Skip to main content

Advertisement

SpringerLink
Log in

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

  • Research Article
  • Published:
Environmental Science and Pollution Research Aims and scope Submit manuscript

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.

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

Similar content being viewed by others

References

  • Ahmadi-Nedushan B, St-Hilaire A, Ouarda TBMJ, Bilodeau L, Robichaud É, Thiémonge N, Bobée B (2007) Predicting river water temperatures using stochastic models: case study of the Moisie river (Quebec, Canada). Hydrol Process 21:21–34

    Article  Google Scholar 

  • Arismendi I, Safeeq M, Dunham JB, Johnson SL (2014) Can air temperature be used to project influences of climate change on stream temperature? Environ Res Lett 9:084015

    Article  Google Scholar 

  • Benyahya L, Caissie D, St-Hilaire A, Ouarda TBMJ, Bobée B (2007) A review of statistical water temperature models. Can Water Resour J 32:179–192

    Article  Google Scholar 

  • Bonacci O, Oskoruš D (2008) The influence of three Croatian hydroelectric power plants operation on the river Drava hydrological and sediment regime. Xxivth Conference of the Danubian Countries on the Hydrological Forecasting & Hydrological Bases of Water Management

  • Cai H, Piccolroaz S, Huang J, Liu Z, Liu F, Toffolon M (2018) Quantifying the impact of the three gorges dam on the thermal dynamics of the Yangtze River. Environ Res Lett 13:054016

    Article  Google Scholar 

  • Caissie D (2006) The thermal regime of rivers-a review. Freshw Biol 51:1389–1406

    Article  Google Scholar 

  • Caissie D, Satish MG, El-Jabi N (2007) Predicting water temperatures using a deterministic model: application on Miramichi River catchments (New Brunswick, Canada). J Hydrol 336:303–315

    Article  Google Scholar 

  • Cakmakci M (2007) Adaptive neuro-fuzzy modeling of anaerobic digestion of primary sedimentation sludge. Bioprocess Biosyst Eng 30:349–357

    Article  CAS  Google Scholar 

  • Carolli M, Bruno MC, Siviglia A, Maiolini B (2011) Responses of benthic invertebrates to abrupt changes of temperature in flume simulations. River Res Appl 28:678–691

    Article  Google Scholar 

  • Casas-Mulet R, Saltveit SJ, Alfredsen KT (2016) Hydrological and thermal effects of hydropeaking on early life stages of salmonids: a modelling approach for implementing mitigation strategies. Sci Total Environ 573:1660–1672

    Article  CAS  Google Scholar 

  • Cole JC, Maloney KO, Schmid M, McKenna JE (2014) Developing and testing temperature models for regulated systems: a case study on the upper Delaware River. J Hydrol 519:588–598

    Article  Google Scholar 

  • Deweber JT, Wagner T (2014) A regional neural network ensemble for predicting mean daily river water temperature. J Hydrol 517:187–200

    Article  Google Scholar 

  • Eaton JG, Mccormick JH, Stefan HG, Hondzo M (1995) Extreme value analysis of a fish/temperature field database. Ecol Eng 4:289–305

    Article  Google Scholar 

  • Gallice A, Schaefli B, Lehning M, Parlange MB, Huwald H (2015) Stream temperature prediction in ungauged basins: review of recent approaches and description of a new physics-derived statistical model. Hydrol Earth Syst Sci 19:3727–3753

    Article  Google Scholar 

  • Grbić R, Kurtagić D, Slišković D (2013) Stream water temperature prediction based on Gaussian process regression. Expert Syst Appl 40:7407–7414

    Article  Google Scholar 

  • Hadzima-Nyarko M, Rabi A, Šperac M (2014) Implementation of artificial neural networks in modeling the water-air temperature relationship of the river Drava. Water Resour Manag 28:1379–1394

    Article  Google Scholar 

  • Haykin S (1999) Neural networks a Comprehensive Foundation. Prentice Hall, Upper Saddle River

    Google Scholar 

  • He Z, Wen X, Liu H, Du J (2014) A comparative study of artificial neural network, adaptive neuro fuzzy inference system and support vector machine for forecasting river flow in the semiarid mountain region. J Hydrol 509:379–386

    Article  Google Scholar 

  • Hebert C, Caissie D, Satish MG, El-Jabi N (2011) Study of stream temperature dynamics and corresponding heat fluxes within Miramichi River catchments (New Brunswick, Canada). Hydrol Process 25:2439–2455

