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| DOI：10.13522/j.cnki.ggps.2022152 |
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| Research and Application on Improved GRNN-Markov Water Quality Prediction Model |
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XU Rui, ZHANG Xiaobin, XUE Pengsong
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1. Yuncheng Water Conservancy Survey and Design Research Institute Co., Ltd., Yuncheng 044000, China;
2. Yuncheng University, Department of Applied Chemistry, Yuncheng 044000, China;
3. Shaanxi Jinke Environmental Protection Engineering Co., Ltd., Xi’an 710119, China
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| Abstract: |
| 【Objective】In view of the complexity of the water environment system, combined with the GRNN network prediction model and Markov theory, an improved GRNN-Markov water quality prediction model was constructed. This model, which provides a reliable method for simulating uncertain, complex and variable river water quality trends.【Method】 According to actual situation of water quality in Fen River’s estuary to Yellow River, taking the main pollutant CODcr as the research index, the water quality monitoring data were preprocessed using the Laida criteria. Using Grey Relational Analysis to determine input nodes of GRNN Network which solved the problem that the GRNN network unable to auto select and optimize input nodes. At the same time, the improved GRNN model was used to simulate and predict the water quality data. In view of the random fluctuation of the water quality prediction data, the Markov model was used to correct the error residual value to achieve better prediction results and provide new ideas and methods for water environmental protection and governance. 【Result】The combined model provides new ideas and methods for water environmental protection and governance. The research show that the improved GRNN-Markov water quality prediction model can improve the accuracy of water quality prediction results. Relative error was -38.27%~-15.71% based on GRA-GRNN model, Relative error was -25.77%~-5.25% based on improved GRNN-Markov water quality prediction model, the correction result was close to the measured value. 【Conclusion】 This combination model can be used in water quality prediction based on small sample data. This study provides a scientific basis for water environment management. |
| Key words: grey relational analysis (GRA); improved GRNN-Markov model; Fen River’s estuary to Yellow River; water quality prediction |
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