Artificial Neural Network Model for Forecasting Natural Disasters: Polak-Ribiere and Powell-Beale Comparison

Abstract

The prediction problem is an interesting topic to be discussed today. The many predictive methods used to solve problems have become an obstacle for researchers and academics alike. This study aimed to analyze the ability of the ANN prediction method using the Polak-Ribiere and Powell-Beale conjugate gradients. The dataset used for the analysis is disaster times-series data in Indonesia for the last ten years (2011-2020). Data obtained from the Indonesian Disaster Geoportal sourced from the National Disaster Management Agency can be seen on the infographic menu on the website https://gis.bnpb.go.id/. The results obtained based on the analysis that has been carried out, that the 4-10-1 architectural model with the Powell-Beale Conjugate gradient method can produce lower MSE Testing/Performance than the Polak-Ribiere Conjugate gradient method, another advantage is faster time. And fewer iterations. So it can be concluded that based on comparing these two methods, the Conjugate gradient Powell-Beale method with the architectural model 4-10-1 can be used for forecasting/predicting natural disasters because it is a better method.