Performance Improvement for Hotspot Prediction Model Using SBi-LSTM-XGBoost and SBi-GRU-XGBoost - Sukmana, Husni Teja; Aripiyanto, Saepul; Alamsyah, Aryajaya; Henry, Amir Acalapati; Nandaputra, Riandi (Society of Visual Informatics, 2025)
Forest fires damage ecosystems and harm all living beings, often triggered by low rainfall that worsens fire spread. Climatic factors such as the El Nino–Southern Oscillation (ENSO) also contribute to reduced rainfall and prolonged dry seasons. This study aims to enhance the performance of fire prediction models to support forest fire mitigation. Modified artificial neural network algorithms—Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) with bidirectional stacked layers—are employed as baseline models. An experimental approach was used to compare the performance of LSTM and GRU models with their ensemble versions, where XGBoost was added to improve prediction accuracy. The results show that the proposed ensemble algorithms significantly outperform the baseline models in multivariate fire prediction. The SBi-LSTM-XGBoost and SBi-GRU-XGBoost models demonstrated more than a 40% performance improvement compared to the original SBi-LSTM and SBi-GRU models. In multivariate modelling, the ensemble models achieved an R-value of 1.0000, with an average MAE of 0.0007, RMSE of 0.0009, and MAPE of 0.0008. This study also identified limitations of the LSTM and GRU models in processing ENSO data due to their non-linearity and weak correlation with hotspot data. As a contribution, our experiments show that integrating XGBoost into LSTM and GRU models effectively overcomes these limitations, significantly improving hotspot prediction accuracy and supporting better forest fire mitigation strategies.