Natural calamities such as hurricanes, earthquakes, floods, and wildfires have devastating effects on human lives, infrastructure, and the environment. Timely prediction and effective mitigation strategies are crucial in minimizing the impact of these disasters. My proposed solution, CalamityAssist, aims to be a one-stop solution for all disaster related assistance, in terms of ML based prediction of a calamity, and providing quick user query resolution through NLP based systems. These queries can cover a wide range of categories from possible mitigation efforts, and relief measures for a calamity to the emergency contact details in various countries and latest news. The solution leverages the power of AI to be able to make predictions about the occurrence of natural calamities and to address a wide range of queries related to natural calamities and disaster management in general.
Project Structure:
- Models
(Jupyter Notebooks)
- queryResolution.ipynb
- tsunamiPrediction.ipynb
- hurricanePrediction.ipynb
- Datasets
- query.csv (for NLP model)
- answers.csv (for NLP model)
- atlantic.csv (for hurricane prediction model)
- earthquake_data.csv (for tsunami prediction model)
Detailed doc - https://drive.google.com/file/d/1BaWFdnCRhkor0vBLvQSnyiDAMhC78ArS/view?usp=sharing