Skip to main content
SpringerLink
Log in

Water Quality Prediction in Urban Waterways Based on Wavelet Packet Denoising and LSTM

  • Published:
Water Resources Management Aims and scope Submit manuscript

Abstract

The prediction of water quality in urban rivers plays a crucial role in supporting water environment management. This study collected real-time water quality monitoring data from four stations in the Fenjiang River Basin of Foshan City, spanning from 2016 to 2021. Then the Wavelet Packet Denoising (WPD) technique was applied to reduce noise interference in historical monitoring data. Subsequently, a single-factor water quality prediction model was developed, which is based on Long Short-Term Memory (LSTM), focusing on chemical oxygen demand (COD) and ammonia nitrogen (NH3-N). The results of this study demonstrate that the integration of WPD with LSTM, referred to as WPD-LSTM, outperformed conventional LSTM models in terms of predictive accuracy. Notably, the WPD-LSTM model exhibited superior performance in predicting the impact of COD and NH3-N on water quality in the Fenjiang River, surpassing the traditional LSTM model over a prediction period of 12 h and 3 days. In the 12-h prediction, the RMSE values of NH3-N predictions in the four monitoring sections decreased by 55% to 67%, and the RMSE values of COD predictions decreased by 18% to 51%.. In the 3-day prediction, the RMSE values of NH3-N predictions in the four monitoring sections decreased by 40% to 83%, and the RMSE values of COD predictions decreased by 50% to 69%. By employing the WPD-LSTM method, this study contributes to improving the precision of water quality prediction, thereby providing valuable insights for effective water environment management in urban river systems.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig.9
Fig.10
Fig.11
Fig.12
Fig.13
Fig.14
Fig.15
Fig.16

Similar content being viewed by others

References

  • Aloui S et al (2023) A review of Soil and Water Assessment Tool (SWAT) studies of Mediterranean catchments: Applications, feasibility, and future directions. J Environ Manage 326:116799

    Article  Google Scholar 

  • Baek SS, Pyo J, Chun JA (2020) Prediction of water level and water quality using a CNN-LSTM combined deep learning approach. Water 12(12):3399

    Article  CAS  Google Scholar 

  • Beck MB (1987) Water quality modeling: a review of the analysis of uncertainty. Water Resour Res 23(8):1393–1442

    Article  CAS  Google Scholar 

  • Brown LC, Barnwell TO (1987) The enhanced stream water quality models QUAL2E and QUAL2E-UNCAS: documentation and user manual. United States Environmental Protection Agency. https://www.researchgate.net/publication/235754236_The_enhanced_stream_water_quality_models_QUAL2E_and_QUAL2E-UNCAS_documentation_and_user_manual

  • Chen H, Zhang H (2014) Uncertainty in water quality predictions: The roles of data quality and model structure. J Hydrol 511:637–647

    Google Scholar 

  • Chen P et al (2021) Assessing the impacts of recent crop expansion on water quality in the Missouri River Basin using the Soil and Water Assessment Tool. Journal of Advances in Modeling Earth Systems 13(6):e2020MS002284

    Article  Google Scholar 

  • Chen Y-C, Tseng C-H, Chen Y-T (2021) Modeling transmission of hexavalent chromium concentration and its health cost with a water quality analysis simulation program. Water Environ Res 93(9):1779–1788

    Article  CAS  Google Scholar 

  • Chen Z et al (2023) Review of water quality prediction methods. Proceedings of the 8th International Conference on Water Resource and Environment 341:237–265. https://doi.org/10.1007/978-981-99-1919-2_17

  • Donoho DL, Johnstone IM (1994) Ideal spatial adaptation by wavelet shrinkage. Biometrika 81(3):425–455

    Article  Google Scholar 

  • Guo ZQ, Qiu DL (2017) The trend and cause analysis of the pollution of Foshan waterway. Journal of Foshan University (natural Science Edition) 35(04):43–46

    Google Scholar 

  • Halder JN, Islam MN (2015) Water pollution and its impact on human health. Journal of Environment and Human 2(1):36–46

    Article  Google Scholar 

  • Hang X, Gao H, Jia S (2019) Assessment of water quality in Taihu Lake using a radial basis function network, structure index, and principal component analysis. Appl Ecol Environ Res 17(6):14241–14258. https://doi.org/10.15666/aeer/1706_1424114258

  • Hochreiter S (1991) Untersuchungen zu dynamischen neuronalen Netzen. Diploma Technische Universität München 91(1):31

    Google Scholar 

  • Jiang J, Ri T et al (2019) Water quality management of heavily contaminated urban rivers using water quality analysis simulation program. Global Journal of Environmental Science and Management 5(3):295–308. https://doi.org/10.22034/GJESM.2019.03.03

