Please wait a minute...
浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2021, Vol. 55 Issue (4): 601-607    DOI: 10.3785/j.issn.1008-973X.2021.04.001
    
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
Download: HTML     PDF(750KB) HTML
Export: BibTeX | EndNote (RIS)      

Abstract  

A surface water quality prediction model based on graph neural network (GNN) was proposed to solve the problem that water quality data has complex dependencies in both temporal and spatial dimensions. GNN was utilized to model the complex spatial dependencies of monitoring stations, and long short-term memory (LSTM) was used to model the complex temporal dependencies of historical water quality sequences. Then the encoded vector was input into the decoder to get the water quality prediction output. The experimental results show that the model can achieve 23.3%, 26.6% and 14.8% performance improvements compared with time series analysis methods, general regression methods and existing deep learning methods.

Key words： water quality prediction      graph neural network (GNN)      deep neural network      long short-term memory (LSTM)      deep learning     
Received: 19 January 2021      Published: 07 May 2021
CLC:  TP 319  
Fund:  “十三五”水体污染控制与治理科技重大专项资助项目（2018ZX07208-009）；中央高校基本科研业务费专项资金资助项目（2020QNA5017）
Corresponding Authors: Ling CHEN     E-mail: xujiahui19@zju.edu.cn;lingchen@cs.zju.edu.cn
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
Jia-hui XU
Jing-chang WANG
Ling CHEN
Yong WU
Cite this article:

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.

URL:

http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2021.04.001     OR     http://www.zjujournals.com/eng/Y2021/V55/I4/601


基于图神经网络的地表水水质预测模型

针对水质数据在时间和空间维度上的复杂依赖关系，提出基于图神经网络（GNN）的地表水水质预测模型. 该模型采用GNN建模地表水水质监测站点在空间上的复杂依赖关系，使用长短时记忆网络（LSTM）建模水质指标序列在时间上的复杂依赖关系，将编码结果输入到解码器中得到预测输出. 实验结果表明，与时间序列分析方法、通用回归方法和一般深度学习方法相比，该模型能够实现23.3%、26.6%和14.8%的性能提升.


