Rainfall Forecasting Using Backpropagation Neural Network


  • Andreas Nugroho Sihananto Faculty of Computer Science, Universitas Brawijaya
  • Wayan Firdaus Mahmudy Universitas Brawijaya




Rainfall already became vital observation object because it affects society life both in rural areas or urban areas. Because parameters to predict rainfall rates is very complex, using physics based model that need many parameters is not a good choice. Using alternative approach like time-series based model is a good alternative. One of the algorithm that widely used to predict future events is Neural Network Backpropagation. On this research we will use Nguyen-Widrow method to initialize weight of Neural Network to reduce training time. The lowest MSE achieved is {0,02815;  0,01686; 0,01934; 0,03196} by using 50 maximum epoch and 3 neurons on hidden layer.

Author Biographies

Andreas Nugroho Sihananto, Faculty of Computer Science, Universitas Brawijaya

Master Student

Faculty of Computer Science 

Universitas Brawijaya

Wayan Firdaus Mahmudy, Universitas Brawijaya

Faculty of Computer Science


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How to Cite

Sihananto, A. N., & Mahmudy, W. F. (2017). Rainfall Forecasting Using Backpropagation Neural Network. Journal of Information Technology and Computer Science, 2(2). https://doi.org/10.25126/jitecs.2017229