The PyDisaster project uses a master function that extracts GPS location and EXIF data from photos submitted to our Flask application platform, and opens a Google Maps search of the location. This project is geared towards helping FEMA conduct damage assessments in areas effected by natural disasters.

It contains codes and documentations within the scope of the Special Topics in Remote Sensing course

Repository to preview, describe, and link to Tableau dashboard.

Using Observable to make data visualizations

Fractal alert is a repository for developing an effective warning system that would predict and prevent the impact of natural disasters such as earthquake, hurricanes, floods, etc.

Graduate deep learning course projects: Physics-informed neural networks for simulating physical systems and CNN-based wildfire prediction models.

ResQ is a socially responsible Flutter Application. It assists the flood rescue operations.

Tableau dashboard on natural disasters

A collection of weather, natural disaster, and US Census data processed and ranked to find the ideal home location for each individual's preferences.

Registro de desaparecidos, proyecto que es una solución a los desastres naturales en Bolivia, es un sistema que trabaja persistencia con archivos, estructura de datos y programación orientada a objetos. Además, contiene una interfaz gráfica para interactuar con el registro el cual está hecho en JavaFx

A Google Trends Sentiment Analysis integrated with FEMA emergency declarations data. I attempt to correlate isolated natural disasters with a change in search patterns for climate change. Emphasis on FL hurricanes and CA wildfires over the past 5 years.

Web scrape data on numerous biological hazards from numerous sources (EMDAT, IDMC, IFRC, DesInventar)

Machine learning based natural disaster prediction for US counties

Jupyter notebooks and web app that uses statistical analysis to prove climate change is real!

Carbon Dioxide and Disasters

Jupyter Notebook showing my solutions to questions related to sample Category 5 Atlantic Hurricane Data. The data covers the years from 1928 to 2018.

A community-powered GIS solution to help make navigating floods safer, together.

[WIP] Get smart insights, alerts & warnings about predicted natural disasters and take precautions before they arrive to keep family, friends & yourself safe.

Jupyter Notebook for representing the data of world-natural-disaster events

Student research paper on precaution and prevention of natural disasters