Study of Climate Change in the Mandalika International Circuit Area Using Neural Network Backpropagation

Abstract

Climate change is a global phenomenon that also causes small-scale effects. This study aims to determine future climate changes in the Mandalika International Circuit area using the artificial neural network of the MATLAB GUI-based backpropagation method. The simulation stage used daily rainfall intensity data collected in the Mandalika International Circuit area from 2012-2021 (365 data). A preliminary analysis concluded that the Mandalika International Circuit area is dominated by a very wet climate according to the Schmidt-Ferguson classification, which occurred in 2012, 2013, 2017, and 2021. This study used two architectural models with two and three hidden layers. The TRAINRP training function and the LOGSIG activation function were utilized at each hidden layer. Between the two architectures, the better architecture was selected, namely the 100-50-10-1 (three hidden layers) that resulted in an accuracy rate of 99.90% and an MSE of 0.0412376 achieved in the 258th iteration. These results indicate that the area has a very wet climate with the highest rain intensity in March and the lowest in January. The results of this study show that the backpropagation method can be used to help formulate an alternative policy on the measures for handling and mitigating extreme climate change in upcoming periods, especially during international events at the Mandalika International Circuit area.