INTEGRATED CLIMATE INTELLIGENCE PLATFORM FOR MULTI HAZARD RISK & DISASTER FORECASTING

Natural disasters such as landslides, earthquakes, and tsunamis pose significant threats to human life, infrastructure, and the environment.Accurate and early prediction of these disasters remains a major challenge due to the complex and dynamic nature of environmental data.Traditional prediction methods often fail to analyze long-term temporal dependencies, leading to delayed or inaccurate warnings.To overcome these limitations, this project presents an Integrated Climate Intelligence System using Deep Learning techniques, specifically the Long Short-Term Memory (LSTM) model, for multi-hazard disaster prediction.The proposed system collects historical and environmental time-series data related to rainfall, seismic activity, soil conditions, and ocean parameters.The data is preprocessed and fed into an LSTM-based model to learn temporal patterns and predict potential disaster occurrences.The system classifies disaster risk levels into Low, Medium, and High, enabling early warning generation and informed decision-making.A Streamlit-based web interface is developed to provide user-friendly visualization, real-time prediction results, and secure access through a login module.The experimental results demonstrate that the proposed model achieves improved prediction accuracy and reliability compared to conventional approaches.This system can significantly enhance disaster preparedness, reduce response time, and support authorities in mitigating the impact of natural disasters.The project highlights the effectiveness of deep learning in climate intelligence and provides a scalable solution for future disaster management systems.