A novel hybrid model for long-term water quality prediction with the ‘decomposition–inputs–prediction’ hierarchical optimization framework

Accurate, stable, and long-term water quality predictions are essential for water pollution warning and efficient water environment management. In this study, a hierarchical water quality prediction (HWQP) model was developed based on ‘data decomposition–predictor screening–efficient prediction’ via...

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Bibliographic Details
Published inJournal of hydroinformatics Vol. 26; no. 11; pp. 3008 - 3026
Main Authors Han, Jianjun, Wang, Lingling, Yao, Qiwen, Zhu, Dawei, Zhu, Hai, Xu, Jin, Hu, Lihan, Yang, Xu
Format Journal Article
LanguageEnglish
Published London IWA Publishing 01.11.2024
Subjects
Accuracy
Ammonia
Artificial intelligence
Chemical oxygen demand
Correlation analysis
Datasets
Decomposition
Deep learning
Environmental management
Long short-term memory
Mathematical models
Neural networks
Optimization
Oxygen requirement
Pollutants
Prediction models
Regression analysis
Simulation
Statistical analysis
Stream flow
Time series
Trends
Water pollution
Water quality
Wavelet analysis
Wavelet transforms
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Summary:Accurate, stable, and long-term water quality predictions are essential for water pollution warning and efficient water environment management. In this study, a hierarchical water quality prediction (HWQP) model was developed based on ‘data decomposition–predictor screening–efficient prediction’ via wavelet decomposition, Spearman correlation analysis, and long short-term memory network, respectively. The observed data from 14 stations in the Huaihe River–Hongze Lake system, including ammonia nitrogen (AN) and chemical oxygen demand (COD), were used to make long-term water quality predictions. The results suggested that, compared to existing water quality prediction models, the HWQP model has higher accuracy, with the root mean square errors of 6 and 17% for simulating AN and COD, respectively. The AN and COD concentrations will range from 0 to 1 mg/l and from 3 to 5 mg/l at 12 stations, respectively, and the COD concentrations will exceed the water quality target at Stations 4 and 5. The established model has great potential to address the challenges associated with the water environment.
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SourceType-Scholarly Journals-1
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content type line 14
ISSN:1464-7141
1465-1734
DOI:10.2166/hydro.2024.244