    Article  Google Scholar 

  • Heddam S (2014) Modeling hourly dissolved oxygen concentration (DO) using two different adaptive neuro-fuzzy inference systems (ANFIS): a comparative study. Environ Monit Assess 186:597–619

    Article  CAS  Google Scholar 

  • Heddam S (2016a) Multilayer perceptron neural network based approach for modelling phycocyanin pigment concentrations: case study from lower Charles River buoy, USA. Environ Sci Pollut Res 23:17210–17225

    Article  CAS  Google Scholar 

  • Heddam S (2016b) New modelling strategy based on radial basis function neural network (RBFNN) for predicting dissolved oxygen concentration using the components of the Gregorian calendar as inputs: case study of Clackamas River, Oregon, USA. Model Earth Syst Environ 2:1–5

    Article  Google Scholar 

  • Heddam S, Kisi O (2017) Extreme learning machines: a new approach for modeling dissolved oxygen (DO) concentration with and without water quality variables as predictors. Environ Sci Pollut Res 24:16702–16724

    Article  CAS  Google Scholar 

  • Hester ET, Doyle MW (2011) Human impacts to river temperature and their effects on biological processes: a quantitative synthesis. J Am Water Resour Assoc 47:571–587

    Article  Google Scholar 

  • Hornik K (1991) Approximation capabilities of multilayer feedforward networks. Neural Netw 4:251–257

    Article  Google Scholar 

  • Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2:359–366

    Article  Google Scholar 

  • Howell PJ, Dunham JB, Sankovich PM (2010) Relationships between water temperatures and upstream migration, cold water refuge use, and spawning of adult bull trout from the Lostine River, Oregon, USA. Ecol Freshw Fish 19:96–106

    Article  Google Scholar 

  • Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23:665–685

    Article  Google Scholar 

  • Jang JSR, Sun CT, Mizutani E (1996) Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Prentice Hall, Upper Saddle River, pp 73–90

    Google Scholar 

  • Jensen MR, Lowney CL (2004) Temperature modeling with HEC-RAS. World Water and Environmental Resources Congress

  • Johnson MF, Wilby RL, Toone JA (2014) Inferring air–water temperature relationships from river and catchment properties. Hydrol Process 28:2912–2928

    Google Scholar 

  • Karaçor AG, Sivri N, Uçan ON (2007) Maximum stream temperature estimation of Degirmendere River using artificial neural network. J Sci Ind Res 66:363–366

    Google Scholar 

  • Karaman S, Ozturk I, Yalcin H, Kayacier A, Sagdic O (2012) Comparison of adaptive neuro-fuzzy inference system and artificial neural networks for estimation of oxidation parameters of sunflower oil added with some natural byproduct extracts. J Sci Food Agric 92:49–58

    Article  CAS  Google Scholar 

  • Kelleher C, Wagener T, Gooseff M, McGlynn B, McGuire K, Marshall L (2012) Investigating controls on the thermal sensitivity of Pennsylvania streams. Hydrol Process 26:771–785

    Article  Google Scholar 

  • Kisi O, Zounemat-Kermani M (2014) Comparison of two different adaptive neuro-fuzzy inference systems in modelling daily reference evapotranspiration. Water Resour Manag 28:2655–2675

    Article  Google Scholar 

  • Krider LA, Magner JA, Perry J, Vondracek B, Ferrington LC (2013) Air-water temperature relationships in the trout streams of southeastern Minnesota’s carbonate-sandstone landscape. J Am Water Resour Assoc 49:896–907

    Article  Google Scholar 

  • Laanaya F, St-Hilaire A, Gloaguen E (2017) Water temperature modelling: comparison between the generalized additive model, logistic, residuals regression and linear regression models. Hydrol Sci J 62:1078–1093

    Google Scholar 

  • Lisi PJ, Schindler DE, Cline TJ, Scheuerell MD, Walsh PB (2015) Watershed geomorphology and snowmelt control stream thermal sensitivity to air temperature. Geophys Res Lett 42:3380–3388

    Article  Google Scholar 

  • Meier W, Wüest A (2004) Wie verändert die hydroelektrische Nutzung die Wassertemperatur der Rhone? Wasser Energie Luft 96:305–309 www.rhone-thur.eawag.ch/wel_rhone.pdf

