Water Environmental Quality Assessment and Effect Prediction Based on Artificial Neural Network

被引：0
|
作者：
An, Wentian [1 ]
机构：
[1] Northeastern Univ, Sch Met, Shenyang 110819, Peoples R China
来源：
3D IMAGING-MULTIDIMENSIONAL SIGNAL PROCESSING AND DEEP LEARNING, VOL 1 | 2022年 / 297卷
关键词：
Artificial neural network; Water environment; Levenberg-Marquardt; Water environmental quality assessment;
D O I：
10.1007/978-981-19-2448-4_9
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
Water environmental quality is an important criterion to promote China's sustainable development strategy and the main direction of China's scientific research and technological innovation. Based on the current situation of water environmental quality evaluation, the artificial neural network and its prediction model were constructed, and the Levenberg-Marquardt optimization algorithm was used to evaluate the water environmental quality.
引用
收藏
页码：91 / 100
页数：10
相关论文
共 50 条
  • [41] Prediction of global solar radiation by artificial neural network based on a meteorological environmental data
    Diaz-Gomez, J.
    Parrales, A.
    Alvarez, A.
    Silva-Martinez, S.
    Colorado, D.
    Hernandez, J. A.
    DESALINATION AND WATER TREATMENT, 2015, 55 (12) : 3210 - 3217
  • [42] Research on prediction of environmental aerosol and PM2.5 based on artificial neural network
    Xianghong Wang
    Baozhen Wang
    Neural Computing and Applications, 2019, 31 : 8217 - 8227
  • [43] Research on prediction of environmental aerosol and PM2.5 based on artificial neural network
    Wang, Xianghong
    Wang, Baozhen
    NEURAL COMPUTING & APPLICATIONS, 2019, 31 (12): : 8217 - 8227
  • [44] Assessment for water quality by artificial neural network in Daya Bay, South China Sea
    Wu, Mei-Lin
    Wang, You-Shao
    Gu, Ji-Dong
    ECOTOXICOLOGY, 2015, 24 (7-8) : 1632 - 1642
  • [45] Advanced Evaluation Methodology for Water Quality Assessment Using Artificial Neural Network Approach
    Sandeep Bansal
    Geetha Ganesan
    Water Resources Management, 2019, 33 : 3127 - 3141
  • [46] Assessment for water quality by artificial neural network in Daya Bay, South China Sea
    Mei-Lin Wu
    You-Shao Wang
    Ji-Dong Gu
    Ecotoxicology, 2015, 24 : 1632 - 1642
  • [47] Advanced Evaluation Methodology for Water Quality Assessment Using Artificial Neural Network Approach
    Bansal, Sandeep
    Ganesan, Geetha
    WATER RESOURCES MANAGEMENT, 2019, 33 (09) : 3127 - 3141
  • [48] Quality Assessment of Lettuce using Artificial Neural Network
    Valenzuela, Ira C.
    Puno, John Carlo V.
    Bandala, Argel A.
    Baldovino, Renann G.
    de Luna, Robert G.
    De Ocampo, Anton Louise
    Cuello, Joel
    Dadios, Elmer P.
    2017 IEEE 9TH INTERNATIONAL CONFERENCE ON HUMANOID, NANOTECHNOLOGY, INFORMATION TECHNOLOGY, COMMUNICATION AND CONTROL, ENVIRONMENT AND MANAGEMENT (IEEE HNICEM), 2017,
  • [49] (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
  • [50] Artificial neural network-based assessment of water quality index (WQI) of surface water in Gwalior-Chambal region
    Chauhan, Shyamveer Singh
    Trivedi, Manoj Kumar
    INTERNATIONAL JOURNAL OF ENERGY AND ENVIRONMENTAL ENGINEERING, 2023, 14 (01) : 47 - 61
12345