A hybrid model for dissolved oxygen prediction in aquaculture based on multi-scale features
To increase prediction accuracy of dissolved oxygen (DO) in aquaculture, a hybrid model based on multi-scale features using ensemble empirical mode decomposition (EEMD) is proposed. Firstly, original DO datasets are decomposed by EEMD and we get several components. Secondly, these components are use...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2018-03-01
|
| Series: | Information Processing in Agriculture |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214317317301208 |
| _version_ | 1797766596148592640 |
|---|---|
| author | Chen Li Zhenbo Li Jing Wu Ling Zhu Jun Yue |
| author_facet | Chen Li Zhenbo Li Jing Wu Ling Zhu Jun Yue |
| author_sort | Chen Li |
| collection | DOAJ |
| description | To increase prediction accuracy of dissolved oxygen (DO) in aquaculture, a hybrid model based on multi-scale features using ensemble empirical mode decomposition (EEMD) is proposed. Firstly, original DO datasets are decomposed by EEMD and we get several components. Secondly, these components are used to reconstruct four terms including high frequency term, intermediate frequency term, low frequency term and trend term. Thirdly, according to the characteristics of high and intermediate frequency terms, which fluctuate violently, the least squares support vector machine (LSSVR) is used to predict the two terms. The fluctuation of low frequency term is gentle and periodic, so it can be modeled by BP neural network with an optimal mind evolutionary computation (MEC-BP). Then, the trend term is predicted using grey model (GM) because it is nearly linear. Finally, the prediction values of DO datasets are calculated by the sum of the forecasting values of all terms. The experimental results demonstrate that our hybrid model outperforms EEMD-ELM (extreme learning machine based on EEMD), EEMD-BP and MEC-BP models based on the mean absolute error (MAE), mean absolute percentage error (MAPE), mean square error (MSE) and root mean square error (RMSE). Our hybrid model is proven to be an effective approach to predict aquaculture DO. |
| first_indexed | 2024-03-12T20:27:31Z |
| format | Article |
| id | doaj.art-a55a312f8fd3405cb909da8339ed1d52 |
| institution | Directory Open Access Journal |
| issn | 2214-3173 |
| language | English |
| last_indexed | 2024-03-12T20:27:31Z |
| publishDate | 2018-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Information Processing in Agriculture |
| spelling | doaj.art-a55a312f8fd3405cb909da8339ed1d522023-08-02T00:24:27ZengElsevierInformation Processing in Agriculture2214-31732018-03-0151112010.1016/j.inpa.2017.11.002A hybrid model for dissolved oxygen prediction in aquaculture based on multi-scale featuresChen Li0Zhenbo Li1Jing Wu2Ling Zhu3Jun Yue4College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, ChinaCollege of Information and Electrical Engineering, China Agricultural University, Beijing 100083, ChinaCollege of Information and Electrical Engineering, China Agricultural University, Beijing 100083, ChinaCollege of Information and Electrical Engineering, China Agricultural University, Beijing 100083, ChinaCollege of Information and Electrical Engineering, Ludong University, Yantai, Shandong 264025, ChinaTo increase prediction accuracy of dissolved oxygen (DO) in aquaculture, a hybrid model based on multi-scale features using ensemble empirical mode decomposition (EEMD) is proposed. Firstly, original DO datasets are decomposed by EEMD and we get several components. Secondly, these components are used to reconstruct four terms including high frequency term, intermediate frequency term, low frequency term and trend term. Thirdly, according to the characteristics of high and intermediate frequency terms, which fluctuate violently, the least squares support vector machine (LSSVR) is used to predict the two terms. The fluctuation of low frequency term is gentle and periodic, so it can be modeled by BP neural network with an optimal mind evolutionary computation (MEC-BP). Then, the trend term is predicted using grey model (GM) because it is nearly linear. Finally, the prediction values of DO datasets are calculated by the sum of the forecasting values of all terms. The experimental results demonstrate that our hybrid model outperforms EEMD-ELM (extreme learning machine based on EEMD), EEMD-BP and MEC-BP models based on the mean absolute error (MAE), mean absolute percentage error (MAPE), mean square error (MSE) and root mean square error (RMSE). Our hybrid model is proven to be an effective approach to predict aquaculture DO.http://www.sciencedirect.com/science/article/pii/S2214317317301208DO predictionAquacultureHybrid model |
| spellingShingle | Chen Li Zhenbo Li Jing Wu Ling Zhu Jun Yue A hybrid model for dissolved oxygen prediction in aquaculture based on multi-scale features Information Processing in Agriculture DO prediction Aquaculture Hybrid model |
| title | A hybrid model for dissolved oxygen prediction in aquaculture based on multi-scale features |
| title_full | A hybrid model for dissolved oxygen prediction in aquaculture based on multi-scale features |
| title_fullStr | A hybrid model for dissolved oxygen prediction in aquaculture based on multi-scale features |
| title_full_unstemmed | A hybrid model for dissolved oxygen prediction in aquaculture based on multi-scale features |
| title_short | A hybrid model for dissolved oxygen prediction in aquaculture based on multi-scale features |
| title_sort | hybrid model for dissolved oxygen prediction in aquaculture based on multi scale features |
| topic | DO prediction Aquaculture Hybrid model |
| url | http://www.sciencedirect.com/science/article/pii/S2214317317301208 |
| work_keys_str_mv | AT chenli ahybridmodelfordissolvedoxygenpredictioninaquaculturebasedonmultiscalefeatures AT zhenboli ahybridmodelfordissolvedoxygenpredictioninaquaculturebasedonmultiscalefeatures AT jingwu ahybridmodelfordissolvedoxygenpredictioninaquaculturebasedonmultiscalefeatures AT lingzhu ahybridmodelfordissolvedoxygenpredictioninaquaculturebasedonmultiscalefeatures AT junyue ahybridmodelfordissolvedoxygenpredictioninaquaculturebasedonmultiscalefeatures AT chenli hybridmodelfordissolvedoxygenpredictioninaquaculturebasedonmultiscalefeatures AT zhenboli hybridmodelfordissolvedoxygenpredictioninaquaculturebasedonmultiscalefeatures AT jingwu hybridmodelfordissolvedoxygenpredictioninaquaculturebasedonmultiscalefeatures AT lingzhu hybridmodelfordissolvedoxygenpredictioninaquaculturebasedonmultiscalefeatures AT junyue hybridmodelfordissolvedoxygenpredictioninaquaculturebasedonmultiscalefeatures |