Rainfall Forecasting Using Backpropagation Neural Network - Sihananto, Andreas Nugroho; Mahmudy, Wayan Firdaus (Journal of Information Technology and Computer Science, 2017)
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;Â  0,01686; 0,01934; 0,03196} by using 50 maximum epoch and 3 neurons on hidden layer.