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
ISSN0167-6369
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. xhwen@yic.ac.cn
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
被引频次:76[WOS]   [WOS记录]     [WOS相关记录]
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
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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.
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