Prediction of water quality based on artificial neural network with grey theory

被引：6
|
作者：
Zhai, W. [1 ]
Zhou, X. [2 ,3 ]
Man, J. [4 ]
Xu, Q. [1 ]
Jiang, Q. [1 ]
Yang, Z. [1 ]
Jiang, L. [1 ]
Gao, Z. [1 ]
Yuan, Y. [1 ]
Gao, W. [1 ]
机构：
[1] Ningbo Univ Technol, Sch Safety Engn, Ningbo 315211, Zhejiang, Peoples R China
[2] Wuhan Univ Technol, Sch Safety Sci & Emergency Management, Wuhan 430070, Hubei, Peoples R China
[3] Wuhan Univ Technol, Sch Resources & Environm Engn, Wuhan 430070, Hubei, Peoples R China
[4] Ningbo Liwah Pharmaceut Co Ltd, Zhenhai Branch, Ningbo 315204, Zhejiang, Peoples R China
来源：
2019 5TH INTERNATIONAL CONFERENCE ON ENERGY MATERIALS AND ENVIRONMENT ENGINEERING | 2019年 / 295卷
关键词：
D O I：
10.1088/1755-1315/295/4/042009
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
In this paper, the grey theory, three type of artificial neural network (back-propagation neural network, radial basis function neural network, and generalized regression neural network) and their combination were used to predict the pH values in the evaluation of water quality. Based on the measured data from the Xielugang in Jiaxin with the post-hoc analysis for the c and p values of the prediction, the results showed that the prediction by using the generalized regression neural network has the averaged relative error 0.61%, and c <0.65, p>0.7.
引用
下载
收藏
页数：4
相关论文
共 50 条
  • [41] Water Quality Prediction of Small Watershed Based on Wavelet Neural Network
    Ma, Chuang
    Li, Linfeng
    Zhou, Daiqi
    2019 INTERNATIONAL CONFERENCE ON CYBER-ENABLED DISTRIBUTED COMPUTING AND KNOWLEDGE DISCOVERY (CYBERC), 2019, : 456 - 463
  • [42] Application of artificial neural networks for water quality prediction
    A. Najah
    A. El-Shafie
    O. A. Karim
    Amr H. El-Shafie
    Neural Computing and Applications, 2013, 22 : 187 - 201
  • [43] Application of artificial neural networks for water quality prediction
    Najah, A.
    El-Shafie, A.
    Karim, O. A.
    El-Shafie, Amr H.
    NEURAL COMPUTING & APPLICATIONS, 2013, 22 : S187 - S201
  • [44] Water Quality Prediction Model of a Water Diversion Project Based on the Improved Artificial Bee Colony-Backpropagation Neural Network
    Chen, Siyu
    Fang, Guohua
    Huang, Xianfeng
    Zhang, Yuhong
    WATER, 2018, 10 (06)
  • [45] Assessment and prediction of water resources based on Grey Theory
    Yang, Hongwang
    PROCEEDINGS OF THE 2016 2ND WORKSHOP ON ADVANCED RESEARCH AND TECHNOLOGY IN INDUSTRY APPLICATIONS, 2016, 81 : 780 - 785
  • [46] Research on prediction of internet public opinion based on grey system theory and fuzzy neural network
    He, H.
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2018, 35 (01) : 325 - 332
  • [47] Prediction of Mumps Incidence Trend in China Based on Difference Grey Model and Artificial Neural Network Learning
    Jia, Jin
    Liu, Mingming
    Xue, Zhigang
    Wang, Zhe
    Pan, Yu
    IRANIAN JOURNAL OF PUBLIC HEALTH, 2021, 50 (07) : 1405 - 1415
  • [48] Failure prediction based on combined model of grey neural network
    Huang K.
    Su C.
    Su, Chun (suchun@seu.edu.cn), 1600, Chinese Institute of Electronics (42): : 238 - 244
  • [49] The Coal Demand Prediction Based on the Grey Neural Network Model
    Wu, Wanjing
    Wang, Xifu
    LISS 2014, 2015, : 1337 - 1343
  • [50] (Artificial neural network to estimate an index of water quality)
    Quinones Huatangari, Lenin
    Ochoa Toledo, Luis
    Kemper Valverde, Nicolas
    Gamarra Torres, Oscar
    Bazan Correa, Jose
    Delgado Soto, Jorge
    ENFOQUE UTE, 2020, 11 (02): : 109 - 120
12345