关键词： 水质预测,  图神经网络（GNN）,  深度神经网络,  长短时记忆网络（LSTM）,  深度学习 
Fig.1 Surface water quality prediction model framework
水质指标 有效值
PH值 0~14
氨氮质量浓度 0~3 mg/L
总磷质量浓度 0~0.6 mg/L
高锰酸盐指数 0~22.5 mg/L
溶解氧质量浓度 0~12 mg/L
Tab.1 Station monitoring data and valid value ranges
气象指标 有效值
气温 ?20~50 °C
气压 900~1100 hPa
湿度 0~100%
降雨量 0~60 mm
Tab.2 Weather data and valid value ranges
Fig.2 Construction of station graph
Fig.3 Distribution diagram of water quality monitoring stations in Hangzhou-Jiaxing-Huzhou area
Fig.4 Mean square error with different distance threshold
方法 PH监测值 高锰酸盐指数 溶解氧质量浓度
MAE MRE MAE MRE MAE MRE
本文模型 0.099 1.4% 0.468 10.7% 0.478 7.5%
w/o wea 0.109 1.6% 0.478 10.9% 0.484 7.6%
w/o water 0.123 1.7% 0.608 13.3% 0.662 10.7%
Tab.3 Variation experiment results
方法 $L=1$ $L=3$ $L=5$ $L=7$ $L=9$
MAE MRE MAE MRE MAE MRE MAE MRE MAE MRE
本文模型 0.081 1.2% 0.090 1.3% 0.096 1.4% 0.097 1.4% 0.110 1.6%
SVR 0.101 1.5% 0.125 1.8% 0.135 1.9% 0.144 2.0% 0.167 2.2%
ARIMA 0.079 1.1% 0.143 2.0% 0.138 1.9% 0.135 1.8% 0.167 2.2%
LSTM 0.089 1.3% 0.125 1.8% 0.110 1.6% 0.105 1.6% 0.126 1.8%
Tab.4 Prediction results of PH value
方法 $L=1$ $L=3$ $L=5$ $L=7$ $L=9$
MAE MRE MAE MRE MAE MRE MAE MRE MAE MRE
本文模型 0.410 8.2% 0.455 10.5% 0.474 10.8% 0.497 11.1% 0.520 11.4%
SVR 0.624 13.7% 0.673 14.6% 0.680 14.8% 0.671 14.6% 0.681 14.8%
ARIMA 0.571 12.9% 0.712 15.5% 0.695 15.1% 0.743 16.0% 0.850 17.7%
LSTM 0.521 11.4% 0.532 11.7% 0.589 13.2% 0.641 14.0% 0.674 14.7%
Tab.5 Prediction results of permanganate index
方法 $L=1$ $L=3$ $L=5$ $L=7$ $L=9$
MAE MRE MAE MRE MAE MRE MAE MRE MAE MRE
本文模型 0.354 5.7% 0.451 7.1% 0.466 7.3% 0.517 8.3% 0.582 9.2%
SVR 0.525 8.4% 0.731 11.5% 0.803 12.0% 0.897 13.9% 0.876 13.7%
ARIMA 0.501 8.0% 0.818 12.3% 0.802 12.0% 0.865 13.6% 0.891 13.8%
LSTM 0.467 7.3% 0.672 10.9% 0.675 10.8% 0.742 11.7% 0.839 12.7%
Tab.6 Prediction results of dissolved oxygen mass concentration
[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.
[1] Hong-li WANG,Bin GUO,Si-cong LIU,Jia-qi LIU,Yun-gang WU,Zhi-wen YU. End context-adaptative deep sensing model with edge-end collaboration[J]. Journal of ZheJiang University (Engineering Science), 2021, 55(4): 626-638.
[2] Teng ZHANG,Xin-long JIANG,Yi-qiang CHEN,Qian CHEN,Tao-mian MI,Piu CHAN. Wrist attitude-based Parkinson's disease ON/OFF state assessment after medication[J]. Journal of ZheJiang University (Engineering Science), 2021, 55(4): 639-647.
[3] Yi-zhe MAO,Guo-fang GONG,Xing-hai ZHOU,Fei WANG. Identification of TBM surrounding rock based on Markov process and deep neural network[J]. Journal of ZheJiang University (Engineering Science), 2021, 55(3): 448-454.
[4] Li-feng XU,Hai-fan HUANG,Wei-long DING,Yu-lei FAN. Detection of small fruit target based on improved DenseNet[J]. Journal of ZheJiang University (Engineering Science), 2021, 55(2): 377-385.
[5] Shi-da CHEN,Qiang LIU,Liang HAN. Gradient sparsification compression approach to reducing communication in distributed training[J]. Journal of ZheJiang University (Engineering Science), 2021, 55(2): 386-394.
[6] Wei-qi CHEN,Jing-chang WANG,Ling CHEN,Yong-qin YANG,Yong WU. Prediction model of multi-factor aware mobile terminal replacement based on deep neural network[J]. Journal of ZheJiang University (Engineering Science), 2021, 55(1): 109-115.
[7] Hao-can XU,Ji-tuo LI,Guo-dong LU. Reconstruction of three-dimensional human bodies from single image by LeNet-5[J]. Journal of ZheJiang University (Engineering Science), 2021, 55(1): 153-161.
[8] Yi-peng HUANG,Ji-su HU,Xu-sheng QIAN,Zhi-yong ZHOU,Wen-lu ZHAO,Qi MA,Jun-kang SHEN,Ya-kang DAI. SE-Mask-RCNN: segmentation method for prostate cancer on multi-parametric MRI[J]. Journal of ZheJiang University (Engineering Science), 2021, 55(1): 203-212.
[9] Qiao-hong CHEN,YI CHEN,Wen-shu Li,Yu-bo JIA. Clothing image classification based on multi-scale SE-Xception[J]. Journal of ZheJiang University (Engineering Science), 2020, 54(9): 1727-1735.
[10] Pu ZHENG,Hong-yang BAI,Wei LI,Hong-wei GUO. Small target detection algorithm in complex background[J]. Journal of ZheJiang University (Engineering Science), 2020, 54(9): 1777-1784.
[11] Deng-wen ZHOU,Jin-yue TIAN,Lu-yao MA,Xiu-xiu SUN. Lightweight image semantic segmentation based on multi-level feature cascaded network[J]. Journal of ZheJiang University (Engineering Science), 2020, 54(8): 1516-1524.
[12] Tao MING,Dan WANG,Ji-chang GUO,Qiang LI. Breast cancer histopathological image classification using multi-scale channel squeeze-and-excitation model[J]. Journal of ZheJiang University (Engineering Science), 2020, 54(7): 1289-1297.
[13] Xu YAN,Xiao-liang FAN,Chuan-pan ZHENG,Yu ZANG,Cheng WANG,Ming CHENG,Long-biao CHEN. Urban traffic flow prediction algorithm based on graph convolutional neural networks[J]. Journal of ZheJiang University (Engineering Science), 2020, 54(6): 1147-1155.
[14] Jin-sheng JIANG,Hao-ran REN,Han-ye LI. Seismic data processing based on convolutional autoencoder[J]. Journal of ZheJiang University (Engineering Science), 2020, 54(5): 978-984.
[15] Ping YANG,Dan WANG,Zi-jian KAGN,Tong LI,Li-hua FU,Yue-ren YU. Prediction model of paroxysmal atrial fibrillation based on pattern recognition and ensemble CNN-LSTM[J]. Journal of ZheJiang University (Engineering Science), 2020, 54(5): 1039-1048.