Integration GSTARIMA with deep neural network to enhance prediction accuracy on rainfall data

Abstract

This study aimed to improve rainfall prediction accuracy by integrating spatio-temporal Generalized Autoregressive Integrated Moving Average (GSTARIMA) with Deep Neural Network (DNN) techniques. Accurate rainfall forecasting is vital for regions like West Java, which face risks of flooding and landslides during heavy rains in December, January and February. The research focused on enhancing monthly rainfall prediction using GSTARIMA model residuals, transformed from daily data, with a spatial resolution of 0.5° × 0.625°. The GSTARIMA-DNN model was developed to reduce prediction errors and closely align with actual rainfall values. The DNN architecture utilized a Multilayer Perceptron (MLP) with eight input nodes for spatio-temporal residuals, two hidden layers with Leaky ReLU (LReLU) activation functions and a linear output layer for regression. The integration of GSTARIMA and DNN significantly improved prediction accuracy compared to traditional methods. Specifically, the GSTARIMA(0,1,1)-DNN model achieved a 7.28% Mean Absolute Percentage Error (MAPE) at four key locations (Tasikmalaya, Cirebon, Majalengka and Garut) among ten locations with the same lag order. This hybrid model effectively managed complex temporal and spatial data, underscoring the benefits of combining spatio-temporal models with DNN for enhanced rainfall forecasting. This approach offers valuable insights for advancing precise spatio-temporal models in various research fields.

Keywords

• 62M45
• 68T07
• 62h12
• Spatio-temporal integration
• GSTARIMA-DNN model
• MAPE accuracy