    Article  Google Scholar 

  • Khorram S, Jehbez N (2023) A Hybrid CNN-LSTM Approach for Monthly Reservoir Inflow Forecasting. Water Resour Manage 37(10):4097–4121. https://doi.org/10.1007/s11269-023-03541-w

    Article  Google Scholar 

  • Khullar S, Singh N (2022) Water quality assessment of a river using deep learning Bi-LSTM methodology: forecasting and validation. Environ Sci Pollut Res 29(9):12875–12889

    Article  CAS  Google Scholar 

  • Kumar DN, Raju KS, Sathish T (2004) River flow forecasting using recurrent neural networks. Water Resour Manage 18:143–161

    Article  Google Scholar 

  • Leong WC et al (2021) Prediction of water quality index (WQI) using support vector machine (SVM) and least square-support vector machine (LS-SVM). International Journal of River Basin Management 19(2):149–156

    Article  Google Scholar 

  • Lin J, Liu Q, Song Y, Liu J, Yin Y, Hall NS (2023) Temporal Prediction of Coastal Water Quality Based on Environmental Factors with Machine Learning. J Mar Sci Eng 11:1608. https://doi.org/10.3390/jmse11081608

    Article  Google Scholar 

  • Liu P et al (2019) Analysis and prediction of water quality using LSTM deep neural networks in IoT environment. Sustainability 11(7):2058

    Article  CAS  Google Scholar 

  • Mbuh MJ, Mbih R, Wendi C (2019) Water quality modeling and sensitivity analysis using Water Quality Analysis Simulation Program (WASP) in the Shenandoah River watershed. Phys Geogr 40(2):127–148

    Article  Google Scholar 

  • Melching CS, Yoon CG (1996) Key sources of uncertainty in QUAL2E model of Passaic River. J Water Resour Plan Manag 122(2):105–113

    Article  Google Scholar 

  • Mendivil-García K et al (2022) Climate change impact assessment on a tropical river resilience using the Streeter-Phelps dissolved oxygen model. Front Environ Sci 10:903046

    Article  Google Scholar 

  • Meng X, Zhang Y, Qiao J (2021) An adaptive task-oriented RBF network for key water quality parameters prediction in wastewater treatment process. Neural Comput & Applic 33:11401–11414. https://doi.org/10.1007/s00521-020-05659-z

  • Moriasi DN et al (2015) Hydrologic and water quality models: Performance measures and evaluation criteria. Transactions of the ASABE 58(6):1763–1785. https://doi.org/10.13031/trans.58.10715

    Article  Google Scholar 

  • Mustiere F, Bolic M, Bouchard M (2009) Speech enhancement based on nonlinear models using particle filters. IEEE Trans Neural Networks 20(12):1923–1937

    Article  Google Scholar 

  • Nas SS, Nas E (2009) Water Quality Modeling and Dissolved Oxygen Balance in Streams: A Point Source Streeter-Phelps Application in the Case of the Harsit Stream. Clean: Soil, Air, Water 37:67–74. https://doi.org/10.1002/clen.200800107

    Article  CAS  Google Scholar 

  • Pang J et al (2022) Contamination Assessment and Source Analysis of Urban Waterways Based on Bayesian and Principal Component Analysis—A Case Study of Fenjiang River. Water 14(18):2912. https://doi.org/10.3390/w14182912

    Article  CAS  Google Scholar 

  • Pradhan P, Tingsanchali T, Shrestha S (2020) Evaluation of Soil and Water Assessment Tool and Artificial Neural Network models for hydrologic simulation in different climatic regions of Asia. Sci Total Environ 701:134308

    Article  CAS  Google Scholar 

  • Rinaldi S, Soncini-Sessa R (1978) Sensitivity analysis of generalized Streeter-Phelps models. Adv Water Resour 1(3):141–146

    Article  Google Scholar 

  • Rinaldi S, Soncini-Sessa R, Romano P (1979) Parameter estimation of Streeter-Phelps models. J Environ Eng Div 105(1):75–88

    Article  CAS  Google Scholar 

  • Roy DK (2021) Long Short-Term Memory Networks to Predict One-Step Ahead Reference Evapotranspiration in a Subtropical Climatic Zone. Environmental Processes 8(2):1–31. https://doi.org/10.1007/s40710-021-00512-4

    Article  CAS  Google Scholar 

  • Shi Q, Dong Z, Luo Y et al (2021) Evaluation and prediction of water quality of hongze lake based on machine learning method. China Rural Water and Hydropower 12:53–59

    Google Scholar 

  • Sifuzzaman M, Islam MR, Ali MZ (2009) Application of Wavelet Transform and its Advantages Compared to Fourier Transform. Journal of Physical Science 13:121–134

