Development of a Multimodal Artificial Intelligence Framework for Forest Fire Prediction

Abstract

This study presents a Multimodal Artificial Intelligence Framework (MAIF) that integrates real-time sensor data and visual imagery to enhance forest fire detection accuracy, responsiveness, and reliability in remote environments. The system combines ensemble classification models such as Random Forest, SVM, KNN, XGBoost and Gradient Boosting with YOLOv8-based image recognition to detect fire risk patterns and visual indicators such as smoke and flame contours. A custom-built Forest Fire Capturing Device (FFCD), equipped with an ESP32 microcontroller and LoRaWiFi, was deployed in Omo forest, Nigeria, to collect heterogeneous environmental data. Visual inputs from ground cameras and drones were fused with sensor-based predictions to minimize false positives and improve generalization. The base classifiers showed performances of 0.98, 0.96, 0.93, 0.98, 0.98 for RF, SVM, KNN, XGBoost and GB, respectively with heterogeneous sensor datasets of 10,334 rows and 13 columns while meta-classifier and YOLOv8 module both achieved 0.98 accuracy, with significantly lower false positive rates compared to single-modality systems. Upon confirmed detection, the system automatically dispatched timestamped fire images via email, enabling rapid situational awareness and emergency response coordination.