Temporal prediction of algal parameters in Three Gorges Reservoir based on highly time-resolved monitoring and long short-term memory network
文献类型：期刊论文
| 作者 | Shan, Kun1,2 ; Ouyang, Tian3; Wang, Xiaoxiao1,2; Yang, Hong4; Zhou, Botian1,2; Wu, Zhongxing3; Shang, Mingsheng1,2
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| 刊名 | JOURNAL OF HYDROLOGY
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| 出版日期 | 2022-02-01 |
| 卷号 | 605页码:12 |
| ISSN号 | 0022-1694 |
| 关键词 | Harmful algal bloom Real-time monitoring Long short-term memory network Microcystin Three Gorges Reservoir |
| DOI | 10.1016/j.jhydrol.2021.127304 |
| 通讯作者 | Shan, Kun(shankun@cigit.ac.cn) |
| 英文摘要 | Many dammed rivers throughout the world have experienced frequent harmful algal blooms (HABs) in the context of climate change and anthropogenic activities. Accurate forecasting of algal parameters (i.e., algal cell density and microcystin concentration) has great practical significance for taking precautions against HABs risks. Long short-term memory (LSTM) networks have recently shown potential in predicting water quality parameters. However, there is still little known about the robustness of the LSTM in forecasting highly time-resolved measurement of algal parameters. This study developed a hybrid deep-learning architecture (XG-LSTM) composed of one XGBoost module and two parallel LSTM models to predict algal cell density and microcystin concentration in the Three Gorges Reservoir (TGR). The proposed model was validated by in situ multi-sensor-system monitoring data at four bloom-impacted tributaries in the TGR. Each modelling process utilized the antecedent information of the algal parameters and the corresponding environmental variables as inputs for forecasting the algal parameters for the coming hours and days. As expected, the presented model achieved better performance than those without special feature extraction procedures, providing that the use of selected environmental parameters can improve LSTM performance. In addition, the hybrid XG-LSTM model successfully captured the time-series patterns of both algal cell density and microcystin concentration compared with other data-driven models, further suggesting the reliable utilization of this model in early warnings of bloom toxicity. Thus, the results presented demonstrate the potential of deep learning technology for real-time prediction of algal parameters in the TGR, and possibly for rapid detection of developing HABs in other aquatic ecosystems. |
| 资助项目 | National Natural Science Foundation of China[62072429] ; National Natural Science Foundation of China[51609229] ; National Natural Science Foundation of China[41901366] ; Chongqing Science and Technology Commission[cstc2019jscx-gksbX0042] ; West Light Foundation of The Chinese Academy of Sciences[E1296001] ; China National Critical Project[2014ZX07104-006] ; key cooperation project of Chongqing Municipal Education Commission[HZ2021008] |
| WOS研究方向 | Engineering ; Geology ; Water Resources |
| 语种 | 英语 |
| 出版者 | ELSEVIER |
| WOS记录号 | WOS:000752499500002 |
| 源URL | [http://119.78.100.138/handle/2HOD01W0/15251]  |
| 专题 | 中国科学院重庆绿色智能技术研究院 |
| 通讯作者 | Shan, Kun |
| 作者单位 | 1.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, CAS Key Lab Reservoir Environm, Chongqing 400714, Peoples R China 2.Univ Chinese Acad Sci, Chongqing Sch, Chongqing 400714, Peoples R China 3.Southwest Univ, Sch Life Sci, Key Lab Ecoenvironm Three Gorges Reservoir Reg, Minist Educ, Chongqing 400715, Peoples R China 4.Univ Reading, Dept Geog & Environm Sci, Whiteknights Reading RG6 6AB, England |
| 推荐引用方式 GB/T 7714 | Shan, Kun,Ouyang, Tian,Wang, Xiaoxiao,et al. Temporal prediction of algal parameters in Three Gorges Reservoir based on highly time-resolved monitoring and long short-term memory network[J]. JOURNAL OF HYDROLOGY,2022,605:12. |
| APA | Shan, Kun.,Ouyang, Tian.,Wang, Xiaoxiao.,Yang, Hong.,Zhou, Botian.,...&Shang, Mingsheng.(2022).Temporal prediction of algal parameters in Three Gorges Reservoir based on highly time-resolved monitoring and long short-term memory network.JOURNAL OF HYDROLOGY,605,12. |
| MLA | Shan, Kun,et al."Temporal prediction of algal parameters in Three Gorges Reservoir based on highly time-resolved monitoring and long short-term memory network".JOURNAL OF HYDROLOGY 605(2022):12. |
入库方式： OAI收割
来源：重庆绿色智能技术研究院
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