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H2O.ai Wildfire & Bushfire Challenge

Join our challenge to build wildfire & bushfire prediction models.

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Wildfires (a.k.a. bushfires) are a serious problem that threaten lives, communities, wildlife, and forests every year, with global climate change, it is getting worse. They are a global issue and are considered one of the most dangerous disasters we face. While humans cause many fires, other factors, including wind, lightning, drought, and landscape, impact where fires occur and how they spread.

Wildfires present unique and severe forecasting challenges. Compared to storms, such as hurricanes, wildfires are ambiguous and hard to predict, especially when you start looking at large, intense wildfires. Those fires combine complex weather, different landscapes, fuel sources such as housing materials or dry forests, and more.

The H2O.ai Fights Fire Challenge aims to provide first responders, local leaders, businesses, and the public with new AI applications that can be used to help save lives and property. We expect the participants and teams to build for one of these audiences, but we want to make sure you have the creative freedom to decide which one to design for, as that will lead to a greater breadth of new applications being built.

 

At the end of this challenge, teams will submit open source AI applications that help in some way with the following:

1. Predicting the behavior of wildfires

 

2. Predicting where wildfires will start

 

3. Reducing the loss of life and property from wildfires

 

We expect solutions to be directly applicable to numerous organizations that are working on these problems today, and we’ll continue to share how the submissions are being used to prevent and contain wildfires.

Leaderboard After Final Submission

Rank   Team Total Score
1 Too Hot Encoder 89.5
2 HTB 88.4
3 Gauss 81.3
4 Titans 73.2
5 A-Team 70.2

Leaderboard After First Submission

Rank   Team Total Score
1 Too Hot Encoder 74
2 Ateam 56
3 Gauss 50
4 HTB 41
5 PSR 35
6 Ffire 33
7 Akvelon 26
8 Titans 24
9 Sapere 21
10 Deepmind 16

Introducing H2O.ai Wildfire and Bushfire Challenge

Watch for a deep dive discussion to learn from H2O.ai Grandmasters.

Challenge Details

Team Size and Registration

Individuals and teams of up-to 5 participants can compete. 
All participants must register individually, and the registration form is located on this page.

Already Joined the Challenge?

If you and your team have already joined the H2O.ai Wildfire Prediction Challenge, please sign in here

Key Dates and Submissions

The challenge has started and there will be two submissions for the challenge.

Challenge Start Date – November 4th

Submission Start Date – November 8th

First submission – December 15th, 11:59 PM UTC

Final submission – February 25th, 11:59 PM UTC

Participants must submit their AI application in the first submission to participate in the final.

We expect to have our judging completed for the first submissions by the end of February.  Due to the extended judging timeline, we have changed our final submission timeline from Jan 15th to Feb 15th, 11:59 PM UTC.

The following elements are required for the AI application submission. All elements will be part of a single .zip file submission.

  • Written page describing the user of the application, the problem the application is solving, and the methodology used to make the AI model or models. This should be a simple readme.md, text, or .doc file.

  • Training Notebook

  • Core AI application

If teams make their AI apps using H2O Wave, they will be able to test their application within the competition platform.

Judges Panel

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Ali Tohidi

About Ali
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Ali Tohidi | About Ali

Dr. Tohidi is a fire and fluid dynamicist. Currently, he is an Assistant Professor of Mechanical Engineering and a Co-PI of the newly established NSF-IUCRC Wildfire Interdisciplinary Research Center (WIRC) at San Jose State University (SJSU). His research and development interests are at the nexus of experimental, data-driven, and mathematical modeling of complex systems across a wide range of spatiotemporal scales, in particular fire behavior and its effects on different ecosystems. His current efforts are focused on understanding the physics of wildfire spread and developing new models to better describe fire behavior throughout the landscape. Before joining SJSU, Dr. Tohidi worked as a Data Scientist in Silicon Valley startups to provide data-driven and data-centric solutions to some of the challenges in different verticals.

