Modeling daily water temperature for rivers: comparison between adaptive neuro-fuzzy inference systems and artificial neural networks models
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Salim Heddam | Marijana Hadzima-Nyarko | Emmanuel Karlo Nyarko | Shiqiang Wu | Sebastiano Piccolroaz | Senlin Zhu | Senlin Zhu | S. Heddam | Shiqiang Wu | E. Nyarko | M. Hadzima-Nyarko | S. Piccolroaz
[1] Annunziato Siviglia,et al. Prediction of river water temperature: a comparison between a new family of hybrid models and statistical approaches , 2016 .
[2] J.-L. Boillat,et al. Hydropeaking indicators for characterization of the Upper-Rhone River in Switzerland , 2011, Aquatic Sciences.
[3] M. A. Yurdusev,et al. Adaptive neuro fuzzy inference system approach for municipal water consumption modeling: An application to Izmir, Turkey , 2009 .
[4] Chuen-Tsai Sun,et al. Neuro-fuzzy And Soft Computing: A Computational Approach To Learning And Machine Intelligence [Books in Brief] , 1997, IEEE Transactions on Neural Networks.
[5] R. Wilby,et al. Inferring air–water temperature relationships from river and catchment properties , 2013 .
[6] Erwan Gloaguen,et al. Water temperature modelling: comparison between the generalized additive model, logistic, residuals regression and linear regression models , 2017 .
[7] Qinggai Wang. Prediction of Water Temperature as Affected by a Pre‐Constructed Reservoir Project Based on MIKE11 , 2013 .
[8] Salim Heddam,et al. 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 , 2016, Modeling Earth Systems and Environment.
[9] Marco Toffolon,et al. A hybrid model for river water temperature as a function of air temperature and discharge , 2015 .
[10] Salim Heddam,et al. Multilayer perceptron neural network-based approach for modeling phycocyanin pigment concentrations: case study from lower Charles River buoy, USA , 2016, Environmental Science and Pollution Research.
[11] B. Majone,et al. Prediction of surface temperature in lakes with different morphology using air temperature , 2014 .
[12] Mysore G. Satish,et al. Predicting water temperatures using a deterministic model : Application on Miramichi River catchments (New Brunswick, Canada) , 2007 .
[13] M. Parlange,et al. Stream temperature prediction in ungauged basins: review of recent approaches and description of a new physics-derived statistical model , 2015 .
[14] Bernard Bobée,et al. A Review of Statistical Water Temperature Models , 2007 .
[15] Adam P. Piotrowski,et al. Comparing various artificial neural network types for water temperature prediction in rivers , 2015 .
[16] D. Caissie. The thermal regime of rivers : a review , 2006 .
[17] Heinz G. Stefan,et al. A nonlinear regression model for weekly stream temperatures , 1998 .
[18] Pavel Kabat,et al. Global river temperatures and sensitivity to atmospheric warming and changes in river flow , 2011 .
[19] M. Toffolon,et al. Exploring and Quantifying River Thermal Response to Heatwaves , 2018, Water.
[20] J. Garvey,et al. Water Temperature and River Stage Influence Mortality and Abundance of Naturally Occurring Mississippi River Scaphirhynchus Sturgeon , 2010 .
[21] M. Huijbregts,et al. Sensitivity of native and non-native mollusc species to changing river water temperature and salinity , 2012, Biological Invasions.
[22] Jason B. Dunham,et al. Can air temperature be used to project influences of climate change on stream temperature? , 2014 .
[23] J. Dunham,et al. Relationships between water temperatures and upstream migration, cold water refuge use, and spawning of adult bull trout from the Lostine River, Oregon, USA , 2010 .
[24] Ratko Grbic,et al. Stream water temperature prediction based on Gaussian process regression , 2013, Expert Syst. Appl..
[25] A. Rosenberger,et al. A global review of freshwater crayfish temperature tolerance, preference, and optimal growth , 2016, Reviews in Fish Biology and Fisheries.
[26] B. Vondracek,et al. Air‐Water Temperature Relationships in the Trout Streams of Southeastern Minnesota's Carbonate‐Sandstone Landscape , 2012 .
[27] J. Kur,et al. Genetic and biochemical characterization of yeasts isolated from Antarctic soil samples , 2017, Polar Biology.
[28] Simon Haykin,et al. Neural Networks: A Comprehensive Foundation , 1998 .
[29] Kurt Hornik,et al. Multilayer feedforward networks are universal approximators , 1989, Neural Networks.
[30] Marijana Hadzima-Nyarko,et al. Modelling river temperature from air temperature: case of the River Drava (Croatia) , 2015 .
[31] Ozgur Kisi,et al. Estimation of Daily Pan Evaporation Using Two Different Adaptive Neuro-Fuzzy Computing Techniques , 2012, Water Resources Management.
[32] Daniele Tonina,et al. Estimation of daily stream water temperatures with a Bayesian regression approach , 2017 .
