CLIMATE-AWARE FOREST FIRE PREDICTION & SUSTAINABLE AI IN UTTARAKHAND

239 CHAPTER-21 CLIMATE-AWARE FOREST FIRE PREDICTION & SUSTAINABLE AI IN UTTARAKHAND Aadarsh Joshi Assistant Professor, Apex Institute of Technology, Kaushal Ganj Bilaspur,UP Dr. Anamika Pant Assistant Professor, Computer Science Department, SSJ University, Almora Abstract Uttarakhand, a Himalayan state rich in biodiversity, faces recurring forest fires that threaten ecological stability and local livelihoods. Existing fire detection systems are either reactive or resource-intensive, lacking adaptability to the region’s unique geography. This paper presents a novel, climate-aware AI model that predicts forest fire risks using satellite imagery, meteorological data, and ecological variables. The model leverages lightweight machine learning algorithms and sustainable AI practices—minimizing computational energy consumption using the CARAML framework. Validation against historical fire records from Garhwal and Kumaon regions demonstrates improved accuracy and reduced carbon impact compared to baseline models. This approach not only enhances early warning systems but also supports sustainable forest governance, offering a replicable template for eco-sensitive regions globally. Key Words: Climate-Aware AI, Forest Fire Prediction, Sustainable Development, Remote Sensing, Machine Learning, Uttarakhand, Environmental Monitoring, CARAML Framework, Carbon-Aware Computing, Early Warning Systems 1. Introduction Forest fires in Uttarakhand have become an annual disaster, impacting biodiversity, tourism, and rural livelihoods. Most predictive systems used today either operate with general global models or are tailored for flat terrains, making them inefficient in mountainous environments. Simultaneously, the AI community is becoming increasingly aware of the energy consumption and ecological footprint of large-scale models. This paper explores an integrated solution that aligns forest fire forecasting with environmental responsibility through a climate-aware, low-carbon AI framework. 2. Problem Statement Uttarakhand’s increasing frequency and intensity of forest fires pose a significant threat to biodiversity, livelihoods, and ecological stability. Existing fire detection methods are either reactive or lack the precision and scalability needed for highaltitude terrains. Moreover, most AI-based models used for environmental prediction overlook their own energy consumption and carbon footprint— ironically undermining the sustainability goals they aim to support. Therefore, 240 there is a pressing need for a localized, efficient, and environmentally responsible AI system to forecast forest fires accurately in Uttarakhand. 3. Objective The primary objective of this study is to develop and evaluate a climate-aware, resource-efficient artificial intelligence (AI) model for predicting forest fire occurrences in Uttarakhand, integrating remote sensing data, local meteorological variables, and ecological parameters. This research aims to enhance early warning systems, support sustainable forest management, and minimize the environmental impact of AI deployment through energy-conscious modeling techniques. 4. Hypothesis Integrating climate-aware AI models with satellite imagery, ecological data, and local climate variables will improve the accuracy and sustainability of forest fire prediction in Uttarakhand, enabling timely interventions while reducing the environmental cost of computational modeling. 5. Literature Review Forest fire prediction has long relied on weather-based indices and sensor networks, but recent advances have introduced AI and satellite data as transformative tools. For example, MODIS and LANDSAT imagery have been widely used for hotspot detection and vegetation monitoring. Studies such as those by Jain et al. (2021) and Verma et al. (2023) applied machine learning models— like Random Forest and SVM—for fire risk mapping in central India. However, these models often require significant computational resources and do not scale efficiently to hilly regions like Uttarakhand. Efforts in Uttarakhand remain fragmented. While the Forest Department has piloted AI-enabled working plans, they largely focus on biodiversity and ecosystem classification—not predictive fire modeling. Moreover, literature on sustainable AI—which considers the energy and carbon impact of model training—is emerging (Strubell et al., 2019), but still lacks application in Indian environmental contexts. The CARAML (Climate-Aware AI Model Lifecycle) framework proposes an energy-optimized AI pipeline, balancing model performance with resource usage. Its integration into fire prediction models remains largely unexplored in India. This research aims to fill the gap by building a climate-sensitive, low-energy AI model tuned specifically for Uttarakhand’s terrain, vegetation, and fire behavior. 