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Celina Lee

About Celina
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Celina Lee | About Celina

Celina Lee is the co-founder CEO of Zindi an online platform that hosts the largest data science community in Africa. Celina has a passion for unleashing the power of data for social good. Celina played central roles in launching global platforms including the Alliance for Financial Inclusioninsight2impact, and now Zindi. Celina's work in Data for Good has touched on various areas including financial inclusion, micro and small enterprise development, gender, climate change, and public health. She has lived and worked in Asia, Latin America, and Africa. Celina holds a Bachelor’s of Science in Applied Mathematics and a Master’s of International Affairs, both from Columbia University in New York.

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Dan Jermyn

About Dan
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Dan Jermyn | About Dan

Dan Jermyn is an experienced leader in both technology and analytics, with an established record of building award-winning, global teams in digital, big data and customer decisioning. Dan joined the Commonwealth Bank of Australia in 2017, where he has responsibility for ensuring they harness specialisms in AI data science, experimentation, analytics, technology and data to drive decisions that help improve the financial wellbeing of CBA’s customers and communities.

After gaining master’s degrees from both Oxford University and Cardiff University, Dan first plied his trade as a strategy consultant and then Head of Analytics for an agency in the UK, where he led engagements with a host of major corporate and governmental organizations. He followed this up by co-founding a successful digital technology startup. Dan joined CBA from the Royal Bank of Scotland, where he held various roles including Head of Digital Analytics and Head of Big Data & Innovation.

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Julian Forero

About Julian
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Julian Forero | About Julian

Julian is a product marketer with experience in both high-growth startups and established enterprise software and infrastructure providers. Prior to Snowflake, Julian was part of Confluent during the fast-growth journey to IPO. He began his career at IBM where as a consultant, he advised large enterprises on their machine learning and analytics strategy. Julian holds an aerospace engineering undergraduate degree from Georgia Tech and an MBA from The University of Chicago. During his free time, Julian enjoys road biking around the San Francisco bay area.

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Kaggle Grandmasters

About Kaggle Grandmasters
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Kaggle Grandmasters | About Kaggle Grandmasters

A selection of H2O.ai Kaggle Grandmasters will be part of the judges panel.

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Mario Miguel Valero

About Mario
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Mario Miguel Valero | About Mario

Dr. Mario Miguel Valero is an Assistant Professor at San Jose State University (San Jose, CA, USA). He is a research member of the Wildfire Interdisciplinary Research Center (WIRC), the only Industry-University Cooperative Research Center devoted to wildfire interdisciplinary research in the United States. Supported by the National Science Foundation, WIRC promotes efficient collaboration between public and private stakeholders to develop the next generation of wildfire management tools. Dr. Valero’s research focuses on the characterization of wildfire behavior using remote sensing techniques and the improvement of existing fire models from a data-driven perspective. Before joining SJSU, Dr. Valero worked as a data scientist in the AI industry in the San Francisco Bay Area.

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Marta Yebra

About Marta
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Marta Yebra | About Marta

Marta Yebra is an Assoiciate Professor with the Fenner School of Environment and Society and the Research School of Engineering. Her research focuses on using remote sensing data to monitor, quantify and forecast natural resources, natural hazards, and landscape function and health at local, regional, and global scales. This has included large multidisciplinary projects that integrated the main fire risk factors, and analysed fire risk trends, considering potential changes in socio-economic factors as well as foreseen impacts of global climate change. She has served on several advisory government bodies including the Australian Space Agency’s Bushfire Earth Observation Taskforce (Feb-May 2020), Australian Space Agency’s Earth Observation Technical Advisory Group (Since 2019) and the Victorian Department of Environment, Land, Water and Planning’s Scientific Reference Panel (Since 2019) and ACT Bushfire Council (Since 2021). Dr Yebra has been awarded several awards for her contributions to bushfire management, including the Bushfire and Natural Hazards CRC's Outstanding Achievement in Research Utilization award in 2019 and the Academy of Science Max Day award (2017).

