Statistical Downscaling of Rainfall in an Arid Coastal Region: A Radial Basis Function Neural Network Approach

2015 ◽  
Vol 735 ◽  
pp. 190-194 ◽  
Author(s):  
Kamal Ahmed ◽  
Shamsuddin Shahid ◽  
Sobri Harun
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Coastal Region ◽  
Circulation Model ◽  
Transformation Method ◽  
Monthly Rainfall ◽  
Power Transformation ◽  
Global Circulation Model ◽  
Radial Basis

Downscaling Global Circulation Model (GCM) output is important in order to understand the present climate as well as future climate changes at local scale. In this study, Radial basis function (RBF) neural network was used to downscale the mean monthly rainfall in an arid coastal region located in Baluchistan province of Pakistan. The RBF model was used to downscale monthly rainfall from National Center for environmental prediction (NCEP) reanalysis dataset at four observation stations in the area. The potential predictors were selected using principal component analysis of NCEP variables at grid points located around the study area. Power transformation method was used to remove the bias in the prediction. The results showed that the RBF model was able to establish a good relation between NCEP predictors and local rainfall. The power transformation method was also found to perform well to correct errors in prediction. It can be concluded that RBF and power transformation methods are reliable and effective methods for downscaling rainfall in an arid coastal region.

scholarly journals Monthly rainfall prediction based on artificial neural networks with backpropagation and radial basis function

2018 ◽  
Vol 4 (2) ◽  
pp. 154
Author(s):  
Ian Mochamad Sofian ◽  
Azhar Kholiq Affandi ◽  
Iskhaq Iskandar ◽  
Yosi Apriani
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Monthly Rainfall ◽  
Rainfall Data ◽  
Training Data ◽  
Rainfall Prediction ◽  
Radial Basis ◽  
Total Data ◽  
Artificial Neural

Two models of Artificial Neural Network (ANN) algorithm have been developed for monthly rainfall prediction, namely the Backpropagation Neural Network (BPNN) and Radial Basis Function Neural Network (RBFNN). A total data of 238 months (1994-2013) was used as the input data, in which 190 data were used as training data and 48 data used as testing data. Rainfall data has been tested using architecture BPNN with various learning rates. In addition, the rainfall data has been tested using the RBFNN architecture with maximum number of neurons K = 200, and various error goals. Statistical analysis has been conducted to calculate R, MSE, MBE, and MAE to verify the result. The study showed that RBFNN architecture with error goal of 0.001 gives the best result with a value of MSE = 0.00072 and R = 0.98 for the learning process, and MSE = 0.00092 and R = 0.86 for the testing process. Thus, the RBFNN can be set as the best model for monthly rainfall prediction.

Sign in / Sign up

Export Citation Format

Share Document