Research on a multiparameter water quality prediction method based on a hybrid model |
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| Affiliation: | 1. College of Electronic Information Engineering, Inner Mongolia University, Hohhot 010021, China;2. Collaborative Innovation Center for Grassland Ecological Security, Ministry of Education of China, Hohhot 010021, China;3. School of Ecology and Environment, Inner Mongolia University, Hohhot 010021, China;1. College of Civil Engineering and Architecture, Guangxi University, Nanning 530004, Guangxi, China;2. Key Laboratory of Disaster Prevention and Structural Safety of Ministry of Education, Guangxi University, China;3. Guangxi Key Laboratory of Disaster Prevention and Engineering Safety, Guangxi University, China;4. Guangxi Provincial Engineering Research Center of Water Security and Intelligent Control for Karst Region, Guangxi University, China;1. EWHALE lab- Biology and Wildlife Department, Institute of Arctic Biology, University of Alaska Fairbanks, United States of America;2. Ecological Modelling Laboratory- Department of Physical and Environmental Sciences, University of Toronto Scarborough, Canada;1. College of Economics and Management, Hunan Institute of Science and Technology, Yueyang 414000, Hunan, China;2. College of Geography and Tourism, Hunan University of Arts and Science, Changde 415000, Hunan, China;1. Department of Environmental Science & Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad 826004, India;2. Department of Environmental Science & Engineering, Head of Centre (HoC), Centre for Water Resource Management (CWRM), Indian Institute of Technology (Indian School of Mines), Dhanbad 826004, India;1. PhD Student, Department of Natural Resources, Isfahan University of Technology, Isfahan 84156-83111, Iran;2. Department of Natural Resources, Isfahan University of Technology, Isfahan 84156-83111, Iran;3. Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran;4. Faculty of Environment and Natural Resources, University of Freiburg,Tennenbacherstr. 4, 79106 Freiburg, Germany |
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| Abstract: | Watershed water quality monitoring is of great significance in the protection and management of water environments. Because existing water quality prediction algorithms cannot achieve high-precision multiparameter analysis and usually require a large amount of data, this paper proposes the VARLST hybrid water quality prediction model. The proposed model combines the traditional statistical vector autoregressive moving average model and the bidirectional long short-term memory neural network to achieve multiparameter water quality data prediction on small data samples, and the model data processing is simple and highly efficient. This model is used to analyze the characteristics of water quality parameters in the Inner Mongolian section of the Yellow River Basin in northern China. The average error and fitting accuracy of the prediction results are 0.0015 and 99.869%, respectively; hence, the model achieves high-accuracy prediction of multiparameter indicators using less data, outperforming single models in terms of feasibility and accuracy. |
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