计算机技术、电信技术 |
|
|
|
|
基于图神经网络的地表水水质预测模型 |
许佳辉1( ),王敬昌2,陈岭1,*( ),吴勇2 |
1. 浙江大学 计算机科学与技术学院,浙江 杭州 310027 2. 浙江鸿程计算机系统有限公司,浙江 杭州 310009 |
|
Surface water quality prediction model based on graph neural network |
Jia-hui XU1( ),Jing-chang WANG2,Ling CHEN1,*( ),Yong WU2 |
1. College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China 2. Zhejiang Hongcheng Computer Systems Limited Company, Hangzhou 310009, China |
引用本文:
许佳辉,王敬昌,陈岭,吴勇. 基于图神经网络的地表水水质预测模型[J]. 浙江大学学报(工学版), 2021, 55(4): 601-607.
Jia-hui XU,Jing-chang WANG,Ling CHEN,Yong WU. Surface water quality prediction model based on graph neural network. Journal of ZheJiang University (Engineering Science), 2021, 55(4): 601-607.
链接本文:
http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2021.04.001
或
http://www.zjujournals.com/eng/CN/Y2021/V55/I4/601
|
1 |
LIANG J, YANG Q, SUN T, et al MIKE 11 model-based water quality model as a tool for the evaluation of water quality management plans[J]. Journal of Water Supply: Research and Technology-AQUA, 2015, 64 (6): 708- 718
doi: 10.2166/aqua.2015.048
|
2 |
GONG R, XU L, WANG D, et al Water quality modeling for a typical urban lake based on the EFDC model[J]. Environmental Modeling and Assessment, 2016, 21 (5): 643- 655
doi: 10.1007/s10666-016-9519-1
|
3 |
FARUK D O A hybrid neural network and ARIMA model for water quality time series prediction[J]. Engineering Applications of Artificial Intelligence, 2010, 23 (4): 586- 594
doi: 10.1016/j.engappai.2009.09.015
|
4 |
PARMAR K S, BHARDWAJ R Water quality management using statistical analysis and time-series prediction model[J]. Applied Water Science, 2014, 4 (4): 425- 434
doi: 10.1007/s13201-014-0159-9
|
5 |
LIU S, TAI H, DING Q, et al A hybrid approach of support vector regression with genetic algorithm optimization for aquaculture water quality prediction[J]. Mathematical and Computer Modelling, 2013, 58 (3): 458- 465
|
6 |
SINGH K P, BASANT N, GUPTA S Support vector machines in water quality management[J]. Analytica Chimica Acta, 2012, 703 (2): 152- 162
|
7 |
CHENG S, ZHANG S, LI L, et al Water quality monitoring method based on TLD 3D Fish tracking and XGBoost[J]. Mathematical Problems in Engineering, 2018, 2018 (7): 1- 12
|
8 |
BUI D T, KHOSRAVI K, TIEFENBACHER J, et al Improving prediction of water quality indices using novel hybrid machine-learning algorithms[J]. Science of the Total Environment, 2020, 721 (15): 137612
|
9 |
LI L, JIANG P, XU H, et al Water quality prediction based on recurrent neural network and improved evidence theory: a case study of Qiantang River, China[J]. Environmental Science and Pollution Research, 2019, 26 (4): 19879- 19896
|
10 |
WANG Y, ZHOU J, CHEN K, et al. Water quality prediction method based on LSTM neural network [C]// International Conference on Intelligent Systems and Knowledge Engineering. Nanjing: IEEE, 2017.
|
11 |
LIU P, WANG J, CHEN K, et al Analysis and prediction of water quality using LSTM deep neural networks in IoT environment[J]. Sustainability, 2019, 11 (4): 2058
|
12 |
KIPF T, WELLING M. Semi-supervised classification with graph convolutional networks [C]// International Conference on Learning Representations. Toulon: [s. n.], 2017.
|
13 |
HOCHREITER S, SCHMIDHUBER J Long short-term memory[J]. Neural Computation, 1997, 9 (8): 1735- 1780
doi: 10.1162/neco.1997.9.8.1735
|
14 |
中华人民共和国生态环境部. 地表水环境质量标准: GB 3838—2002 [S]. 北京: 中国环境出版集团, 2002.
|
15 |
PASZKE A, GROSS S, MASSA F, et al. Pytorch: an imperative style, high-performance deep learning library [C]// Advances in Neural Information Processing Systems. Vancouver: Curran Associates, 2019.
|
16 |
FEY M, LENSSEN J E. Fast graph representation learning with PyTorch geometric [C]// International Conference on Learning Representations RLGM Workshop. New Orleans: [s. n.], 2019.
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
|
Shared |
|
|
|
|
|
Discussed |
|
|
|
|