Artificial neural network modeling of dissolved oxygen in the Heihe River, Northwestern China
文献类型：期刊论文
| 作者 | Wen, Xiaohu1,2; Fang, Jing3; Diao, Meina1,2,4; Zhang, Chuanqi1,2,4 |
| 刊名 | ENVIRONMENTAL MONITORING AND ASSESSMENT
 |
| 出版日期 | 2013-05-01 |
| 卷号 | 185期号:5页码:4361-4371 |
| 关键词 | Artificial Neural Network Dissolved Oxygen Modeling Heihe River |
| ISSN号 | 0167-6369 |
| 产权排序 | [Wen, Xiaohu; Diao, Meina; Zhang, Chuanqi] Chinese Acad Sci, Key Lab Coastal Zone Environm Proc, Yantai Inst Coastal Zone Res, Yantai 264003, Shandong, Peoples R China; [Wen, Xiaohu; Diao, Meina; Zhang, Chuanqi] Chinese Acad Sci, Shandong Prov Key Lab Coastal Zone Environm Proc, Yantai Inst Coastal Zone Res, Yantai 264003, Shandong, Peoples R China; [Fang, Jing] Chinese Acad Sci, Cold & Arid Reg Environm & Engn Res Inst, Lanzhou 730000, Gansu, Peoples R China; [Diao, Meina; Zhang, Chuanqi] Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
| 通讯作者 | Wen, XH (reprint author), Chinese Acad Sci, Key Lab Coastal Zone Environm Proc, Yantai Inst Coastal Zone Res, Chunhui Rd 17, Yantai 264003, Shandong, Peoples R China. xhwen@yic.ac.cn |
| 文献子类 | Article |
| 英文摘要 | Identification and quantification of dissolved oxygen (DO) profiles of river is one of the primary concerns for water resources managers. In this research, an artificial neural network (ANN) was developed to simulate the DO concentrations in the Heihe River, Northwestern China. A three-layer back-propagation ANN was used with the Bayesian regularization training algorithm. The input variables of the neural network were pH, electrical conductivity, chloride (Cl-), calcium (Ca2+), total alkalinity, total hardness, nitrate nitrogen (NO3-N), and ammonical nitrogen (NH4-N). The ANN structure with 14 hidden neurons obtained the best selection. By making comparison between the results of the ANN model and the measured data on the basis of correlation coefficient (r) and root mean square error (RMSE), a good model-fitting DO values indicated the effectiveness of neural network model. It is found that the coefficient of correlation (r) values for the training, validation, and test sets were 0.9654, 0.9841, and 0.9680, respectively, and the respective values of RMSE for the training, validation, and test sets were 0.4272, 0.3667, and 0.4570, respectively. Sensitivity analysis was used to determine the influence of input variables on the dependent variable. The most effective inputs were determined as pH, NO3-N, NH4-N, and Ca2+. Cl- was found to be least effective variables on the proposed model. The identified ANN model can be used to simulate the water quality parameters.; Identification and quantification of dissolved oxygen (DO) profiles of river is one of the primary concerns for water resources managers. In this research, an artificial neural network (ANN) was developed to simulate the DO concentrations in the Heihe River, Northwestern China. A three-layer back-propagation ANN was used with the Bayesian regularization training algorithm. The input variables of the neural network were pH, electrical conductivity, chloride (Cl-), calcium (Ca2+), total alkalinity, total hardness, nitrate nitrogen (NO3-N), and ammonical nitrogen (NH4-N). The ANN structure with 14 hidden neurons obtained the best selection. By making comparison between the results of the ANN model and the measured data on the basis of correlation coefficient (r) and root mean square error (RMSE), a good model-fitting DO values indicated the effectiveness of neural network model. It is found that the coefficient of correlation (r) values for the training, validation, and test sets were 0.9654, 0.9841, and 0.9680, respectively, and the respective values of RMSE for the training, validation, and test sets were 0.4272, 0.3667, and 0.4570, respectively. Sensitivity analysis was used to determine the influence of input variables on the dependent variable. The most effective inputs were determined as pH, NO3-N, NH4-N, and Ca2+. Cl- was found to be least effective variables on the proposed model. The identified ANN model can be used to simulate the water quality parameters. |
| 学科主题 | Environmental Sciences |
| URL标识 | 查看原文 |
| WOS关键词 | WATER-TABLE DEPTH ; AGRICULTURAL CATCHMENT ; QUALITY ; PREDICTION ; RUNOFF ; ANN ; PERFORMANCE ; MANAGEMENT ; VARIABLES ; DYNAMICS |
| WOS研究方向 | Environmental Sciences & Ecology |
| 语种 | 英语 |
| WOS记录号 | WOS:000316968500061 |
| 资助机构 | One Hundred Person Project of the Chinese Academy of Sciences [29Y127D01]; National Natural Science Foundation of China [41171026, 91025024] |
| 公开日期 | 2013-08-15 |
| 源URL | [http://ir.yic.ac.cn/handle/133337/6511]  |
| 专题 | 烟台海岸带研究所_海岸带信息集成与综合管理实验室 |
| 作者单位 | 1.Chinese Acad Sci, Key Lab Coastal Zone Environm Proc, Yantai Inst Coastal Zone Res, Yantai 264003, Shandong, Peoples R China 2.Chinese Acad Sci, Shandong Prov Key Lab Coastal Zone Environm Proc, Yantai Inst Coastal Zone Res, Yantai 264003, Shandong, Peoples R China 3.Chinese Acad Sci, Cold & Arid Reg Environm & Engn Res Inst, Lanzhou 730000, Gansu, Peoples R China 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wen, Xiaohu,Fang, Jing,Diao, Meina,et al. Artificial neural network modeling of dissolved oxygen in the Heihe River, Northwestern China[J]. ENVIRONMENTAL MONITORING AND ASSESSMENT,2013,185(5):4361-4371. |
| APA | Wen, Xiaohu,Fang, Jing,Diao, Meina,&Zhang, Chuanqi.(2013).Artificial neural network modeling of dissolved oxygen in the Heihe River, Northwestern China.ENVIRONMENTAL MONITORING AND ASSESSMENT,185(5),4361-4371. |
| MLA | Wen, Xiaohu,et al."Artificial neural network modeling of dissolved oxygen in the Heihe River, Northwestern China".ENVIRONMENTAL MONITORING AND ASSESSMENT 185.5(2013):4361-4371. |
入库方式： OAI收割
来源：烟台海岸带研究所
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