ABSTRACT
The water quality monitoring data reflects the ecological environment of the river and its changing trend. The accurate analysis and prediction of water quality is of great significance to improve the efficiency of river management and maintenance, ensure the safety and stability of the ecological environment of the river basin. The traditional water quality prediction model combines hydrodynamic theory and water quality change process mechanism, which can represent the overall change trend of water quality. However, this method is mostly aimed at a fixed watershed, with poor portability ability and difficult to accurately predict key areas. Although some new mathematical statistical methods and machine learning methods can achieve high accuracy in the prediction of water quality data from a single station, the spatial correlation characteristics between multiple stations are ignored in the comprehensive water quality prediction of the basin, resulting in a decline in accuracy. In this paper, based on the spatial distribution and graph structure characteristics of multiple water quality monitoring stations in the basin, the temporal graph convolutional neural network is used to analyse the time series characteristics of water quality, and realize the prediction of water quality data from multiple monitoring stations. The analysis of water quality monitoring data of a river located in the Guangdong Province of China proves that, compared with the commonly used model LSTM, the T-GCN model can better fit the spatial correlation characteristics and time series characteristics of water quality monitoring data, and achieve a more accurate water quality predictive analysis. Based on the water quality monitoring data of multiple stations, T-GCN model can reflect the changing trend of water quality in the entire basin, and accurately predict the water quality changing in specific locations. The research results can provide a more accurate and effective reference for river operation and management.
- Zhao Y, Pan Y, Wang W 2021 A brain-inspired dynamic environmental emergency response framework for sudden water pollution accidents Water 13(21): 3097.Google Scholar
- Yao H, Zhang T, Liu B 2016 Analysis of surface water pollution accidents in China: characteristics and lessons for risk management Environmental management 57(4): 868-878.Google Scholar
- Shi B, Wang P, Jiang J, 2018 Applying high-frequency surrogate measurements and a wavelet-ANN model to provide early warnings of rapid surface water quality anomalies Science of the Total Environment 610: 1390 -1399.Google Scholar
- Graham D N, Butts M B 2005 Flexible, integrated watershed modelling with MIKE SHE Watershed models 849336090: 245-272.Google Scholar
- Li X, Huang M, Wang R 2020 Numerical simulation of Donghu Lake hydrodynamics and water quality based on remote sensing and MIKE 21 ISPRS International Journal of Geo-Information 9(2): 94-114.Google Scholar
- Paliwal R, Sharma P, Kansal A 2007 Water quality modelling of the river yamuna (India) using QUAL2E-UNCAS Journal of environmental management 83(2): 131-144.Google Scholar
- Zehra R, Singh S P, Verma J, 2023 Spatio-temporal investigation of physico-chemical water quality parameters based on comparative assessment of QUAL 2Kw and WASP model for the upper reaches of Yamuna River stretching from Paonta Sahib, Sirmaur district to Cullackpur, North Delhi districts of North India Environmental Monitoring and Assessment 195(4): 480-494.Google Scholar
- Darji J, Lodha P, Tyagi S 2022 Assimilative capacity and water quality modeling of rivers: a review AQUA—Water Infrastructure, Ecosystems and Society 71(10): 1127-1147.Google Scholar
- Wen Y, Schoups G, Van De Giesen N 2017 Organic pollution of rivers: Combined threats of urbanization, livestock farming and global climate change Scientific reports 7(1): 43289.Google Scholar
- Shabani A, Woznicki S A, Mehaffey M 2021 A coupled hydrodynamic (HEC‐RAS 2D) and water quality model (WASP) for simulating flood‐induced soil, sediment, and contaminant transport Journal of flood risk management 14(4): e12747.Google Scholar
- Ramos-Ramírez L Á, Guevara-Luna M A, Chiriví-Salomón J S, 2020 Simulation of Cr-III dispersion in the High Bogotá River Basin using the WASP model[J]. Revista Facultad de Ingeniería Universidad de Antioquia, 2020 (97): 30-40.Google Scholar
