Rainfall Forecasting Using Neural Network With Bee Colony Optimization

Abstract

The rainy season is only available in tropical climates. One of the problem that occurs during the rainy season is flooding. Hence forecasting is needed to predict rainfall. This research proposes a rainfall forecasting using backpropagation method with Bee colony optimization. One problem with the backpropagation method is the initialization of initial weights and random biases. Where the backpropagation weight value can be optimized using the bee colony algorithm. The initial weight can be optimized by the bee colony method. The bee colony is included in a swarm intelligence that can be used to optimize weight and bias form backpropagation method. Expected results from the backpropagation method could be improved with bee colony optimization. Results of the study shows that the learning rate of 0.3 MSE with 0.0111 results. From the previous results with the best learning rate of 0.3, then optimization of weight and bias with bee colony algorithm. Many foods sources and the number of bees as much as 3 MSE with 0.00939 results. Therefore, it is concluded that bee colony can optimize the weight and biases of backpropagation to get minimal errors with learning $\mathbf{rate} =3$ and number of $\mathbf{bees} =3$ .