Optimized IoT-Based Multimodal Fusion for Early Forest Fire Detection and Prediction
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Abstract
Forest and land fires are recurring ecological disasters that pose serious threats to environmental sustainability, particularly in vulnerable regions like Indonesia. Conventional fire detection methods using only visual or single-sensor data often suffer from low accuracy in poor lighting, thin smoke, or extreme weather. This study proposes an IoT-based multimodal system that combines visual imagery and real-time meteorological sensor data. Fire detection was conducted using the YOLOv11 model, trained for 50 epochs with the SGD optimizer. The model achieved a precision of 87.9%, recall of 79.7%, mAP@0.5 of 87.7%, and mAP@0.5:0.95 of 53.7%. Detected images are further classified using a hybrid ViT-GRU model, which achieves 99.97% accuracy by capturing spatial and temporal fire patterns. We performed fire detection using an LSTM model optimized with Optuna and SMOTE, yielding 92.66% accuracy and an AUC of 1.00. The decision-level fusion approach integrates visual and sensor outputs to improve the accuracy and contextual relevance of the nal prediction. We deployed the system in a real-time Streamlit dashboard connected to cloud-based data acquisition. Results show that this multimodal approach significantly improves the reliability of early re detection and risk prediction.
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