Pioneering Real-Time Forest Fire Detection: A Comprehensive Examination of Advanced Machine Learning Techniques in IoT-Integrated Systems for Enhanced Environmental Adaptability

The purpose of this research is to revolutionize forest fire detection by leveraging the integration of Internet of Things (IoT) technology, specifically sensors and Raspberry Pi, and advanced machine learning algorithms like AutoML. Aiming to address the need for adaptable systems to various environmental conditions, the primary objectives of this study include developing real-time prediction algorithms, employing multi-sensor data acquisition, and implementing intelligent response mechanisms to enhance accuracy, scalability, and responsiveness. The originality of this work lies in the novel combination of advanced computational models with real-time sensor data, setting a new paradigm in environmental protection and forest management. This paper begins with an in-depth analysis of existing IoT-based wildfire detection systems, particularly focusing on their machine learning components, an aspect often overlooked in the literature. Based on this analysis, a framework for a real-time forest fire detection system is proposed, integrating robust IoT infrastructure, including sensors and Raspberry Pi, with sophisticated machine learning techniques such as AutoML. Rigorous evaluation reveals the framework’s outstanding performance in terms of detection accuracy, computational efficiency, and adaptability to various environmental conditions. The results firmly indicate that the proposed system, which leverages the power of AutoML and the flexibility of IoT devices, represents a superior and innovative solution to existing methods, thus contributing significantly to the field of environmental protection and forest management.