High-Level Project Summary
Climate change is unarguably the most imminent, most pressing issue of our generation. Years of "handling it later" and pushbacks have only exacerbated consequences of climate change such as hurricanes, tornadoes, and drought. Although the majority of U.S. citizens experience climate change through mass media, places such as California, Louisiana, Arizona, and many more are losing their livelihoods to wildfires, rising sea levels, and drought. Thus, in our support of the Climate Change bill, we develop this solution to visualize drought risk globally as well as to provide a model to predict drought risk on crowdsourced data.
Link to Project "Demo"
Link to Final Project
Detailed Project Description
Aestus
Aestus offers an innovative platform allowing scientists to analyze drought risk throughout the world. Our solution features 3 distinct layers visualizing levels of humidity, pressure, and precipitation. As our visualization covers areas all around globe, researchers can analyze drought risk of any location on our planet. In addition to our Deck.gl webmap, we utilized Tensorflow 2.0 to deliver a state-of-the-art artificial neural network (ANN) to predict drought risk when given crowdsourced data. Our model takes in measurements of humidity, temperature, pressure, as well as wind speed to predict drought risk based on the GPCC Drought Index. Through our predictive model and webmap, we hope to deliver essential tools to combat the ongoing climate crisis.
Technologies
We utilized Next.js for our front-end user-interface, Deck.gl for our visualization, and Tensorflow for our artificial neural network.
Live Demo
Space Agency Data
Hackathon Journey
What inspired our team to choose this challenge was the ever-increasing news articles and mentions of the devastations of climate change on the news. Every summer, each of us can feel the weather getting unbearably warm, and it is even more concerning to think about the devastating consequences of climate change in the near future. We approached this challenge by procuring data from NASA Earth Database and visualized it on our custom webapp. We then realized that we lack a predictive model, and decided to make an accessible cloud-based Tensorflow model.
References
Beaudoing, H. and M. Rodell, NASA/GSFC/HSL (2020), GLDAS Noah Land Surface Model L4 monthly 0.25 x 0.25 degree V2.1, Greenbelt, Maryland, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: [2021-10-03], 10.5067/SXAVCZFAQLNO
Rodell, M., P.R. Houser, U. Jambor, J. Gottschalck, K. Mitchell, C. Meng, K. Arsenault, B. Cosgrove, J. Radakovich, M. Bosilovich, J.K. Entin, J.P. Walker, D. Lohmann, and D. Toll, 2004: The Global Land Data Assimilation System, Bull. Amer. Meteor. Soc., 85, 381-394, doi:10.1175/BAMS-85-3-381
GISTEMP Team, 2021: GISS Surface Temperature Analysis (GISTEMP), version 4. NASA Goddard Institute for Space Studies. Dataset accessed 2021-10-03 at https://data.giss.nasa.gov/gistemp/.
Lenssen, N., G. Schmidt, J. Hansen, M. Menne, A. Persin, R. Ruedy, and D. Zyss, 2019: Improvements in the GISTEMP uncertainty model. J. Geophys. Res. Atmos., 124, no. 12, 6307-6326, doi:10.1029/2018JD029522.
Fan et al., 2008, A global monthly land surface air temperature analysis for 1948 - present, Journal of Geophysical Research, Vol. 113, p. D01103, DOI:10.1029/2007JD008470
McKee et al., 1993, The Relationship of Drought Frequency and Duration to Time Scales, Eighth Conference on Applied Climatology
Pietzsch et al., 2011, A modified drought index for WMO RA VI, Adv. in Science and Research, Vol. 6, p. 275-279, DOI:10.5194/asr-6-275-2011
Thornthwaite, 1948, An approach towards a rational classification of climate, Geographical Review, Vol. 38, p. 55-94
Vicente-Serrano et al., 2009, A Multiscalar Drought Index Sensitive to Global Warming: The Standardized Precipitation Evapotranspiration Index, Journal of Climate, Vol. 23, p. 1696-1718, DOI:10.1175/2009JCLI2909.1
Tags
#data #deck.gl #machinelearning #visualization
Global Judging
This project has been submitted for consideration during the Judging process.