    Google Scholar 

  • Song C et al (2021) A water quality prediction model based on variational mode decomposition and the least squares support vector machine optimized by the sparrow search algorithm (VMD-SSA-LSSVM) of the Yangtze River. China Environmental Monitoring and Assessment 193(6):363

    Article  CAS  Google Scholar 

  • Wang Q, Yang Z (2016) Industrial water pollution, water environment treatment, and health risks in China. Environ Pollut 218:358–365. https://doi.org/10.1016/j.envpol.2016.07.011

    Article  CAS  Google Scholar 

  • Wang H, Chao M et al (2021) Current status and prospects of the treatment of urban water-related problems in China. China Water Resources 14:4–7

    Google Scholar 

  • Wang X et al (2021) Predicting water quality during urbanization based on a causality-based input variable selection method modified back-propagation neural network. Environ Sci Pollut Res 28:960–973

    Article  CAS  Google Scholar 

  • Wang Z, Si Y, Chu H (2022) Daily Streamflow Prediction and Uncertainty Using a Long Short-Term Memory (LSTM) Network Coupled with Bootstrap. Water Resour Manage 36(12):4575–4590. https://doi.org/10.1007/s11269-022-03264-4

    Article  Google Scholar 

  • Yahya A, Saeed A et al (2019) Water quality prediction model based support vector machine model for ungauged river catchment under dual scenarios. Water 11(6):1231

    Article  CAS  Google Scholar 

  • Yan J et al (2019) Application of a hybrid optimized BP network model to estimate water quality parameters of Beihai Lake in Beijing. Applied Sciences 9(9):1863

    Article  Google Scholar 

  • Yan J et al (2021) Water quality prediction in the Luan River based on 1-DRCNN and BiGRU hybrid neural network model. Water 13(9):1273

    Article  CAS  Google Scholar 

  • Ye Q et al (2019) River water quality parameters prediction method based on LSTM-RNN model. 2019 Chinese Control And Decision Conference (CCDC). IEEE:3024–3028. https://doi.org/10.1109/CCDC.2019.8832885

  • Zhang Z, Zhang T, Wang J (2016) Impacts of data quality on the calibration and validation of water quality models: A case study in the Luhun Reservoir China. Environ Monit Assess 188(3):165

    Google Scholar 

  • Zhang Y et al (2022) Accurate prediction of water quality in urban drainage network with integrated EMD-LSTM model. J Clean Prod 354:131724

    Article  CAS  Google Scholar 

  • Zhu D, Du J, Ou Y et al (2020) Investigation and Analysis of Water Quality in Zhongshan Park Section of Fenjiang River in Foshan City. Guangdong Chemical Industry 47(19):115–116+101

    Google Scholar 

Download references

Acknowledgements

This research was funded by the Guangdong Basic and Applied Basic Research Foundation (2023B1515040028), and the Guangzhou Bureau of Hydrology project “Research on the mechanism of hydro-ecological dynamics in a typical river network area”.

Author information

Authors and Affiliations

  1. Center for Water Resources and Environment, and Guangdong Key Laboratory of Marine Civil Engineering, School of Civil Engineering, Sun Yat-Sen University, Guangzhou, 510275, China

    Jiafeng Pang, Wei Luo, Zeyu Yao, Chunyu Dong & Kairong Lin

  2. Guangzhou Feng Ze-Yuan Water Conservancy Technology Co., Ltd, Guangzhou, 510663, China

    Jing Chen

  3. Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai, 519000, China

    Chunyu Dong & Kairong Lin

Authors
  1. Jiafeng Pang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wei Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zeyu Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jing Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chunyu Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Kairong Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization, J.P. and K.L.; methodology, J.P.; software, J.P. and W.L.; validation, J.P.; formal analysis, J.P. and J.C.; investigation, J.P.; resources, J.P.; writing—original draft preparation, J.P. and J.C.; writing—review and editing, Z.Y., C.D. and K.L.; funding acquisition, C.D. and K.L. All authors have read and agreed to the published version of the manuscript.

Corresponding authors

Correspondence to Wei Luo or Kairong Lin.

Ethics declarations

Ethical Approval

All authors have seen and agreed with the contents of the manuscript.

Consent to Participate

All authors gave explicit consent to participate in this work.

Consent to Publish

All authors gave explicit consent to publish this manuscript.

Conflicts of Interest

The authors declare that they have no known competing financial interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pang, J., Luo, W., Yao, Z. et al. Water Quality Prediction in Urban Waterways Based on Wavelet Packet Denoising and LSTM. Water Resour Manage 38, 2399–2420 (2024). https://doi.org/10.1007/s11269-024-03774-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11269-024-03774-3

Keywords

Search

Navigation