Enhancing Wildfire Detection with Real-Time Predictive Analytics using Extreme Learning Machine

Data shows that larger and more catastrophic fires will cause greater fatalities and widespread damage, posing a severe threat to human life, ecosystems, infrastructure, and the atmosphere. Having technologies in place for wildfire detection and real-time predictive analysis is crucial for reducing these hazards. Our wildfire detection system scans hundreds of cameras per minute to improve detection efficiency. It is powered by a cloud-based workflow that integrates DL based picture identification software. Using min-max normalisation to standardise brightness and contrast allows for better image comparison, while Gaussian filtering improves picture quality by eliminating noise. Furthermore, a technique for extracting important features from images that is based on segmentation might simplify image preparation. The algorithm uses a probabilistic ELM model to identify wildfires. A dependable method for early fire detection, the ELM-based predictive model obtained 97% accuracy. Reduce wildfire-related losses and fatalities through enhanced early detection and reaction times. This system optimises wildfire monitoring and forest management by merging cloud-based workflows, ML-powered image processing, and real-time predictive analysis.