This repository contains the source code of paper: "Semantic-based End-to-End Learning for Typhoon Intensity Prediction"

A Bidirectional LSTM model to classify whether a given tweet talks about a real disaster or not. This was my project in "CSC 522: Automated Learning and Data Analysis" course at NC State University.

Our project aims to help people come up with solutions to cope up with disasters before, during and after the disaster. We incorporate internet-less chatting, skin detection, fundraising, disaster prediction, live ground status and drones to deliver amenities into our project.

An approach to solve the Kaggle Competition, Natural Language Processing with Disaster Tweets

A disaster prediction web application aided with AI to provide user with information on any upcoming natural disasters noted

AI-powered disaster prediction system for Libya using machine learning models to predict floods, storms, and other natural disasters

An advanced AI-powered disaster prediction platform utilizing a weighted fusion of LSTM, Prophet, and Random Forest models. Implements a high-accuracy ensemble mechanism for global hazard forecasting as presented in Research Paper : "AI-Powered Disaster Prediction System: A Comprehen sive Approach to Mitigating Natural Disasters"

Computer vision system that analyzes geological formations from satellite/drone imagery to identify mineral deposits and predict natural disasters.

A comprehensive, scientific-grade Python application for earthquake risk assessment that integrates geological, tectonic, and volcanic analysis using advanced machine learning techniques.

This project aims to develop a robust, scalable system for predicting various disasters using real-time data from IoT sensors and machine learning algorithms. It will feature a user-friendly interface for disseminating alerts and managing responses effectively.

A real-time AI disaster risk prediction platform that analyzes weather, soil, elevation, and seismic data to forecast Heatwaves, Floods, and Landslides using ML models and FastAPI backend.

Real-time multi-hazard disaster risk prediction system using machine learning to assess flood and heatwave risks based on meteorological and hydrological indicators.

AI Disaster Prediction System - AI & IoT Integration project in AI / ML / GenAI for CSEProjects360 final year project catalog.

Analytics-based monitoring system that aggregates environmental data and historical disaster records to track risks and generate predictive insights for events like floods and earthquakes, enabling early detection and informed decision-making.

A Streamlit dashboard for visualizing global natural disaster data with ML-based flood, earthquake, and wildfire prediction models.

A website that users can use to detect if their uploaded image is disasterous or not using CNN.

Deep learning system for climate change analysis using satellite imagery and weather data. Predicts natural disasters, monitors deforestation, tracks glacier melting, and analyzes urban heat islands.

This project involves building a machine learning model to classify tweets as disaster-related or not, using a dataset of 10,000 hand-labeled tweets to assist in real-time emergency detection.

Smart Disaster Predictor: Streamlit app predicting flood and earthquake risks with real-time AWS SNS SMS alerts. Deployed on AWS EC2 with Load Balancer.

An autonomous multimodal AI framework for disaster prediction, satellite flood segmentation, physics-informed validation, ensemble fusion, and adaptive monitoring across earthquake, cyclone, wildfire, and flood events.