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.
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
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
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
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
Caissie D (2006) The thermal regime of rivers-a review. Freshw Biol 51:1389–1406
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
Cakmakci M (2007) Adaptive neuro-fuzzy modeling of anaerobic digestion of primary sedimentation sludge. Bioprocess Biosyst Eng 30:349–357
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
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
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
Deweber JT, Wagner T (2014) A regional neural network ensemble for predicting mean daily river water temperature. J Hydrol 517:187–200
Eaton JG, Mccormick JH, Stefan HG, Hondzo M (1995) Extreme value analysis of a fish/temperature field database. Ecol Eng 4:289–305
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
Grbić R, Kurtagić D, Slišković D (2013) Stream water temperature prediction based on Gaussian process regression. Expert Syst Appl 40:7407–7414
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
Haykin S (1999) Neural networks a Comprehensive Foundation. Prentice Hall, Upper Saddle River
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
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
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
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
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
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
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
Hornik K (1991) Approximation capabilities of multilayer feedforward networks. Neural Netw 4:251–257
Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2:359–366
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
Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23:665–685
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
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
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
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
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
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
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
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
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
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
Meile T, Boillat JL, Schleiss AJ (2011) Hydropeaking indicators for characterization of the upper-Rhone River in Switzerland. Aquat Sci 73:171–182
Mohseni O, Stefan HG (1999) Stream temperature/air temperature relationship: a physical interpretation. J Hydrol 218:128–141
Mohseni O, Stefan HG, Erickson TR (1998) A non-linear regression model for weekly stream temperatures. Water Resour Res 34:2685–2692
Morrill JC, Bales RC, Conklin MH (2005) Estimating stream temperature from air temperature: implications for future water quality. J Environ Eng 131:139–146
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
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
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
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
Piccolroaz S, Toffolon M, Robinson CT, Siviglia A (2018) Exploring and Quantifying River thermal response to heatwaves. Water 10:1098
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
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
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
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
Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representations by error propagation. MIT Press, Massachusetts
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
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
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
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
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
Stefan HG, Preud’homme EB (1993) Stream temperature estimation from air temperature. J Am Water Resour Assoc 29:27–45
Temizyurek M, Dadasercelik F (2018) Modelling the effects of meteorological parameters on water temperature using artificial neural networks. Water Sci Technol 77:1724–1733
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
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
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
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
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
Wang Q (2013) Prediction of water temperature as affected by a pre-constructed reservoir project based on MIKE11. Acta Hydrochim Hydrobiol 41:1039–1043
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
Wei M, Bai B, Sung AH, Liu Q, Wang J, Cather ME (2007) Predicting injection profiles using ANFIS. Inf Sci 177:4445–4461
Westhoff JT, Rosenberger AE (2016) A global review of freshwater crayfish temperature tolerance, preference, and optimal growth. Rev Fish Biol Fish 26:329–349
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
Zhu S, Nyarko EK, Nyarko MH (2018) Modelling daily water temperature from air temperature for the Missouri River. PeerJ 6:e4894
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
Corresponding author
Additional information
Responsible editor: Marcus Schulz
Rights and permissions
About this article
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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11356-018-3650-2