Harnessing Artificial Intelligence For Environmental Hazard Prediction: Models, Methodologies, And Real-World Applications
- IJLLR Journal
- Aug 10
- 1 min read
Sonali Debbarma, Research Scholar, Faculty of Law, ICFAI University, Tripura,
ABSTRACT
Floods, wildfires, hurricanes, earthquakes, and droughts are becoming more frequent and severe as a result of climate change, urbanization, and environmental degradation. These catastrophes pose serious threats to human lives, ecosystems, and economic systems. Traditional forecasting approaches, while valuable, frequently struggle with the nonlinear and dynamic nature of environmental systems. In recent years, Artificial Intelligence (AI) has emerged as a transformative tool in environmental hazard prediction, providing more accurate, adaptable, and real-time forecasting. This article examines how environmental forecasting systems can incorporate AI methods including machine learning, deep learning, and hybrid models. It investigates how artificial intelligence uses huge, complex datasets from satellite photography, remote sensing, meteorological sources, and real-time sensor networks to model and predict environmental dangers. The article demonstrates the versatility and usefulness of AI models by discussing numerous dangers in detail, such as flood forecasting using neural networks, wildfire detection using image recognition algorithms, and earthquake early warning systems based on pattern recognition.
Furthermore, the article examines real-world case studies and pilot initiatives from throughout the world, providing insight into both successful implementations and current issues. It also addresses crucial challenges such as data quality, model interpretability, infrastructure inequities, and ethical concerns in the use of AI for public safety.
This article emphasizes AI-based forecasting's expanding significance in catastrophe planning, policymaking, and long-term risk management by exploring both its potential and limitations. It promotes a collaborative, data- driven approach to creating more resilient communities in the face of rising environmental risks.
