Forest fires are among the most destructive natural disasters, causing significant environmental damage, biodiversity loss, economic disruption, and threats to human life. Conventional fire monitoring techniques, such as watchtowers, satellite imaging, and manual patrols, often suffer from delayed detection, limited coverage, and high operational costs. This paper proposes an Edge-AI Enabled Real-Time Forest Fire Detection and Early Warning Framework using the YOLOv8 object detection model and Internet of Things (IoT) technologies. The proposed framework performs real-time fire and smoke detection directly on edge devices, reducing detection latency, bandwidth consumption, and dependence on cloud connectivity, making it suitable for remote forest environments. Environmental data collected from IoT sensors, including temperature, humidity, smoke concentration, carbon monoxide (CO), and air quality, are integrated through a sensor fusion mechanism to validate visual detections and minimize false alarms. The framework comprises five layers: Data Acquisition, Edge AI Processing, IoT Sensing, Cloud Communication, and Emergency Alert Generation. Detection results, GPS location, confidence score, timestamp, and sensor readings are transmitted to a cloud dashboard, where automated alerts are delivered to forest authorities via SMS, email, mobile applications, or web platforms. The proposed framework provides low-latency processing, improved detection accuracy, reliable early warning, and scalable deployment for intelligent wildfire monitoring. Future work includes integrating thermal imaging, Vision Transformers, Explainable AI, federated learning, autonomous drones, and Digital Twin technology to further enhance wildfire prediction and disaster management.
Shashikala T. K., S. R, J. Chandrashekhara· Advanced International Journ...· 0 citations
Weather forecasting is an essential element of modern society which is crucial for agricultural planning, disaster
management, transport and logistic networks, aviation, power generation, water resources management, and environmental
monitoring. The problem of weather prediction is exceptionally challenging since it involves the spatio-temporal behavior of the
atmosphere which is subject to complex physical processes while constantly changing in time. Conventional statistical
forecasting models and machine learning methods including Artificial Neural Networks (ANN), Recurrent Neural Networks
(RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU) are effective in short-term forecasting but fail to
account for long-range spatial and temporal weather patterns accurately. The historical weather data used in this research was
obtained from public databases. The data consists of temperature, humidity, pressure, rainfall, wind speed, wind direction, solar
radiation, and cloud cover. It undergoes numerous preprocessing steps before being fed into the developed framework as an
input. Extracted features from the processed data are then encoded using an attention-based multi-head encoder to forecast the
weather while being used to perform climate analytics such as identifying climate trends, seasonality, and climate anomalies
detection. The evaluation results of the proposed framework on the forecasting performance using the Mean Absolute Error
(MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination
($R^2$) are expected to demonstrate greater forecasting accuracy, more effective capturing of long-range spatial and temporal
patterns, lower computational complexity, and faster processing speeds compared to conventional deep learning approaches. The
framework contributes significantly to long-range climate forecasting while facilitating informed decision-making in disaster
management, precision agriculture, smart grids, and environmental monitoring. The proposed solution, therefore, presents a
novel and effective approach to weather prediction and climate analytics using the Spatio-Temporal Transformer framework.
S. R, Shashikala T K, J. Chandrashekhara· International Journal for Re...· 0 citations
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