The Adaptive-Clustering and Error-Correction Method for Forecasting Cyanobacteria Blooms in Lakes and Reservoirs
Globally, cyanobacteria blooms frequently occur, and effective prediction of cyanobacteria blooms in lakes and reservoirs could constitute an essential proactive strategy for water-resource protection. However, cyanobacteria blooms are very complicated because of the internal stochastic nature of th...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Advances in Mathematical Physics |
| Online Access: | http://dx.doi.org/10.1155/2017/9037358 |
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| author | Xiao-zhe Bai Hui-yan Zhang Xiao-yi Wang Li Wang Ji-ping Xu Jia-bin Yu |
| author_facet | Xiao-zhe Bai Hui-yan Zhang Xiao-yi Wang Li Wang Ji-ping Xu Jia-bin Yu |
| author_sort | Xiao-zhe Bai |
| collection | DOAJ |
| description | Globally, cyanobacteria blooms frequently occur, and effective prediction of cyanobacteria blooms in lakes and reservoirs could constitute an essential proactive strategy for water-resource protection. However, cyanobacteria blooms are very complicated because of the internal stochastic nature of the system evolution and the external uncertainty of the observation data. In this study, an adaptive-clustering algorithm is introduced to obtain some typical operating intervals. In addition, the number of nearest neighbors used for modeling was optimized by particle swarm optimization. Finally, a fuzzy linear regression method based on error-correction was used to revise the model dynamically near the operating point. We found that the combined method can characterize the evolutionary track of cyanobacteria blooms in lakes and reservoirs. The model constructed in this paper is compared to other cyanobacteria-bloom forecasting methods (e.g., phase space reconstruction and traditional-clustering linear regression), and, then, the average relative error and average absolute error are used to compare the accuracies of these models. The results suggest that the proposed model is superior. As such, the newly developed approach achieves more precise predictions, which can be used to prevent the further deterioration of the water environment. |
| format | Article |
| id | doaj-art-6a1d87f247aa4294917d27cd53ef8f2c |
| institution | Kabale University |
| issn | 1687-9120 1687-9139 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Mathematical Physics |
| spelling | doaj-art-6a1d87f247aa4294917d27cd53ef8f2c2025-08-20T03:33:39ZengWileyAdvances in Mathematical Physics1687-91201687-91392017-01-01201710.1155/2017/90373589037358The Adaptive-Clustering and Error-Correction Method for Forecasting Cyanobacteria Blooms in Lakes and ReservoirsXiao-zhe Bai0Hui-yan Zhang1Xiao-yi Wang2Li Wang3Ji-ping Xu4Jia-bin Yu5School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, ChinaSchool of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, ChinaSchool of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, ChinaSchool of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, ChinaSchool of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, ChinaSchool of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, ChinaGlobally, cyanobacteria blooms frequently occur, and effective prediction of cyanobacteria blooms in lakes and reservoirs could constitute an essential proactive strategy for water-resource protection. However, cyanobacteria blooms are very complicated because of the internal stochastic nature of the system evolution and the external uncertainty of the observation data. In this study, an adaptive-clustering algorithm is introduced to obtain some typical operating intervals. In addition, the number of nearest neighbors used for modeling was optimized by particle swarm optimization. Finally, a fuzzy linear regression method based on error-correction was used to revise the model dynamically near the operating point. We found that the combined method can characterize the evolutionary track of cyanobacteria blooms in lakes and reservoirs. The model constructed in this paper is compared to other cyanobacteria-bloom forecasting methods (e.g., phase space reconstruction and traditional-clustering linear regression), and, then, the average relative error and average absolute error are used to compare the accuracies of these models. The results suggest that the proposed model is superior. As such, the newly developed approach achieves more precise predictions, which can be used to prevent the further deterioration of the water environment.http://dx.doi.org/10.1155/2017/9037358 |
| spellingShingle | Xiao-zhe Bai Hui-yan Zhang Xiao-yi Wang Li Wang Ji-ping Xu Jia-bin Yu The Adaptive-Clustering and Error-Correction Method for Forecasting Cyanobacteria Blooms in Lakes and Reservoirs Advances in Mathematical Physics |
| title | The Adaptive-Clustering and Error-Correction Method for Forecasting Cyanobacteria Blooms in Lakes and Reservoirs |
| title_full | The Adaptive-Clustering and Error-Correction Method for Forecasting Cyanobacteria Blooms in Lakes and Reservoirs |
| title_fullStr | The Adaptive-Clustering and Error-Correction Method for Forecasting Cyanobacteria Blooms in Lakes and Reservoirs |
| title_full_unstemmed | The Adaptive-Clustering and Error-Correction Method for Forecasting Cyanobacteria Blooms in Lakes and Reservoirs |
| title_short | The Adaptive-Clustering and Error-Correction Method for Forecasting Cyanobacteria Blooms in Lakes and Reservoirs |
| title_sort | adaptive clustering and error correction method for forecasting cyanobacteria blooms in lakes and reservoirs |
| url | http://dx.doi.org/10.1155/2017/9037358 |
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