- Hankin B, Bielby S, Pope L 2016 Catchment‐scale sensitivity and uncertainty in water quality modelling Hydrological Processes 30(22): 4004-4018.Google Scholar
- Al-Murib M D, Wells S A 2019 Hydrodynamic and total dissolved solids model of the Tigris River using CE-QUAL-W2 Environmental Processes 6: 619-641.Google Scholar
- Wang M, Kong B, Li X 2016 Grey prediction theory and extension strategy-based excitation control for generator International Journal of Electrical Power & Energy Systems 79: 188-195.Google Scholar
- Zhai W, Zhou X, Man J 2019 Prediction of water quality based on artificial neural network with grey theory IOP Conference Series: Earth and Environmental Science 295(4): 042009.Google Scholar
- Xie Z, Su K 2010 Improved grey model base on exponential smoothing for river water pollution prediction 2010 4th International Conference on Bioinformatics and Biomedical Engineering. IEEE 1:1-4.Google Scholar
- Ma K, Teng L, Wang X 2021 Color image encryption scheme based on the combination of the fisher-yates scrambling algorithm and chaos theory Multimedia Tools and Applications 80: 24737-24757.Google Scholar
- Ding T, Zhou H C, Huang J H 2004 Interval prediction of chaotic hydrological time series Journal of Hydraulic Engineering 12:15-20.Google Scholar
- Wang W, Chau K, Xu D 2015 Improving forecasting accuracy of annual runoff time series using ARIMA based on EEMD decomposition Water Resources Management 29: 2655-2675.Google Scholar
- Zhang J, Xiao H, Fang H 2022 Component-based reconstruction prediction of runoff at multi-time scales in the source area of the Yellow River based on the ARMA model Water Resources Management 36(1): 433-448.Google Scholar
- Hearst M A, Dumais S T, Osuna E 1998 Support vector machines IEEE Intelligent Systems and their applications 13(4): 18-28.Google Scholar
- Leong W C, Bahadori A, Zhang J 2021 Prediction of water quality index (WQI) using support vector machine (SVM) and least square-support vector machine (LS-SVM) International Journal of River Basin Management 19(2): 149-156.Google Scholar
- Koranga M, Pant P, Pant D 2021 SVM model to predict the water quality based on physicochemical parameters International Journal of Mathematical, Engineering and Management Sciences 6(2): 645.Google Scholar
- Graves A, Graves A 2021 Long short-term memory Supervised sequence labelling with recurrent neural networks 1: 37-45.Google Scholar
- Wu J, Zhang J, Tan W 2023 Application of Time Serial Model in Water Quality Predicting Comput. Mater. Contin 74: 67-82.Google Scholar
- Li Q, Yang Y, Yang L 2023 Comparative analysis of water quality prediction performance based on LSTM in the Haihe River Basin, China Environmental Science and Pollution Research 30(3): 7498-7509.Google ScholarCross Ref
- Debow A, Shweikani S, Aljoumaa K 2023 Predicting and forecasting water quality using deep learning International Journal of Sustainable Agricultural Management and Informatics 9(2): 114-135.Google Scholar
- Kipf T N, Welling M 2017 Semi-supervised classification with graph convolutional networks Computer Science 2: 1-14.Google Scholar
- Han M, Su Z, Na X 2023 Predict water quality using an improved deep learning method based on spatiotemporal feature correlated: a case study of the Tanghe Reservoir in China Stochastic Environmental Research and Risk Assessment 4: 1-13.Google Scholar
- Ni Q, Cao X, Tan C 2023 An improved graph convolutional network with feature and temporal attention for multivariate water quality prediction Environmental Science and Pollution Research 30(5): 11516-11529.Google Scholar
- Zhao L, Song Y, Zhang C 2019 T-GCN: A temporal graph convolutional network for traffic prediction IEEE transactions on intelligent transportation systems 21(9): 3848-3858.Google Scholar
- Krizhevsky A, Sutskever I, Hinton G E 2017 Imagenet classification with deep convolutional neural networks Communications of the ACM 60(6): 84-90.Google Scholar
- Zaremba W, Sutskever I, Vinyals O 2014 Recurrent neural network regularization Computer Science 8(1):1-8Google Scholar
Index Terms
- Multi-station Water Quality Prediction Considering Temporal and Spatial Correlation: Based On T-GCN
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