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

(1) * Ian Mochamad Sofian Mail (University of Sriwijaya, Indonesia)
(2) Azhar Kholiq Affandi Mail (University of Sriwijaya, Indonesia)
(3) Iskhaq Iskandar Mail (University of Sriwijaya, Indonesia)
(4) Yosi Apriani Mail (Universitas Muhammadiyah Palembang, Indonesia)
*corresponding author


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 results. 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.



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International Journal of Advances in Intelligent Informatics
ISSN 2442-6571  (print) | 2548-3161 (online)
Organized by Informatics Department - Universitas Ahmad Dahlan , and UTM Big Data Centre - Universiti Teknologi Malaysia
Published by Universitas Ahmad Dahlan
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This work is licensed under a Creative Commons Attribution-ShareAlike 4.0