| Artificial neural network modeling of dissolved oxygen in the Heihe River, Northwestern China | |
| Wen, Xiaohu1,2; Fang, Jing3; Diao, Meina1,2,4; Zhang, Chuanqi1,2,4 | |
| 发表期刊 | ENVIRONMENTAL MONITORING AND ASSESSMENT
 |
| ISSN | 0167-6369 |
| 2013-05-01 | |
| 卷号 | 185期号:5页码:4361-4371 |
| 关键词 | Artificial Neural Network Dissolved Oxygen Modeling Heihe River |
| 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. [email protected] |
| 作者部门 | 海岸带信息集成与综合管理实验室 |
| 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. |
| 文章类型 | Article |
| 资助机构 | One Hundred Person Project of the Chinese Academy of Sciences [29Y127D01]; National Natural Science Foundation of China [41171026, 91025024] |
| 收录类别 | SCI |
| 语种 | 英语 |
| 关键词[WOS] | WATER-TABLE DEPTH ; AGRICULTURAL CATCHMENT ; QUALITY ; PREDICTION ; RUNOFF ; ANN ; PERFORMANCE ; MANAGEMENT ; VARIABLES ; DYNAMICS |
| 研究领域[WOS] | Environmental Sciences & Ecology |
| WOS记录号 | WOS:000316968500061 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.yic.ac.cn/handle/133337/6510 |
| 专题 | 中国科学院海岸带环境过程与生态修复重点实验室_海岸带信息集成与战略规划研究中心 |
| 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. |
| 条目包含的文件 | 下载所有文件 | |||||
| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
| Artificial neural ne（388KB） | 开放获取 | -- | 浏览 下载 | |||
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。
修改评论