[33] Marijana Hadzima-Nyarko,et al. Implementation of Artificial Neural Networks in Modeling the Water-Air Temperature Relationship of the River Drava , 2014, Water Resources Management.
[34] M. Temizyurek,et al. Modelling the effects of meteorological parameters on water temperature using artificial neural networks. , 2018, Water science and technology : a journal of the International Association on Water Pollution Research.
[35] Kurt Hornik,et al. Approximation capabilities of multilayer feedforward networks , 1991, Neural Networks.
[36] Qingzhong Liu,et al. Predicting injection profiles using ANFIS , 2007, Inf. Sci..
[37] K. Alfredsen,et al. Hydrological and thermal effects of hydropeaking on early life stages of salmonids: A modelling approach for implementing mitigation strategies. , 2016, The Science of the total environment.
[38] Dennis P. Lettenmaier,et al. Coupled daily streamflow and water temperature modelling in large river basins , 2012 .
[39] R. Knust,et al. Temperature-dependent metabolism in Antarctic fish: Do habitat temperature conditions affect thermal tolerance ranges? , 2016, Polar Biology.
[40] Adam P. Piotrowski,et al. Comparing large number of metaheuristics for artificial neural networks training to predict water temperature in a natural river , 2014, Comput. Geosci..
[41] Matthias Schmid,et al. Developing and testing temperature models for regulated systems: A case study on the Upper Delaware River , 2014 .
[42] Heinz G. Stefan,et al. Extreme value analysis of a fish/temperature field database , 1995 .
[43] Feng Liu,et al. Quantifying the impact of the Three Gorges Dam on the thermal dynamics of the Yangtze River , 2018 .
[44] D. Schindler,et al. Watershed geomorphology and snowmelt control stream thermal sensitivity to air temperature , 2015 .
[45] Mehmet Cakmakci,et al. Adaptive neuro-fuzzy modelling of anaerobic digestion of primary sedimentation sludge , 2007, Bioprocess and biosystems engineering.
[46] Tyler Wagner,et al. A regional neural network ensemble for predicting mean daily river water temperature , 2014 .
[47] A. Siviglia,et al. RESPONSES OF BENTHIC INVERTEBRATES TO ABRUPT CHANGES OF TEMPERATURE IN FLUME SIMULATIONS , 2012 .
[48] Roger C. Bales,et al. Estimating Stream Temperature from Air Temperature: Implications for Future Water Quality , 2005 .
[49] P. Rowiński,et al. Dissolved oxygen and water temperature dynamics in lowland rivers over various timescales , 2015 .
[50] G. Sahoo,et al. Forecasting stream water temperature using regression analysis, artificial neural network, and chaotic non-linear dynamic models , 2009 .
[51] Heinz G. Stefan,et al. STREAM TEMPERATURE ESTIMATION FROM AIR TEMPERATURE , 1993 .
[52] Heinz G. Stefan,et al. Stream temperature/air temperature relationship : a physical interpretation , 1999 .
[53] Desmond E. Walling,et al. Water–air temperature relationships in a Devon river system and the role of flow , 2003 .
[54] B. Majone,et al. The role of stratification on lakes' thermal response: The case of Lake Superior , 2015 .
[55] X. Wen,et al. 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 , 2014 .
[56] Ozgur Kisi,et al. Extreme learning machines: a new approach for modeling dissolved oxygen (DO) concentration with and without water quality variables as predictors , 2017, Environmental Science and Pollution Research.
[57] Martin W. Doyle,et al. Human Impacts to River Temperature and Their Effects on Biological Processes: A Quantitative Synthesis 1 , 2011 .
[58] Mohammad Ali Ghorbani,et al. 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) , 2011 .
[59] Jyh-Shing Roger Jang,et al. ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..
[60] Osman Sagdic,et al. 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. , 2012, Journal of the science of food and agriculture.
[61] Geoffrey E. Hinton,et al. Learning internal representations by error propagation , 1986 .
[62] M. Jensen,et al. Temperature Modeling with HEC - RAS , 2004 .
[63] Marijana Hadzima-Nyarko,et al. Modelling daily water temperature from air temperature for the Missouri River , 2018, PeerJ.
[64] Russell G. Death,et al. An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data , 2004 .
[65] Ozgur Kisi,et al. Comparison of Two Different Adaptive Neuro-Fuzzy Inference Systems in Modelling Daily Reference Evapotranspiration , 2014, Water Resources Management.
[66] Taha B. M. J. Ouarda,et al. Predicting river water temperatures using stochastic models: case study of the Moisie River (Québec, Canada) , 2007 .
[67] Thorsten Wagener,et al. Investigating controls on the thermal sensitivity of Pennsylvania streams , 2012 .
[68] D. Caissie,et al. Study of stream temperature dynamics and corresponding heat fluxes within Miramichi River catchments (New Brunswick, Canada) , 2011 .
[69] Salim Heddam,et al. Modeling hourly dissolved oxygen concentration (DO) using two different adaptive neuro-fuzzy inference systems (ANFIS): a comparative study , 2013, Environmental Monitoring and Assessment.