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CNN-EA-ConvLSTM Water Quality Prediction Model Based on Evolutionary Algorithm Optimization
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Hong-chen WANGa, Jun MAb, Bo-hang CHENc
Water Resources and Power | 2023, 41(8) : 73 - 76
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Water Resources and Power | 2023, 41(8): 73-76
• HYDROLOGY, WATER RESOURCES AND ENVIRONMENT •
CNN-EA-ConvLSTM Water Quality Prediction Model Based on Evolutionary Algorithm Optimization
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Hong-chen WANGa, Jun MAb, Bo-hang CHENc
Affiliations
  • a.College of Physics and Electronic Information Engineering, Qinghai Normal University, Xining 810016, China
  • b.Academy of Plateau Science and Sustainability (Key Laboratory of Internet of Things), Qinghai Normal University, Xining 810016, China
  • c.College of Computer, Qinghai Normal University, Xining 810016, China
Published: 2023-08-25 doi: 10.20040/j.cnki.1000-7709.2023.20222020
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Aiming at the problem that the traditional water quality prediction method is difficult to capture the spatial and temporal characteristics of the sample, this paper proposes to establish a CNN-EA-ConvLSTM based water quality prediction model. The convolutional neural network (CNN) was used to reduce the dimensionality of the data and extract the sample features. Then the hidden information among samples was explored by the external attention mechanism. The convolutional long and short-term memory network (ConvLSTM) was further used to capture the spatial characteristics of the data. To achieve optimal results of the model, a genetic algorithm was used to optimize the parameters of the model. The water quality test data of Qinghai Province was used as a sample to simulate and validate the model. The results show that the mean absolute error (MMAE) of the model is 0.063, the root mean square error (RRMSE) is 0.012, and the mean absolute percentage error is 2.6%, which are respectively reduced by 18% and 24%, 14% and 25%, 16% and 21% compared with the CNN-EA model and CNN-LSTM model. Therefore, the model can effectively obtain the spatial and temporal characteristics of water quality, attenuate the influence of different samples, and achieve the ideal prediction effect.

water quality prediction  /  CNN  /  external attention  /  ConvLSTM  /  GA
Hong-chen WANG, Jun MA, Bo-hang CHEN. CNN-EA-ConvLSTM Water Quality Prediction Model Based on Evolutionary Algorithm Optimization[J]. Water Resources and Power, 2023 , 41 (8) : 73 -76 . DOI: 10.20040/j.cnki.1000-7709.2023.20222020
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doi: 10.20040/j.cnki.1000-7709.2023.20222020
  • Receive Date：2022-09-28
  • Online Date：2026-01-28
  • Published：2023-08-25
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  • Received：2022-09-28
  • Revised：2022-10-24
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Affiliations
    a.College of Physics and Electronic Information Engineering, Qinghai Normal University, Xining 810016, China
    b.Academy of Plateau Science and Sustainability (Key Laboratory of Internet of Things), Qinghai Normal University, Xining 810016, China
    c.College of Computer, Qinghai Normal University, Xining 810016, China
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https://castjournals.cast.org.cn/joweb/sdnykx/EN/10.20040/j.cnki.1000-7709.2023.20222020
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红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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