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Environmental Science and Pollution Research

, Volume 26, Issue 1, pp 402–420 | Cite as

Modeling daily water temperature for rivers: comparison between adaptive neuro-fuzzy inference systems and artificial neural networks models

  • Senlin ZhuEmail author
  • Salim Heddam
  • Emmanuel Karlo Nyarko
  • Marijana Hadzima-Nyarko
  • Sebastiano Piccolroaz
  • Shiqiang Wu
Research Article

Abstract

River water temperature is a key control of many physical and bio-chemical processes in river systems, which theoretically depends on multiple factors. Here, four different machine learning models, including multilayer perceptron neural network models (MLPNN), adaptive neuro-fuzzy inference systems (ANFIS) with fuzzy c-mean clustering algorithm (ANFIS_FC), ANFIS with grid partition method (ANFIS_GP), and ANFIS with subtractive clustering method (ANFIS_SC), were implemented to simulate daily river water temperature, using air temperature (Ta), river flow discharge (Q), and the components of the Gregorian calendar (CGC) as predictors. The proposed models were tested in various river systems characterized by different hydrological conditions. Results showed that including the three inputs as predictors (Ta, Q, and the CGC) yielded the best accuracy among all the developed models. In particular, model performance improved considerably compared to the case where only Ta is used as predictor, which is the typical approach of most of previous machine learning applications. Additionally, it was found that Q played a relevant role mainly in snow-fed and regulated rivers with higher-altitude hydropower reservoirs, while it improved to a lower extent model performance in lowland rivers. In the validation phase, the MLPNN model was generally the one providing the highest performances, although in some river stations ANFIS_FC and ANFIS_GP were slightly more accurate. Overall, the results indicated that the machine learning models developed in this study can be effectively used for river water temperature simulation.

Keywords

River water temperature Air temperature River flow discharge Gregorian calendar MLPNN ANFIS Hydrological regime 

Notes

Acknowledgements

We acknowledge the Swiss Federal Office of the Environment (FOEN), the Swiss Meteorological Institute (MeteoSchweiz), and the Croatian Meteorological and Hydrological Service for providing the water temperature, air temperature, and river flow discharge data used in this study. We thank an anonymous reviewer for the useful comments and suggestions which helped to improve the quality of the study.

Funding

This work was jointly funded by the National Key R&D Program of China (2018YFC0407203, 2016YFC0401506) and the research project from Nanjing Hydraulic Research Institute (Y118009).

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.State Key Laboratory of Hydrology-Water resources and Hydraulic EngineeringNanjing Hydraulic Research InstituteNanjingChina
  2. 2.Faculty of Science, Agronomy Department, Hydraulics DivisionUniversity 20 Août 1955SkikdaAlgeria
  3. 3.Faculty of Electrical Engineering, Computer Science and Information Technology OsijekUniversity J.J. Strossmayer in OsijekOsijekCroatia
  4. 4.Faculty of Civil Engineering OsijekUniversity J.J. Strossmayer in OsijekOsijekCroatia
  5. 5.Institute for Marine and Atmospheric Research, Department of PhysicsUtrecht UniversityUtrechtThe Netherlands
  6. 6.Service for Torrent ControlTrentoItaly

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