    Google Scholar 

  • Meile T, Boillat JL, Schleiss AJ (2011) Hydropeaking indicators for characterization of the upper-Rhone River in Switzerland. Aquat Sci 73:171–182

    Article  CAS  Google Scholar 

  • Mohseni O, Stefan HG (1999) Stream temperature/air temperature relationship: a physical interpretation. J Hydrol 218:128–141

    Article  Google Scholar 

  • Mohseni O, Stefan HG, Erickson TR (1998) A non-linear regression model for weekly stream temperatures. Water Resour Res 34:2685–2692

    Article  Google Scholar 

  • Morrill JC, Bales RC, Conklin MH (2005) Estimating stream temperature from air temperature: implications for future water quality. J Environ Eng 131:139–146

    Article  CAS  Google Scholar 

  • Olden JD, Joy MK, Death RG (2004) An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data. Ecol Model 178:389–397

    Article  Google Scholar 

  • Phelps QE, Tripp SJ, Hintz WD, Garvey JE, Herzog DP, Ostendorf DE, Ridings JW, Crites JW, Hrabik RA (2010) Water temperature and river stage influence mortality and abundance of naturally occurring Mississippi River scaphirhynchus sturgeon. N Am J Fish Manag 30:767–775

    Article  Google Scholar 

  • Piccolroaz S, Toffolon M, Majone B (2015) The role of stratification on lakes’ thermal response: the case of Lake Superior. Water Resour Res 51:7878–7894

    Article  Google Scholar 

  • Piccolroaz S, Calamita E, Majone B, Gallice A, Siviglia A, Toffolon M (2016) Prediction of river water temperature: a comparison between a new family of hybrid models and statistical approaches. Hydrol Process 30:3901–3917

    Article  Google Scholar 

  • Piccolroaz S, Toffolon M, Robinson CT, Siviglia A (2018) Exploring and Quantifying River thermal response to heatwaves. Water 10:1098

    Article  Google Scholar 

  • Piotrowski AP, Osuch M, Napiorkowski MJ, Rowinski PM, Napiorkowski JJ (2014) Comparing large number of metaheuristics for artificial neural networks training to predict water temperature in a natural river. Comput Geosci 64:136–151

    Article  Google Scholar 

  • Piotrowski AP, Napiorkowski MJ, Napiorkowski JJ, Osuch M (2015) Comparing various artificial neural network types for water temperature prediction in rivers. J Hydrol 529:302–315

    Article  Google Scholar 

  • Rabi A, Hadzima-Nyarko M, Sperac M (2015) Modelling river temperature from air temperature in the river Drava (Croatia). Hydrol Sci J 60:1490–1507

    Article  Google Scholar 

  • Rajwakuligiewicz A, Bialik RJ, Rowiński PM (2015) Dissolved oxygen and water temperature dynamics in lowland rivers over various timescales. J Hydrol Hydromech 63:353–363

    Article  CAS  Google Scholar 

  • Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representations by error propagation. MIT Press, Massachusetts

    Google Scholar 

  • Sahoo GB, Schladow SG, Reuter JE (2009) Forecasting stream water temperature using regression analysis, artificial neural network, and chaotic non-linear dynamic models. J Hydrol 378:325–342

    Article  Google Scholar 

  • Sandersfeld T, Mark FC, Knust R (2017) Temperature-dependent metabolism in Antarctic fish: do habitat temperature conditions affect thermal tolerance ranges? Polar Biol 40:1–9

    Article  Google Scholar 

  • Sanikhani H, Kisi O, Nikpour MR, Dinpashoh Y (2012) Estimation of daily pan evaporation using two different adaptive neuro-fuzzy computing techniques. Water Resour Manag 26:4347–4365

    Article  Google Scholar 

  • Shiri J, Dierickx W, Baba PA, Neamati S, Ghorbani MA (2011) Estimating daily pan evaporation from climatic data of the state of Illinois, USA using adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN). Hydrol Res 42:491–502

    Article  Google Scholar 

  • Sohrabi MM, Benjankar R, Tonina D, Wenger SJ, Isaak DJ (2017) Estimation of daily stream water temperatures with a Bayesian regression approach. Hydrol Process 31:1719–1733

    Article  Google Scholar 

  • Stefan HG, Preud’homme EB (1993) Stream temperature estimation from air temperature. J Am Water Resour Assoc 29:27–45

