Attention-based generative adversarial networks for aquaponics environment time series data imputation
Environmental parameter data collected by sensors for monitoring the environment of agricultural facility operations are usually incomplete due to external environmental disturbances and device failures. And the missing of collected data is completely at random. In practice, missing data could creat...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Information Processing in Agriculture |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S221431732300077X |
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| author | Keyang Zhong Xueqian Sun Gedi Liu Yifeng Jiang Yi Ouyang Yang Wang |
| author_facet | Keyang Zhong Xueqian Sun Gedi Liu Yifeng Jiang Yi Ouyang Yang Wang |
| author_sort | Keyang Zhong |
| collection | DOAJ |
| description | Environmental parameter data collected by sensors for monitoring the environment of agricultural facility operations are usually incomplete due to external environmental disturbances and device failures. And the missing of collected data is completely at random. In practice, missing data could create biased estimations and make multivariate time series predictions of environmental parameters difficult, leading to imprecise environmental control. A multivariate time series imputation model based on generative adversarial networks and multi-head attention (ATTN-GAN) is proposed in this work to reducing the negative consequence of missing data. ATTN-GAN can capture the temporal and spatial correlation of time series, and has a good capacity to learn data distribution. In the downstream experiments, we used ATTN-GAN and baseline models for data imputation, and predicted the imputed data, respectively. For the imputation of missing data, over the 20%, 50% and 80% missing rate, ATTN-GAN had the lowest RMSE, 0.1593, 0.2012 and 0.2688 respectively. For water temperature prediction, data processed with ATTN-GAN over MLP, LSTM, DA-RNN prediction methods had the lowest MSE, 0.6816, 0.8375 and 0.3736 respectively. Those results revealed that ATTN-GAN outperformed all baseline models in terms of data imputation accuracy. The data processed by ATTN-GAN is the best for time series prediction. |
| format | Article |
| id | doaj-art-955a41785ba7455cbc10c82d893dd724 |
| institution | OA Journals |
| issn | 2214-3173 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Information Processing in Agriculture |
| spelling | doaj-art-955a41785ba7455cbc10c82d893dd7242025-08-20T02:39:07ZengElsevierInformation Processing in Agriculture2214-31732024-12-0111454255110.1016/j.inpa.2023.10.001Attention-based generative adversarial networks for aquaponics environment time series data imputationKeyang Zhong0Xueqian Sun1Gedi Liu2Yifeng Jiang3Yi Ouyang4Yang Wang5National Innovation Center for Digital Fishery, China Agricultural University, 100083 Beijing, China; Key Laboratory of Smart Farming Technologies for Aquatic Animal and Livestock, Ministry of Agriculture and Rural Affairs, China Agricultural University, 100083 Beijing, China; Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, China Agricultural University, 100083 Beijing, China; College of Information and Electrical Engineering, China Agricultural University, 100083 Beijing, ChinaNational Innovation Center for Digital Fishery, China Agricultural University, 100083 Beijing, China; Key Laboratory of Smart Farming Technologies for Aquatic Animal and Livestock, Ministry of Agriculture and Rural Affairs, China Agricultural University, 100083 Beijing, China; Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, China Agricultural University, 100083 Beijing, China; College of Information and Electrical Engineering, China Agricultural University, 100083 Beijing, ChinaNational Innovation Center for Digital Fishery, China Agricultural University, 100083 Beijing, China; Key Laboratory of Smart Farming Technologies for Aquatic Animal and Livestock, Ministry of Agriculture and Rural Affairs, China Agricultural University, 100083 Beijing, China; Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, China Agricultural University, 100083 Beijing, China; College of Information and Electrical Engineering, China Agricultural University, 100083 Beijing, ChinaNational Innovation Center for Digital Fishery, China Agricultural University, 100083 Beijing, China; Key Laboratory of Smart Farming Technologies for Aquatic Animal and Livestock, Ministry of Agriculture and Rural Affairs, China Agricultural University, 100083 Beijing, China; Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, China Agricultural University, 100083 Beijing, China; College of Information and Electrical Engineering, China Agricultural University, 100083 Beijing, ChinaNational Innovation Center for Digital Fishery, China Agricultural University, 100083 Beijing, China; Key Laboratory of Smart Farming Technologies for Aquatic Animal and Livestock, Ministry of Agriculture and Rural Affairs, China Agricultural University, 100083 Beijing, China; Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, China Agricultural University, 100083 Beijing, China; College of Information and Electrical Engineering, China Agricultural University, 100083 Beijing, ChinaNational Innovation Center for Digital Fishery, China Agricultural University, 100083 Beijing, China; Key Laboratory of Smart Farming Technologies for Aquatic Animal and Livestock, Ministry of Agriculture and Rural Affairs, China Agricultural University, 100083 Beijing, China; Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, China Agricultural University, 100083 Beijing, China; College of Information and Electrical Engineering, China Agricultural University, 100083 Beijing, China; Corresponding author at: College of Information and Electrical Engineering, China Agricultural University, 100083 Beijing, China.Environmental parameter data collected by sensors for monitoring the environment of agricultural facility operations are usually incomplete due to external environmental disturbances and device failures. And the missing of collected data is completely at random. In practice, missing data could create biased estimations and make multivariate time series predictions of environmental parameters difficult, leading to imprecise environmental control. A multivariate time series imputation model based on generative adversarial networks and multi-head attention (ATTN-GAN) is proposed in this work to reducing the negative consequence of missing data. ATTN-GAN can capture the temporal and spatial correlation of time series, and has a good capacity to learn data distribution. In the downstream experiments, we used ATTN-GAN and baseline models for data imputation, and predicted the imputed data, respectively. For the imputation of missing data, over the 20%, 50% and 80% missing rate, ATTN-GAN had the lowest RMSE, 0.1593, 0.2012 and 0.2688 respectively. For water temperature prediction, data processed with ATTN-GAN over MLP, LSTM, DA-RNN prediction methods had the lowest MSE, 0.6816, 0.8375 and 0.3736 respectively. Those results revealed that ATTN-GAN outperformed all baseline models in terms of data imputation accuracy. The data processed by ATTN-GAN is the best for time series prediction.http://www.sciencedirect.com/science/article/pii/S221431732300077XAttention-based Generative Adversarial NetworksAquaponics GreenhouseMissing DataData ImputationMultivariate Time Series |
| spellingShingle | Keyang Zhong Xueqian Sun Gedi Liu Yifeng Jiang Yi Ouyang Yang Wang Attention-based generative adversarial networks for aquaponics environment time series data imputation Information Processing in Agriculture Attention-based Generative Adversarial Networks Aquaponics Greenhouse Missing Data Data Imputation Multivariate Time Series |
| title | Attention-based generative adversarial networks for aquaponics environment time series data imputation |
| title_full | Attention-based generative adversarial networks for aquaponics environment time series data imputation |
| title_fullStr | Attention-based generative adversarial networks for aquaponics environment time series data imputation |
| title_full_unstemmed | Attention-based generative adversarial networks for aquaponics environment time series data imputation |
| title_short | Attention-based generative adversarial networks for aquaponics environment time series data imputation |
| title_sort | attention based generative adversarial networks for aquaponics environment time series data imputation |
| topic | Attention-based Generative Adversarial Networks Aquaponics Greenhouse Missing Data Data Imputation Multivariate Time Series |
| url | http://www.sciencedirect.com/science/article/pii/S221431732300077X |
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