Assessment of Pan Evaporation Modeling Using Bootstrap Resampling and Soft Computing Methods

AbstractThis study evaluated combined bootstrap resampling and neural network models for estimating daily pan evaporation (PE) in the Republic of Korea. Two different support vector machines (SVMs), e-support vector regression (e-SVR) and ν-support vector regression (ν-SVR), were developed for the local implementation of SVMs. Five-input combination models (e-SVR5 and ν-SVR5) were found to be generally the best for the local implementation of SVMs. Optimal SVMs, including e-SVR5 and ν-SVR5, were employed to develop bootstrap-based support vector machines (BSVMs) for two weather stations. The ensemble PE was estimated by averaging the output of 50 individual BSVMs. A Mann-Whitney U test was performed to compare the observed and ensemble of bootstrap resamplings for the training data of the PE. The uncertainty associated with PE estimation using BSVMs was evaluated. Results indicated that BSVMs could improve confidence in PE estimation and that ensemble PE using BSVMs is more stable and reliable than that u...

[1]  B. Efron.  Bootstrap Methods: Another Look at the Jackknife , 1979 .

[2]  Hung Soo Kim,et al.  Neural networks and genetic algorithm approach for nonlinear evaporation and evapotranspiration modeling , 2008 .

[3]  Chandranath Chatterjee,et al.  Uncertainty assessment and ensemble flood forecasting using bootstrap based artificial neural networks (BANNs) , 2010 .

[4]  A. Tokar,et al.  Rainfall-Runoff Modeling Using Artificial Neural Networks , 1999 .

[5]  Ozgur Kisi,et al.  Generalized Neurofuzzy Models for Estimating Daily Pan Evaporation Values from Weather Data , 2012 .

[6]  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 .

[7]  H. Akaike.  A new look at the statistical model identification , 1974 .

[8]  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 .

[9]  Bellie Sivakumar,et al.  River flow forecasting: use of phase-space reconstruction and artificial neural networks approaches , 2002 .

[10]  Özgür Kişi,et al.  Modeling monthly evaporation using two different neural computing techniques , 2009, Irrigation Science.

[11]  V. V. Srinivas,et al.  Downscaling of precipitation for climate change scenarios: A support vector machine approach , 2006 .

[12]  Ozgur Kisi,et al.  Reply to the Discussion of “Evapotranspiration modelling using support vector machines” by R. J. Abrahart et al. , 2010 .

[13]  Anil Kumar Singh,et al.  A Comparative Study of Daily Pan Evaporation Estimation Using ANN, Regression and Climate Based Models , 2010 .

[14]  Li-Chiu Chang,et al.  Assessing the effort of meteorological variables for evaporation estimation by self-organizing map neural network , 2010 .

[15]  Özlem Terzi,et al.  Artificial Neural Network Models of Daily Pan Evaporation , 2006 .

[16]  Holger R. Maier,et al.  Neural networks for the prediction and forecasting of water resource variables: a review of modelling issues and applications , 2000, Environ. Model. Softw..

[17]  Martin F. Lambert,et al.  Bayesian training of artificial neural networks used for water resources modeling , 2005 .

[18]  Alison L. Kay,et al.  Calculating potential evaporation from climate model data: A source of uncertainty for hydrological climate change impacts , 2008 .

[19]  William W. Hsieh,et al.  Applying Neural Network Models to Prediction and Data Analysis in Meteorology and Oceanography. , 1998 .

[20]  O. Kisi,et al.  Pan Evaporation Modeling Using Neural Computing Approach for Different Climatic Zones , 2012, Water Resources Management.

[21]  Bernhard Schölkopf,et al.  New Support Vector Algorithms , 2000, Neural Computation.

[22]  Simon Li,et al.  Uncertainties in real‐time flood forecasting with neural networks , 2007 .

[23]  K. P. Sudheer,et al.  Explaining the internal behaviour of artificial neural network river flow models , 2004 .

[24]  Ozgur Kisi,et al.  Streamflow Forecasting Using Different Artificial Neural Network Algorithms , 2007 .

[25]  Thomas M. Cover,et al.  Geometrical and Statistical Properties of Systems of Linear Inequalities with Applications in Pattern Recognition , 1965, IEEE Trans. Electron. Comput..

[26]  Hossein Tabari,et al.  Estimation of daily pan evaporation using artificial neural network and multivariate non-linear regression , 2010, Irrigation Science.

[27]  Young-Oh Kim,et al.  Rainfall‐runoff models using artificial neural networks for ensemble streamflow prediction , 2005 .

[28]  N. S. Raghuwanshi,et al.  Artificial neural networks approach in evapotranspiration modeling: a review , 2010, Irrigation Science.

[29]  Teresa B. Culver,et al.  Bootstrapped artificial neural networks for synthetic flow generation with a small data sample , 2006 .

[30]  R. W. McClendon,et al.  Estimating daily pan evaporation with artificial neural networks , 2000 .

[31]  K. N. Tiwari,et al.  Bootstrap based artificial neural network (BANN) analysis for hierarchical prediction of monthly runoff in Upper Damodar Valley Catchment , 2009 .

[32]  Robert Tibshirani,et al.  A Comparison of Some Error Estimates for Neural Network Models , 1996, Neural Computation.

[33]  Alice E. Smith,et al.  Bias and variance of validation methods for function approximation neural networks under conditions of sparse data , 1998, IEEE Trans. Syst. Man Cybern. Part C.

[34]  E. Zio,et al.  A study of the bootstrap method for estimating the accuracy of artificial neural networks in predicting nuclear transient processes , 2006, IEEE Transactions on Nuclear Science.

[35]  Chandranath Chatterjee,et al.  Development of an accurate and reliable hourly flood forecasting model using wavelet–bootstrap–ANN (WBANN) hybrid approach , 2010 .

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

[37]  Ozgur Kisi,et al.  Evapotranspiration modelling using support vector machines / Modélisation de l'évapotranspiration à l'aide de ‘support vector machines’ , 2009 .

[38]  B. Bates,et al.  Nonlinear, discrete flood event models, 3. Analysis of prediction uncertainty , 1988 .

[39]  Indrajeet Chaubey,et al.  A simplified approach to quantifying predictive and parametric uncertainty in artificial neural network hydrologic models , 2007 .

[40]  Aytac Guven,et al.  Daily pan evaporation modeling using linear genetic programming technique , 2011, Irrigation Science.

[41]  Paulin Coulibaly,et al.  Bayesian neural network for rainfall‐runoff modeling , 2006 .