https://github.com/invcble/disaster-risk-analysis-platform
Natural Disaster Analysis Website using Deep Learning & Poisson Distribution
https://github.com/invcble/disaster-risk-analysis-platform
autoencoder-neural-network aws-ec2 deep-learning flask-application web-application
Last synced: 3 months ago
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Natural Disaster Analysis Website using Deep Learning & Poisson Distribution
- Host: GitHub
- URL: https://github.com/invcble/disaster-risk-analysis-platform
- Owner: invcble
- Created: 2024-04-21T07:07:56.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-04-23T08:12:16.000Z (about 1 year ago)
- Last Synced: 2024-12-26T00:26:25.714Z (4 months ago)
- Topics: autoencoder-neural-network, aws-ec2, deep-learning, flask-application, web-application
- Language: Jupyter Notebook
- Homepage:
- Size: 595 KB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Disaster Risk Analytics Platform
## About the Project
Welcome to our project, "Disaster Risk Analytics Platform," developed with an aim to provide comprehensive disaster risk analysis for investment firms operating within the U.S. markets. Using advanced statistical analysis and deep learning technologies, this platform helps predict potential disasters and their financial impacts on various states, aiding firms in making informed, resilient investment decisions.
## Live Demo
The web application is currently hosted and can be accessed http://imonbera13.pythonanywhere.com/.
## Features
- **Statistical Modeling:** Utilizes Poisson's distribution to accurately model the frequency of disaster events.
- **Deep Learning Integration:** Features an autoencoder for efficient data encoding and a custom deep learning-based regression model ("Regression Net") for financial damage estimation.
- **User-Friendly Interface:** A Flask web application with an intuitive map interface allows users to interact with and visualize risk data effectively.
- **Scalable Deployment:** Hosted on AWS EC2 to ensure scalable and reliable access.
## Built With
- **Flask**: For backend framework.
- **HTML/CSS/JavaScript**: For creating a responsive and interactive front-end.
- **Machine Learning & Deep Learning**: Powers the core analytics of the platform.
- **Autoencoders**: Used for data encoding and dimensionality reduction.
- **RegressionNet**: A custom deep learning model for estimating financial impacts.
## Data Sources
This project utilizes the comprehensive disaster dataset provided by [EM-DAT](https://www.emdat.be/), which was instrumental in developing our predictive models.
## Codefest Participation
This project was developed for the Philly Codefest. More information about the event can be found at [event.phillycodefest.com](https://event.phillycodefest.com/).
## Getting Started
To get a local copy up and running, follow these simple steps.
### Prerequisites
- Python 3.x
- npm (Node package manager)
### Installation
1. Clone the repo
```sh
git clone https://github.com/invcble/Disaster-Risk-Analysis-Platform
```