AI-Based Fake Media Detection Using Machine Learning and Natural Language Processing

Abstract

Rapid industrialization, urbanization, and climate change have introduced complex environmental risks, including rising temperatures, sea level changes, and extreme weather events. Accurate prediction of these environmental factors is crucial for disaster preparedness, policy-making, and sustainable urban planning. This project proposes an Intelligent Environmental Risk Prediction System that leverages machine learning models to predict environmental hazards such as temperature fluctuations and earthquake risks. The system integrates multiple models including Random Forest, Support Vector Machine, Linear Regression, and K-Nearest Neighbors, offering ensemble comparison for improved reliability.The web-based platform is developed using Django, providing user-friendly registration, login, and prediction interfaces. Users input environmental parameters such as CO2 levels, sea level, humidity, wind speed, atmospheric pressure, deforestation rates, pollution index, and ocean temperature, and the system predicts the potential risk level. Data preprocessing and scaling ensure model accuracy and consistency. The Random Forest model serves as the primary prediction engine due to its high performance, while other models provide comparative analysis to validate predictions.The system includes robust user and admin management. Regular users can track their prediction history, while administrators can monitor user activities, view model performance metrics, and analyze datasets. The platform supports both global and regional (e.g., Indian cities) datasets, enabling localized and worldwide risk predictions. Historical predictions are stored for further analysis, and dynamic visualizations help users interpret prediction results.This framework not only improves prediction accuracy through multi-model evaluation but also emphasizes ease of access, real-time computation, and visual interpretability. Its modular architecture allows for future integration of deep learning models, real-time sensor data, and IoT connectivity. Overall, this system addresses the urgent need for accessible, accurate, and interpretable environmental risk prediction tools, enabling proactive environmental management and informed decision-making.