Jul 2026· International Scientific Journal of Engineering and Management· Vol 05, pp. 1-7· 0 citations
TL;DR
An integrated AI-powered forest fire detection and prediction system that combines a ground-based wireless sensor network for early smoke and thermal anomaly detection, a deep learning-based fire risk prediction model that forecasts fire probability up to 72 hours in advance using meteorological and vegetation moisture data, and a real-time alert dissemination pipeline for forest department response coordination is presented.
Abstract
Forest fires represent one of the most ecologically destructive and economically costly natural hazards, with global wildfire activity destroying an estimated 350 to 450 million hectares of vegetation annually and contributing significantly to greenhouse gas emissions, biodiversity loss, and air quality degradation. In India, the Forest Survey of India recorded over 2.23 lakh forest fire alert points during the 2023 fire season alone, with the Eastern Ghats, Western Ghats, and Central Indian forest belts being recurrently affected. Conventional forest fire monitoring relies predominantly on satellite-based thermal anomaly detection, which suffers from coarse temporal resolution (revisit intervals of 1–12 hours depending on satellite), cloud cover interference, and an inherent detection lag that allows fires to spread substantially before alerts reach ground response teams. This paper presents an integrated AI-powered forest fire detection and prediction system that combines a ground-based wireless sensor network for early smoke and thermal anomaly detection, a deep learning-based fire risk prediction model that forecasts fire probability up to 72 hours in advance using meteorological and vegetation moisture data, and a real-time alert dissemination pipeline for forest department response coordination. The detection module employs a lightweight CNN trained on multi-spectral sensor fusion data (temperature, humidity, CO concentration, and particulate matter) deployed across a wireless mesh network, achieving fire event detection within 4 minutes of ignition with 95.8% accuracy. The prediction module uses a Random Forest-LSTM ensemble trained on 12 years of Forest Survey of India fire occurrence records combined with IMD meteorological data, achieving fire risk forecasting accuracy of 89.4% for the 72-hour prediction window. Field-calibrated simulation results based on deployment parameters from the Sathyamangalam Tiger Reserve forest range in Tamil Nadu demonstrate that the proposed system reduces fire detection-to-alert latency by 91% compared to satellite-only monitoring and provides actionable early warning that extends the intervention window for forest department fire suppression teams by an average of 6.4 hours.
Keywords — Forest Fire Detection, Wildfire Prediction, Wireless Sensor Network, CNN, LSTM, Random Forest, IoT, Fire Risk Index, Remote Sensing, Environmental Monitoring
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
Forest fires pose a serious threat to ecologically sound forests and environmental protection Also, loss of life and considerable natural individual properties, adding hundreds of houses and thousands of hectares of wilderness 80% of fire-related costs could have been avoided if the fire had been discovered sooner. Forest fires become worse and can be detected and forecast with IoT-based NodeMCU. The study is an honest attempt to comprehend the current system, studying various factors affecting forest fires. We tried to make it smarter with thehelp of IoT technology by linking the entire monitoring process to a cloud server. It has been utilized to identify forestfires in extensive surveys. The goal of this research is to look into the losses and damage caused by wildfires, both natural and human-induced and fire detection techniques. The major goal of this research is to anticipate how a firewill progress by monitoring temperature, humidity, and other factors.
Dinesh H. Burnade, Kaushal Jain, Bhavani Sutar· Journal of Science & Tec...· 3 citations
: Forest fires pose a recurring and severe threat to forest ecosystems, biodiversity, and human communities across India, particularly during the dry season. Traditional methods for predicting and monitoring forest fires are often constrained by their reliance on manual observations and limited historical datasets, leading to sub optimal accuracy and untimely interventions. Leveraging recent advances in remote sensing and data science, this study develops a comprehensive machine learning framework for the prediction of forest fires in India using state-wise fire detection data from the SNPP-VIIRS satellite, supplemented with environmental and meteorological features. Multiple regression, classification, and time series forecasting models were implemented to estimate fire counts, categorize states by risk, extract key predictors, and anticipate future fire trends. Experimental results indicate that ensemble machine learning methods, specifically Gradient Boosting and Random Forest, significantly outperform traditional linear approaches, achieving high predictive accuracy (R² = 0.91 for regression; 91% accuracy for classification). Seasonal analysis revealed that meteorological variables, notably temperature and rainfall, are dominant drivers of fire risk, with fire incidents peaking during the pre-monsoon period. The study's findings provide interpretable, actionable insights for policymakers and resource managers, enabling targeted early warning systems and improved allocation of fire prevention resources. This framework demonstrates the potential of integrating satellite data and machine learning to advance forest fire prediction and supports sustainable risk mitigation strategies across the Indian landscape.
