PyGMTSAR (Python InSAR): Powerful and Accessible Satellite Interferometry

Repository for model of models development.

Tableau dashboard on natural disasters

Machine learning based natural disaster prediction for US counties

The objective of the project is to predict whether a particular tweet, of which the text (occasionally the keyword and the location as well) is provided, indicates a real disaster or not. We use various NLP techniques and classification models for this purpose and objectively compare these models by means of appropriate evaluation metric.

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

A webapp that displays natural disasters on an interactive map. Built with Mapbox and LeafletJS.

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

Exploring deaths by natural disasters, and possible relations to climate change.

A Django application to archive real-time earthquake notifications from the USGS's Advanced National Seismic System

Modular structure that feeds on instant data, warns about natural disasters and conveys what needs to be done

Data visualization of death toll by natural disasters - written in Python3 using tkinter (GUI) and matplotlib (Visualization) .

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

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

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

A visualisation of natural disasters, pandemics, and extreme weather events in python over periods of time and locations.

Carbon Dioxide and Disasters

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

An open service to connect those that need emergency housing, with the people who can provide it.