EcoGuard: Uniting IoT and AI to Secure Forests and Combat Climate Change in Real-Time

Forest fires, illegal logging, and climate change pose substantial threats to forest ecosystems, necessitating advanced monitoring and early warning systems. This research proposes an innovative approach that integrates the Internet of Things (IoT) and Artificial Intelligence (AI) for real-time monitoring and early detection of forest fires, illegal logging, and climate change indicators. The proposed system utilizes IoT-enabled sensor networks to gather real-time data on temperature, humidity, air quality, and sound, providing extensive coverage of forested areas. The collected data is processed using AI algorithms, including Deep Learning with Decision Trees and Time Series Analysis (DL-DT-TSA), to identify patterns and anomalies associated with forest fire risks, illegal logging incidents, and climate change indicators. Through extensive simulation analysis, the DL-DT-TSA algorithm is compared with existing algorithms, such as Support Vector Machine (SVM) and Artificial Neural Network (ANN), using evaluation metrics like accuracy, precision, and recall. The results demonstrate that the DL-DT-TSA algorithm outperforms the other algorithms, showcasing its effectiveness in detecting and alerting relevant authorities and stakeholders about potential threats in real-time. By integrating IoT and AI technologies, this research contributes to the existing knowledge in monitoring and mitigating forest fire risks, combating illegal logging, and addressing climate change challenges. The proposed system's proactive nature enables prompt intervention, enhancing environmental conservation efforts and supporting policymakers, environmental agencies, and researchers in implementing preventive measures. The insights gained from this study can guide the development of robust monitoring and early warning systems to safeguard forests, mitigate illegal activities, and adapt to the changing climate.