BAYESIAN NEURAL NETWORK RAINFALL MODELLING: A CASE STUDY IN EAST JAVA

Abstract

Rainfall is an important parameter in meteorology and hydrology, and it measures the amount of rain that falls from the atmosphere to the ground surface in liquid form. However, in the process of measuring rainfall, changes in the rainfall cycle sometimes occur due to climate change, global warming, and other factors. Therefore, this research aims to model daily rainfall using the Bayesian Neural Network (BNN) approach, combining the Bayesian Method and Artificial Neural Network (ANN). ANN is suitable for rainfall models that have intermittent characteristics. Meanwhile, the Bayesian method provides advantages in producing model parameter inferences that provide uncertainty measurements in predictions. BNN is expected to deliver better daily rainfall predictions than ANN. This research used daily rainfall data in East Jawa, and the results show that the Bayesian Neural Network produces better rainfall predictions when describing rainfall in East Java. These predictions will be very useful for the government and the people of East Java province to prevent flooding. Also, with rainfall predictions, people will know more about what crops should be planted during the rains.