    Article  Google Scholar 

  • Temizyurek M, Dadasercelik F (2018) Modelling the effects of meteorological parameters on water temperature using artificial neural networks. Water Sci Technol 77:1724–1733

    Article  Google Scholar 

  • Toffolon M, Piccolroaz S (2015) A hybrid model for river water temperature as a function of air temperature and discharge. Environ Res Lett 10:114011

    Article  Google Scholar 

  • Toffolon M, Piccolroaz S, Majone B, Soja AM, Peeters F, Schmid M, Wuest A (2014) Prediction of surface temperature in lakes with different morphology using air temperature. Limnol Oceanogr 59:2185–2202

    Article  Google Scholar 

  • Verbrugge LNH, Schipper AM, Huijbregts MAJ, Velde GVD, Leuven RSEW (2012) Sensitivity of native and non-native mollusc species to changing river water temperature and salinity. Biol Invasions 14:1187–1199

    Article  Google Scholar 

  • Vliet MTHV, Ludwig F, Zwolsman JJG, Weedon GP, Kabat P (2011) Global river temperatures and sensitivity to atmospheric warming and changes in river flow. Water Resour Res 47:247–255

    Google Scholar 

  • Vliet MTHV, Yearsley JR, Franssen WHP, Ludwig F, Haddeland I, Lettenmaier DP, Kabat P (2012) Coupled daily streamflow and water temperature modeling in large river basins. Hydrol Earth Syst Sci 16:4303–4321

    Article  Google Scholar 

  • Wang Q (2013) Prediction of water temperature as affected by a pre-constructed reservoir project based on MIKE11. Acta Hydrochim Hydrobiol 41:1039–1043

    CAS  Google Scholar 

  • Webb BW, Clack PD, Walling DE (2003) Water-air temperature relationships in a Devon river system and the role of flow. Hydrol Process 17:3069–3084

    Article  Google Scholar 

  • Wei M, Bai B, Sung AH, Liu Q, Wang J, Cather ME (2007) Predicting injection profiles using ANFIS. Inf Sci 177:4445–4461

    Article  Google Scholar 

  • Westhoff JT, Rosenberger AE (2016) A global review of freshwater crayfish temperature tolerance, preference, and optimal growth. Rev Fish Biol Fish 26:329–349

    Article  Google Scholar 

  • Yurdusev MA, Firat M (2009) Adaptive neuro fuzzy inference system approach for municipal water consumption modeling: an application to Izmir, Turkey. J Hydrol 365:225–234

    Article  Google Scholar 

  • Zhu S, Nyarko EK, Nyarko MH (2018) Modelling daily water temperature from air temperature for the Missouri River. PeerJ 6:e4894

    Article  Google Scholar 

Download references

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).

Author information

Authors and Affiliations

  1. State Key Laboratory of Hydrology-Water resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing, 210029, China

    Senlin Zhu & Shiqiang Wu

  2. Faculty of Science, Agronomy Department, Hydraulics Division, University 20 Août 1955, Route El Hadaik, BP 26, Skikda, Algeria

    Salim Heddam

  3. Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, University J.J. Strossmayer in Osijek, Kneza Trpimira 2b, 31000, Osijek, Croatia

    Emmanuel Karlo Nyarko

  4. Faculty of Civil Engineering Osijek, University J.J. Strossmayer in Osijek, Osijek, Croatia

    Marijana Hadzima-Nyarko

  5. Institute for Marine and Atmospheric Research, Department of Physics, Utrecht University, Princetonplein 5, 3584, CC, Utrecht, The Netherlands

    Sebastiano Piccolroaz

  6. Service for Torrent Control, Autonomous Province of Trento, via Trener 3, I-38121, Trento, Italy

    Sebastiano Piccolroaz

Authors
  1. Senlin Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Salim Heddam
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Emmanuel Karlo Nyarko
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Marijana Hadzima-Nyarko
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sebastiano Piccolroaz
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Shiqiang Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Senlin Zhu.

Additional information

Responsible editor: Marcus Schulz

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhu, S., Heddam, S., Nyarko, E.K. et al. Modeling daily water temperature for rivers: comparison between adaptive neuro-fuzzy inference systems and artificial neural networks models. Environ Sci Pollut Res 26, 402–420 (2019). https://doi.org/10.1007/s11356-018-3650-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11356-018-3650-2

Keywords

Search

Navigation