Daily precipitation predictions using three different wavelet neural network algorithms by meteorological data

In this study, three different neural network algorithms (feed forward back propagation, FFBP; radial basis function; generalized regression neural network) and wavelet transformation were used for daily precipitation predictions. Different input combinations were tested for the precipitation estimation. As a result, the most appropriate neural network model was determined for each station. Also linear regression model performance is compared with the wavelet neural networks models. It was seen that the wavelet FFBP method provided the best performance evaluation criteria. The results indicate that coupling wavelet transforms with neural network can provide significant advantages for estimation process. In addition, global wavelet spectrum provides considerable information about the structure of the physical process to be modeled.

[1]  Bo Liu,et al.  Projected streamflow in the Huaihe River Basin (2010–2100) using artificial neural network , 2010 .

[2]  Juan B. Valdés,et al.  NONLINEAR MODEL FOR DROUGHT FORECASTING BASED ON A CONJUNCTION OF WAVELET TRANSFORMS AND NEURAL NETWORKS , 2003 .

[3]  H. Aksoy,et al.  Annual and monthly rainfall data generation schemes , 2004 .

[4]  F. Anctil,et al.  An exploration of artificial neural network rainfall-runoff forecasting combined with wavelet decomposition , 2004 .

[5]  B. Krishna,et al.  Monthly Rainfall Prediction Using Wavelet Neural Network Analysis , 2013, Water Resources Management.

[6]  G..  A Theory for Multiresolution Signal Decomposition : The Wavelet Representation , 2004 .

[7]  Akira Kawamura,et al.  Neural Networks for Rainfall Forecasting by Atmospheric Downscaling , 2004 .

[8]  Paulin Coulibaly,et al.  Wavelet analysis of variability in annual Canadian streamflows , 2004 .

[9]  S. Jain,et al.  Radial Basis Function Neural Network for Modeling Rating Curves , 2003 .

[10]  H. Tatli,et al.  A statistical downscaling method for monthly total precipitation over Turkey , 2004 .

[11]  N. J. Ferreira,et al.  Artificial neural network technique for rainfall forecasting applied to the São Paulo region , 2005 .

[12]  Hikmet Kerem Cigizoglu,et al.  Generalized regression neural network in modelling river sediment yield , 2006, Adv. Eng. Softw..

[13]  Vijay P. Singh,et al.  Wet and dry spell analysis of Global Climate Model-generated precipitation using power laws and wavelet transforms , 2011 .

[14]  H. K. Cigizoglu,et al.  ESTIMATION AND FORECASTING OF DAILY SUSPENDED SEDIMENT DATA BY MULTI-LAYER PERCEPTRONS , 2004 .

[15]  Norman W. Junker,et al.  Evaluation of 33 Years of Quantitative Precipitation Forecasting at the NMC , 1995 .

[16]  Jörg H. Siekmann,et al.  Artificial Intelligence and Soft Computing - ICAISC 2004 , 2004, Lecture Notes in Computer Science.

[17]  Hikmet Kerem Cigizoglu,et al.  Generalized regression neural network in monthly flow forecasting , 2005 .

[18]  Hikmet Kerem Cigizoglu,et al.  Estimation, forecasting and extrapolation of river flows by artificial neural networks , 2003 .

[19]  H. K. Cigizoglu,et al.  Prediction of daily precipitation using wavelet—neural networks , 2009 .

[20]  Ingrid Daubechies,et al.  The wavelet transform, time-frequency localization and signal analysis , 1990, IEEE Trans. Inf. Theory.

[21]  Özgür Kişi,et al.  A combined generalized regression neural network wavelet model for monthly streamflow prediction , 2011 .

[22]  K. P. Sudheer,et al.  Modelling evaporation using an artificial neural network algorithm , 2002 .

[23]  John A. Dracup,et al.  Artificial Neural Networks and Long-Range Precipitation Prediction in California , 2000 .

[24]  Hikmet Kerem Cigizoglu,et al.  Rainfall-Runoff Modelling Using Three Neural Network Methods , 2004, ICAISC.

[25]  Asaad Y. Shamseldin,et al.  Comparative study of different wavelet based neural network models for rainfall–runoff modeling , 2014 .

[26]  Jan Adamowski,et al.  Long-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet neural network and wavelet support vector regression models , 2014 .

[27]  Shakeel Ahmed,et al.  Forecasting groundwater level using artificial neural networks. , 2009 .

[28]  A. Barros,et al.  Localized Precipitation Forecasts from a Numerical Weather Prediction Model Using Artificial Neural Networks , 1998 .

[29]  K. P. Sudheer,et al.  Rainfall‐runoff modelling using artificial neural networks: comparison of network types , 2005 .

[30]  Yves Meyer.  Wavelets - algorithms & applications , 1993 .

[31]  Ö. Kisi.  Generalized regression neural networks for evapotranspiration modelling , 2006 .

[32]  A. W. Jayawardena,et al.  Use of Radial Basis Function Type Artificial Neural Networks for Runoff Simulation , 1998 .

[33]  Ashu Jain,et al.  Hybrid neural network models for hydrologic time series forecasting , 2007, Appl. Soft Comput..

[34]  C. Torrence,et al.  A Practical Guide to Wavelet Analysis. , 1998 .

[35]  M Ghodsian,et al.  River flow forecasting using artificial neural networks , 2004 .

[36]  David S. Broomhead,et al.  Multivariable Functional Interpolation and Adaptive Networks , 1988, Complex Syst..

[37]  Sin Chan Chou,et al.  Evaluation of April 1999 rainfall forecasts over South America using the Eta model , 1999 .

[38]  H. Altun,et al.  Treatment of multi-dimensional data to enhance neural network estimators in regression problems , 2006 .

[39]  J. Adamowski,et al.  A wavelet neural network conjunction model for groundwater level forecasting , 2011 .

[40]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[41]  Laurence C. Smith,et al.  Stream flow characterization and feature detection using a discrete wavelet transform , 1998 .

[42]  D. Broomhead,et al.  Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks , 1988 .

[43]  Richard L. Pfeffer,et al.  Comparison of Methodologies for Probabilistic Quantitative Precipitation Forecasting , 2002 .

[44]  Wensheng Wang,et al.  Wavelet Network Model and Its Application to the Prediction of Hydrology , 2003 .

[45]  Özgür Kisi,et al.  Performance of radial basis and LM-feed forward artificial neural networks for predicting daily watershed runoff , 2013, Appl. Soft Comput..