Application of Artificial Neural Network in Predicting Direct Economic Losses Due to Earthquake - Ulil Azmi; Soehardjoepri Soehardjoepri; Rudi Prihandoko; Iqra Asif (Jurnal Varian, 2023)
Accurately predicting the direct economic losses caused by earthquakes is important for policy makers for disaster budgets. Before a disaster strikes, it is important to consider the public policy costs associated with disaster relief and recovery. The aim of this study is to provide a risk assessment approach, which can benefit all parties involved. Artificial neural networks are widely used for time series forecasting, especially financial forecasting. Therefore, this study proposes a cutting-edge forecasting method such as backpropagation neural network (BPNN) and other prediction methods: neural network autoregressive (NNAR) and ARIMA-GARCH to obtain the best prediction results. This paper applies interpolation data to increase the amount of data used. Two interpolations were applied to amplify the original small sample with virtual points, namely cubic splines and further piecewise interpolation using. The results of this study are the cubic spline interpolation is the most effective way to solve the small sampling problem to predict direct economic losses due to the Indonesian earthquake and the BPNN method outperforms other traditional methods with an RMSE of 0.024 in the training period and 0.174 in the testing period, significantly lower than other methods. The results of this research can be used as reference material for the government in estimating the level of earthquake losses and can be used to develop risk reduction strategies.