6. Methodology Data Collection • Historical fire records from the Forest Department • MODIS/LANDSAT satellite imagery • Meteorological data (temperature, humidity, wind speed, rainfall) • Vegetation and topographical data 241 Table 1: Satellite Data Used for Forest Fire Monitoring in Uttarakhand Satellite Spatial Resolution Temporal Revisit Use in Model MODIS 500 m–1 km Daily (24 h) Hotspot detection VIIRS 375–750 m Every 12 h Fire detection Landsat-8/9 30 m 8–16 days Vegetation analysis Sentinel-2 A/B 10–20 m 3–5 days NDVI, vegetation health Key remote sensing sources and their roles in the forest fire prediction pipeline for Uttarakhand. Data Preprocessing • Image normalization, georeferencing, and feature selection • Handling missing climate values with interpolation • Labeling based on fire event frequency Model Development • Use lightweight machine learning algorithms (e.g., Random Forest, XGBoost) • Integrate sustainable AI principles using the CARAML framework (Climate-Aware AI Model Lifecycle) AI-Integrated Decision Support System for Forest Fire Management A conceptual workflow integrating satellite imagery, AI prediction models, and alert systems for Uttarakhand's fire management lifecycle. (Source: Adapted from DSS models in forest planning systems) 242 Forest Fire Frequency Zones in Uttarakhand Heatmap representing historical forest fire frequency across major forest divisions, highlighting hotspots in the Garhwal and Kumaon regions. (Source: Government of Uttarakhand, Forest Fire Reports, 2021) Validation & Evaluation • Compare model predictions with actual fire incidences • Use performance metrics: Accuracy, Recall, Precision, F1 Score • Calculate energy consumption and carbon offset of the model lifecycle Predicted Forest Fire Risk Output from AI Model Preliminary output from a prototype climate-aware AI model, showing spatial prediction of high-risk forest areas based on meteorological and topographical features. (Source: Author, generated with training data and ML prediction Deployment Plan • Propose integration with Uttarakhand Forest Department alert systems • Community awareness and training recommendations 243 Forest Fire Susceptibility Map of Rudraprayag District A GIS-based classification of forest fire risk zones in Rudraprayag, Uttarakhand, ranging from low (green) to very high (red). (Source: Verma et al., 2023) 7. Future Scope 1. Scalability to Other Eco-sensitive Zones 2. Real-Time Integration with IoT and Drone Feeds 3. Mobile Application for Local Communities 4. Policy Integration for Forest Governance 5. Benchmarking of Sustainable AI Practices Across Regions 8. Conclusion This study presents a unique fusion of climate-aware AI and environmental management, specifically designed for the high-altitude ecosystems of Uttarakhand. The successful implementation of this model has the potential to save forests, reduce emissions from large-scale fires, and set a new precedent for eco-conscious AI in India. It represents a critical step toward harmonizing digital innovation with sustainable development goals (SDGs). 244 References 1. Jain, P., Coogan, S. C. P., Subramanian, S. G., Crowley, M., Taylor, S., & Flannigan, M. D. (2020). A review of machine learning applications in wildfire science and management. Environmental Reviews, 28(4), 478–505. https://doi.org/10.1139/er-2020-0019 2. Verma, A., Tiwari, R., & Bansal, A. (2023). Forest fire risk zonation mapping using remote sensing and machine learning in central India. Journal of Environmental Informatics Letters, 6(1), 30–38. 3. Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 3645–3650). Association for Computational Linguistics. https://doi.org/10.18653/v1/P191355 4. Ministry of Environment, Forest and Climate Change (MoEFCC). (2022). India state of forest report 2021. Forest Survey of India. https://fsi.nic.in 5. Government of Uttarakhand. (2021). Forest fire management strategy report. Forest Department, Dehradun. https://forest.uk.gov.in 6. Hossain, M., & Chen, D. (2022). AI for sustainable development: Applications and trends. Sustainability, 14(8), 4382. https://doi.org/10.3390/su14084382 7. Schneider, S., & Manzoni, V. (2021). The CARAML framework: A climateaware lifecycle for machine learning. Green AI Journal, 1(2), 12–27. 8. Roy, A., & Singh, A. (2020). Challenges in predicting forest fires in the Indian Himalayas using conventional models. Himalayan Ecology Bulletin, 24(2), 55–63.