prediction in aquaculture |
| topic | Ammonia nitrogen prediction Extreme learning machine Soft computing Aquaculture |
| url | http://www.sciencedirect.com/science/article/pii/S2214317320300342 |
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