Application of bootstrap-based neural networks for monthly rainfall forecasting in Western Jilin Province, China

Rainfall forecasting is an important pre-requisite for effectively managing and planning water resources. This study developed a generalized regression neural network (GRNN) combined with a bootstrap approach for rainfall forecasting, and the forecasting results were compared with the autoregressive model and single GRNN model. The test was performed in western Jilin Province, China with a 53-year (1957–2010) monthly rainfall time series. To obtain the good performance of GRNN model, the number of input neurons was decided by the analysis of Bayesian information criterion, and the appropriate spread was selected considering the performance of the training and testing phases. mean absolute error, root mean square error, coefficient of efficiency and R 2 are employed to evaluate the performances of the forecasting models. The results showed that the bootstrap-based GRNN model performed better than single GRNN and AR models in forecasting monthly rainfall and the proposed method can improve the prediction accuracy of monthly rainfall time series, while generating uncertainty estimates of the rainfall forecasting.

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

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

[3]  Ahmed El-Shafie,et al.  Radial basis function neural networks for reliably forecasting rainfall , 2012 .

[4]  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..

[5]  H. Lütkepohl.  COMPARISON OF CRITERIA FOR ESTIMATING THE ORDER OF A VECTOR AUTOREGRESSIVE PROCESS , 1985 .

[6]  Ayhan Kesimal,et al.  Application of Generalized Regression Neural Networks in Predicting the Unconfined Compressive Strength of Carbonate Rocks , 2012, Rock Mechanics and Rock Engineering.

[7]  Robert Tibshirani,et al.  An Introduction to the Bootstrap , 1994 .

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

[9]  Robert J. Abrahart,et al.  Neural network rainfall-runoff forecasting based on continuous resampling , 2003 .

[10]  Sanjeev S. Tambe,et al.  Prediction of all India summer monsoon rainfall using error-back-propagation neural networks , 1997 .

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

[12]  Mahmud Güngör,et al.  Generalized Regression Neural Networks and Feed Forward Neural Networks for prediction of scour depth around bridge piers , 2009, Adv. Eng. Softw..

[13]  Harianto Rahardjo,et al.  Prediction of pore-water pressure using radial basis function neural network , 2012 .

[14]  Marina Theodosiou,et al.  Disaggregation & aggregation of time series components: A hybrid forecasting approach using generalized regression neural networks and the theta method , 2011, Neurocomputing.

[15]  Madan M. Gupta,et al.  Improving reliability of river flow forecasting using neural networks, wavelets and self-organising maps , 2013 .

[16]  Upul Sonnadara,et al.  Performance of an artificial neural network on forecasting the daily occurrence and annual depth of rainfall at a tropical site , 2008 .

[17]  D. Peña,et al.  Detecting nonlinearity in time series by model selection criteria , 2005 .

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

[19]  Andy J. Keane,et al.  Recent advances in surrogate-based optimization , 2009 .

[20]  Yi Wang,et al.  Monthly water quality forecasting and uncertainty assessment via bootstrapped wavelet neural networks under missing data for Harbin, China , 2013, Environmental Science and Pollution Research.

[21]  Susan A. Murphy,et al.  Monographs on statistics and applied probability , 1990 .

[22]  Eleni G. Farmaki,et al.  Artificial Neural Networks in water analysis: Theory and applications , 2010 .

[23]  Nitin K. Tripathi,et al.  An artificial neural network model for rainfall forecasting in Bangkok, Thailand , 2008 .

[24]  Guoqiang Peter Zhang,et al.  Time series forecasting using a hybrid ARIMA and neural network model , 2003, Neurocomputing.