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Sri Ambati

About Sri
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Sri Ambati | About Sri

Sri Ambati is the founder and CEO of H2O.ai. A product visionary who has assembled world-class teams throughout his career, Sri founded H2O.ai in 2012 with a mission to democratize AI for anyone, anywhere, creating a movement of the world’s top data scientists, physicists, academics and technologists at more than 20,000 organizations worldwide. Sri also regularly partners with global business leaders to fund AI projects designed to solve compelling business, environmental and societal challenges. Most recently, Sri led the initiative o2forindia.org, sourcing O2 concentrators for more than 200 public health organizations in Tier 2 cities and rural communities in India during the Delta wave of the COVID-19 pandemic, helping to save thousands of lives. His strong “AI for Good” ethos for the responsible and fair use of AI to make the world a better place drives H2O.ai’s business model and corporate direction.

A sought-after speaker and thought-leader, Sri has presented at industry events including Ai4, Money2020, Red Hat Summit and more, is a frequent university guest speaker, and has been featured in publications including The Wall Street JournalCNBCIDG and the World Economic Forum and has been named a Datanami Person to Watch.

Before founding H2O.ai, Sri co-founded Platfora, a big data analytics company (acquired by Workday) and was director of engineering at DataStax and Azul Systems. His academic background includes sabbaticals focused on Theoretical Neuroscience at Stanford University and U.C. Berkeley, and he holds a master’s degree in Math and Computer Science from the University of Memphis. Sri is inspired by his two daughters and genius.

Scoring Detail

Our judges have different expertise. Some are experts on AI and some will be end users of the application. The judges will collaborate on each submission to determine the overall score.

Each submission will be scored within 1-2 weeks.

After the submissions are scored, the top 10 will be posted on the Wildfire challenge webpage.

Clarity (10 pts)

Did the author present a clear thread of questions or themes motivating their AI app?

Did the author document why/what/how a set of methods was chosen and used for their analysis?

Is the training notebook documented in a way that is easily reproducible (e.g., code, additional data sources, citations)?

Accuracy (10 pts)

Did the author process the data (e.g., merging) and/or additional data sources accurately?

Is the methodology used in the analysis appropriate and reasonable?

Are the interpretations based on the analysis and visualization reasonable and convincing?

Creativity (15 pts)

Is the App something new that presents the data in a way that is addressing the challenge?

Does the notebook help the reader learn something new or challenge the reader to think in a new way?

Does the training notebook leverage novel methods and/or visualizations that help reveal insights from data and/or communicate findings?

Did you use or create alternate data sets?

Explainability (15 pts)

Is it simple to understand how the model works?

Can non-technical individuals understand the model and provide insights based on domain expertise?

Usability (50 pts)

Is the application easily usable for the audience it is meant to serve?

Would the customer actually use and improve upon the application after the challenge?

How useful would the application be in accomplishing its designed goal?

Prize Detail

We’ve structured our Wildfire Challenge to be a global collaborative effort to do good for the world that we share.

We want teams to submit their ideas and applications freely, knowing that other teams will learn from what they’ve done to improve their AI application. We believe this is in the spirit of progress and innovative freedom.

Based on this open philosophy, we have multiple awards for competing teams, and we will award teams for participation and each application submission.

First submission $1.5k for each of the top 10 submissions

Final submission $3k for each of the top 5 submissions

Swag packages for all participants who submit

Getting Started

Step 1 – Create your Team and Register

Register for the Wildfire Challenge using the form below. Talk to your friends and coworkers to create teams. Within the competition platform, you’ll be able to create and join teams.

Step 2 – Choose your Audience and Problem

Do some research and pick the audience you want to focus on:

  • First Responders (e.g. Firefighters or Firefighting Command Centers)

  • Local Leaders (e.g. Governors and Mayors)

  • Businesses (e.g. Local Retailers or Large Global Enterprises)

  • The General Public (e.g. you, friends, community)

Continue your research and select the problem you want to solve for your audience. Set requirements for your application.

Step 3 – Check Out the AI Starter Kit

The AI Starter Kit is located within the competition platform. The kit contains all of the elements of an application submission with the text document, training notebook, and full application code. You do not need to use the starter app, but it could be used to accelerate your solution.

Step 4 – Make your Solution

Work toward the first submission date, December 15th, and deliver your solution.

Partners

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