Estimation of Long-Term Monthly Temperatures by Three Different Adaptive Neuro-Fuzzy Approaches Using Geographical Inputs

AbstractThis paper investigates the accuracy of three different adaptive neuro-fuzzy inference systems (ANFISs), ANFIS with grid partition (ANFIS-GP), ANFIS with substructive clustering (ANFIS-SC),...

[1]  H. Toros.  Spatio‐temporal precipitation change assessments over Turkey , 2012 .

[2]  Ozgur Kisi,et al.  Estimation of mean monthly air temperatures in Turkey , 2014 .

[3]  Ozgur Kisi,et al.  Global cross-station assessment of neuro-fuzzy models for estimating daily reference evapotranspiration , 2013 .

[4]  Raju K. George,et al.  Prediction of soil temperature by using artificial neural networks algorithms , 2001 .

[5]  Ozgur Kisi,et al.  Prediction of long‐term monthly precipitation using several soft computing methods without climatic data , 2015 .

[6]  Deg-Hyo Bae,et al.  Monthly dam inflow forecasts using weather forecasting information and neuro-fuzzy technique , 2007 .

[7]  Ozgur Kisi,et al.  Estimation of dew point temperature using neuro-fuzzy and neural network techniques , 2013, Theoretical and Applied Climatology.

[8]  O. Kisi,et al.  Prediction of long‐term monthly air temperature using geographical inputs , 2014 .

[9]  Ali M. Abdulshahed,et al.  Thermal error modelling of machine tools based on ANFIS with fuzzy c-means clustering using a thermal imaging camera , 2015 .

[10]  Mohammad Najafzadeh.  Neurofuzzy-Based GMDH-PSO to Predict Maximum Scour Depth at Equilibrium at Culvert Outlets , 2016 .

[11]  Tarek Sayed,et al.  Comparison of Adaptive Network Based Fuzzy Inference Systems and B-spline Neuro-Fuzzy Mode Choice Models , 2003 .

[12]  Narendra Singh Raghuwanshi,et al.  Estimation of monthly solar radiation from measured air temperature extremes , 2008 .

[13]  Ozgur Kisi,et al.  Comparison of Two Different Adaptive Neuro-Fuzzy Inference Systems in Modelling Daily Reference Evapotranspiration , 2014, Water Resources Management.

[14]  T. S. Lee,et al.  Applicability of Adaptive Neuro-Fuzzy Inference Systems in Daily Reservoir Inflow Forecasting , 2011 .

[15]  Sungwon Kim,et al.  Modeling the physical dynamics of daily dew point temperature using soft computing techniques , 2015 .

[16]  V. Singh,et al.  Estimating Daily Pan Evaporation Using Different Data-Driven Methods and Lag-Time Patterns , 2013, Water Resources Management.

[17]  O. Kisi,et al.  SVM, ANFIS, regression and climate based models for reference evapotranspiration modeling using limited climatic data in a semi-arid highland environment , 2012 .

[18]  Sungwon Kim,et al.  Daily water level forecasting using wavelet decomposition and artificial intelligence techniques , 2015 .

[19]  Ahmed El-Shafie,et al.  A neuro-fuzzy model for inflow forecasting of the Nile river at Aswan high dam , 2007 .

[20]  R. E. Abdel-Aal,et al.  Hourly temperature forecasting using abductive networks , 2004, Eng. Appl. Artif. Intell..

[21]  Mohammad Najafzadeh,et al.  Neuro-fuzzy GMDH systems based evolutionary algorithms to predict scour pile groups in clear water conditions , 2015 .

[22]  Gerrit Hoogenboom,et al.  Dewpoint Temperature Prediction Using Artificial Neural Networks , 2008 .

[23]  J. Kirchner.  Getting the right answers for the right reasons: Linking measurements, analyses, and models to advance the science of hydrology , 2006 .

[24]  Ozgur Kisi,et al.  Estimation of Daily Pan Evaporation Using Two Different Adaptive Neuro-Fuzzy Computing Techniques , 2012, Water Resources Management.

