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Prediction of Electrical Conductivity in Sub-lake of Poyang Lake Based on Random Forest Regression Model and High-frequency Data
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Li-zhen LIU1, Qi HUANG2, Dian-wei CHI3, Chao-yang FANG2, Ming-hang CHU1
Water Resources and Power | 2023, 41(10) : 50 - 53
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Water Resources and Power | 2023, 41(10): 50-53
• HYDROLOGY, WATER RESOURCES AND ENVIRONMENT •
Prediction of Electrical Conductivity in Sub-lake of Poyang Lake Based on Random Forest Regression Model and High-frequency Data
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Li-zhen LIU1, Qi HUANG2, Dian-wei CHI3, Chao-yang FANG2, Ming-hang CHU1
Affiliations
  • 1.Institute of Microbiology of Jiangxi Academy of Sciences, Jiangxi Academy of Sciences, Nanchang 330096, China
  • 2.Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Jiangxi Normal University, Nanchang 330022, China
  • 3.College of Artificial Intelligence, Yantai Institute of Technology, Yantai 264005, China
Published: 2023-10-25 doi: 10.20040/j.cnki.1000-7709.2023.20222548
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Conductivity is an important parameter to measure water quality. High-frequency monitoring of water conductivity plays an important role in water quality management. Due to the complexity of field conditions, equipment failure often leads to data loss. In order to improve the high-frequency monitoring data, machine learning model was used to predict the conductivity content in water body based on the meteorological and physical indexes obtained from high-frequency monitoring. The results show that the random forest regression model has the best prediction effect, with its determination coefficient R2 reaching 0.996, root mean square error (RRMSE) 1.31 μS/cm, and mean relative error (M MRE) 0.38%. The pH value contributed the most and was the dominant factor affecting the conductivity. The results are conducive to optimizing the field high-frequency monitoring system platform, improving the high-frequency monitoring data, which provides scientific basis for water quality management.

conductivity  /  random forest regression model  /  high-frequency monitoring data  /  Poyang Lake
Li-zhen LIU, Qi HUANG, Dian-wei CHI, Chao-yang FANG, Ming-hang CHU. Prediction of Electrical Conductivity in Sub-lake of Poyang Lake Based on Random Forest Regression Model and High-frequency Data[J]. Water Resources and Power, 2023 , 41 (10) : 50 -53 . DOI: 10.20040/j.cnki.1000-7709.2023.20222548
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doi: 10.20040/j.cnki.1000-7709.2023.20222548
  • Receive Date：2022-12-07
  • Online Date：2026-01-28
  • Published：2023-10-25
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History
  • Received：2022-12-07
  • Revised：2023-01-06
Funding
Affiliations
    1.Institute of Microbiology of Jiangxi Academy of Sciences, Jiangxi Academy of Sciences, Nanchang 330096, China
    2.Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Jiangxi Normal University, Nanchang 330022, China
    3.College of Artificial Intelligence, Yantai Institute of Technology, Yantai 264005, China
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多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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