Tabish Rao, Atika Gupta, Divya Kapil et al.· Proceedings of the 1st Inter...· 0 citations
Forest fires are a major environmental hazard, and early prediction and detection are essential for reducing damage. To address the limitations of traditional forest surveillance in Andhra Pradesh, this project presents FireGuard AI, a hybrid deep learning system that combines historical weather data with live visual detection. The proposed Hybrid Multi-Fire Model unifies a fine-tuned ResNet50 for real-time fire and smoke detection from webcam images and a BiLSTM network for district-level fire-risk prediction using weather and land-condition data from 2015–2025. The system is implemented using Flask and provides risk visualization, interactive district maps, model performance metrics, and automated Twilio SMS alerts for high-risk conditions. By combining predictive weather analysis with real-time image detection, FireGuard AI provides an integrated approach to early forest-fire warning and monitoring.
Keywords
Forest Fire Prediction, Real-Time Fire Detection, Deep Learning, ResNet50, BiLSTM, Hybrid Model, Weather Data, Computer Vision, Andhra Pradesh, FireGuard AI, Early Warning System, Flask
N. Chandrika, G. Sujatha· International Journal of Cre...· 0 citations
Forest fires are among the most destructive natural hazards, posing significant threats to ecosystems, infrastructure, and human life. In Mediterranean regions such as Algeria, the frequency and intensity of wildfires have increased due to climate change and human activities. This article proposes a cloud-centric hybrid Internet of Things (IoT) and machine learning (ML) framework for intelligent forest fire monitoring and prevention. The proposed system integrates distributed IoT sensor nodes equipped with temperature and humidity sensors that continuously collect environmental data and transmit them to a central gateway through NRF24L01 communication modules, while long-range communication with the cloud platform is achieved using a SIM808 cellular module. To enhance predictive capabilities, the framework combines real-time IoT sensing data with complementary meteorological variables, including wind speed and rainfall, obtained from external meteorological services during dataset construction. Six supervised ML models—logistic regression, decision tree, random forest, XGBoost, LightGBM, and CatBoost—were evaluated using historical Algerian wildfire data (2000–2003) together with a recent dataset collected in 2024. Experimental results show that XGBoost achieved the highest overall predictive performance with a test accuracy of 98.75% and an F1-score of 98.97%, while Random Forest and CatBoost also demonstrated robust and stable performance. Logistic Regression achieved competitive results with significantly lower computational cost, making it suitable for resource-constrained IoT environments. The proposed hybrid IoT–ML framework enables early wildfire risk assessment and supports proactive decision-making for forest management. These findings demonstrate the potential of integrating IoT sensing, meteorological information, and machine learning to support sustainable environmental monitoring within ambient intelligence systems.
Ali Kourtiche, Souad Belhia, Mahmoud Fahsi et al.· Journal of Ambient Intellige...· 0 citations
Forest fires are an increasing environmental and financial risk and require intelligent and rapid fire detection mechanisms. In this paper, an AI-based IoT system was proposed, which combines thermal, visual, and meteorological information to identify a forest fire at its early-stage development and activate a response based on UAV. The presented system involves a hybrid deep ensemble framework, namely, DeViW-FNet that consists of Swin Transformers, weather models based on BiLSTMs, and multimodal co-attention fusion to determine anomalous fire patterns. The new uncommon methods, such as the Cross-Domain Calibration, the Federated Dynamic Time Warping Autoencoders, and the Quantum-Inspired Edge Ensemble Voting, have a significant impact on the system and enhance its strength in the extreme and dubious environment. Experiments on a wide range of environmental conditions such as fog, smoke, low light, etc. reveal that the detection performance is high with 94.5 mean visual detection accuracy, 95.1 weather-based classification accuracy, and 90 plus anomaly detection F1-score. Swarm reinforcement learning is applied to ensure the response latency of UAV is minimized to a level that the accuracy of the navigation was 93.5%. The study also presents the promise of cross-modal AI fusion in real-time fire detection in complicated environments. The suggested framework is scalable, low-latency, and accommodating to the changes in the environment, which would be applicable to forests in large scale. Some improvements that can be made in the future are thermal drone vision, explainable AI modules, and compatibility with satellite-based wildfire propagation simulators.
K. Venkateshwaran, V. K., K. Chandrasekaran et al.· 2026 7th International Confe...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