[25]  Mehmet Bilgili,et al.  Prediction of Long-term Monthly Temperature and Rainfall in Turkey , 2009 .

[26]  F. A. Ogbu,et al.  The importance of maximum air temperature in the parameterisation of solar radiation in Nigeria , 1995 .

[27]  Andrea Király,et al.  Stochastic modeling of daily temperature fluctuations. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[28]  Imre M. Jánosi,et al.  Nonlinear correlations of daily temperature records over land , 2006 .

[29]  Ozgur Kisi,et al.  Estimation of daily dew point temperature using genetic programming and neural networks approaches , 2014 .

[30]  Ozgur Kisi,et al.  Local vs. external training of neuro-fuzzy and neural networks models for estimating reference evapotranspiration assessed through k-fold testing , 2015 .

[31]  Lisa Guan,et al.  Cross-correlations between weather variables in Australia , 2007 .

[32]  Özgür Kisi,et al.  Neuro-fuzzy and neural network techniques for forecasting sea level in Darwin Harbor, Australia , 2013, Comput. Geosci..

[33]  H. Wackernagel,et al.  Mapping temperature using kriging with external drift: Theory and an example from scotland , 1994 .

[34]  Darko Joksimovic,et al.  Plunging Flow Depth Estimation in a Stratified Dam Reservoir Using Neuro-Fuzzy Technique , 2015, Water Resources Management.

[35]  Ozgur Kisi,et al.  Estimating daily reference evapotranspiration using available and estimated climatic data by adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) , 2013 .

[36]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[37]  Chuen-Tsai Sun,et al.  Neuro-fuzzy modeling and control , 1995, Proc. IEEE.

[38]  D. Marks,et al.  Daily air temperature interpolated at high spatial resolution over a large mountainous region , 1997 .

[39]  J.-S.R. Jang,et al.  Input selection for ANFIS learning , 1996, Proceedings of IEEE 5th International Fuzzy Systems.

[40]  Mahdi Eftekhari,et al.  Extracting compact fuzzy rules for nonlinear system modeling using subtractive clustering, GA and unscented filter , 2008 .

[41]  Ozgur Kisi,et al.  Modeling soil temperatures at different depths by using three different neural computing techniques , 2015, Theoretical and Applied Climatology.

[42]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[43]  Ozgur Kisi,et al.  River suspended sediment modelling using a fuzzy logic approach , 2006 .

[44]  O. Kisi,et al.  Short-term and long-term streamflow forecasting using a wavelet and neuro-fuzzy conjunction model , 2010 .

[45]  Chuntian Cheng,et al.  Long-Term Prediction of Discharges in Manwan Hydropower Using Adaptive-Network-Based Fuzzy Inference Systems Models , 2005, ICNC.

[46]  Ozgur Kisi,et al.  Modelling solar radiation reached to the Earth using ANFIS, NN-ARX, and empirical models (Case studies: Zahedan and Bojnurd stations) , 2015 .

[47]  Z. Samani,et al.  Reference crop evapotranspiration from ambient air temperature , 1985 .

[48]  Peggy A. Johnson,et al.  Stream hydrological and ecological responses to climate change assessed with an artificial neural network , 1996 .

[49]  Songul Albayrak,et al.  FUZZY C-MEANS CLUSTERING ON MEDICAL DIAGNOSTIC SYSTEMS , 2003 .

[50]  Özgür Kisi,et al.  Long-term monthly evapotranspiration modeling by several data-driven methods without climatic data , 2015, Comput. Electron. Agric..

[51]  Vijay P. Singh,et al.  Modeling daily soil temperature using data-driven models and spatial distribution , 2014, Theoretical and Applied Climatology.

[52]  Özgür Kişi,et al.  Determination of Mean Velocity and Discharge in Natural Streams Using Neuro-Fuzzy and Neural Network Approaches , 2014, Water Resources Management.