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

IJAIN (International Journal of Advances in Intelligent Informatics)

Yosi Apriani¹  Ian Mochamad Sofian²  Azhar Kholiq Affandi²  Iskhaq Iskandar² 

[1] Universitas Muhammadiyah Palembang;University of Sriwijaya;

关键词: Prediction;    Rainfall;    BPNN;    RBFNN;   

DOI  :  10.26555/ijain.v4i2.208

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown