Combination of SOM-RBF for drought code prediction using rainfall and air temperature data
Open Access
- 18 November 2019
- journal article
- Published by Institute of Research and Community Services Diponegoro University (LPPM UNDIP) in Jurnal Teknologi dan Sistem Komputer
- Vol. 8 (1), 64-68
- https://doi.org/10.14710/jtsiskom.8.1.2020.64-68
Abstract
This study aims to predict Drought Code (DC) in Kabupaten Kubu Raya using a combination of SOM-RBF. The final weight value of SOM was used as a center on the RBF network. The input data variables are rainfall data and air temperature data for three days with three binary outputs to predict DC values. This study also observed the effect of the number of neurons, learning rates, and the number of iterations on the results of the SOM-RBF network training. The smallest MSE of training result from the SOM-RBF network was 0.159933 using 65 neurons in the hidden layer, learning rate 0.007, and epoch 45000. The detection accuracy of SOM-RBF was 91.34 % from 245 test data.Keywords
Funding Information
- Universitas Tanjungpura
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