How Improving Aviation uses Gemini and Google Cloud to predict and combat wildfires
By Google Cloud Tech
Key Concepts:
- Wildfire modeling
- Ember-caused spot fires
- Fire behavior determinants (topography, fuel models, weather)
- Rothermel equation
- Atmospheric boundary layers
- Machine learning for ignition probability
- Google Cloud (Cloud Run, Kubernetes, BigQuery, Vertex AI, Gemini)
- Real-time data analysis
- Computational power for predictions
- AI for natural disaster prediction
1. The Problem: Limitations of Traditional Wildfire Models
- Traditional wildfire models, some dating back to the 1970s, are effective at predicting fire spread on the ground but struggle with ember-caused spot fires.
- These models often rely on the Rothermel equation, which uses empirical evidence to predict surface fire spread.
- Factors like atmospheric boundary layers, winds, and ember transport are difficult to predict, yet crucial in rapidly evolving wildfires.
- Rocio Frej notes her past research lacked real-world impact until she focused on improving wildfire prediction.
2. Embers and Spot Fires: A Major Challenge
- Embers are red-hot pieces of material lifted by the fire that can start new spot fires.
- Jake Rosenzweig emphasizes the danger of embers drifting from house to house.
3. Factors Influencing Fire Behavior
- Fire behavior is determined by three main components:
- Topography: Slope, elevation, and aspect of the landscape.
- Fuel Models: Types and amount of nearby vegetation.
- Weather: Wind, precipitation, humidity, and temperature.
4. Solution: Leveraging Machine Learning and Google Cloud
- The team is using machine learning to estimate the probability of ignition once embers land, considering weather and topographical conditions.
- Google Cloud is used to overcome computational limitations and deliver real-time predictions to firefighters in the field.
- Before Google Cloud, the code was thousands of lines long and ran on a single computer.
- Alex Rodriguez highlights the ease of using Google Cloud products like Cloud Run and Kubernetes for rapid deployment and visualization.
5. Google Cloud Technologies in Detail
- Gemini: Used to scour the web for historical fire data, collecting variables of interest into a database.
- BigQuery: Processes the collected data.
- Vertex AI: Used for post-analysis and training the machine learning models to improve efficiency and speed.
- Google Kubernetes Engine: Enables running thousands of instances on Google Cloud, providing near real-time predictions to users in the field via a browser-based device.
6. Real-Time Data and Prediction Pipeline
- Rocio Frej describes a pipeline of real-time data coming from drones, sensors, phones, and satellites, feeding into prediction models.
- This pipeline enables predictions in seconds.
7. Impact and Future Applications
- Kevin Speer emphasizes that this work is at the forefront of wildland fire modeling, combining traditional models with new computational techniques.
- The result is a rapid and useful risk assessment for users.
- Rocio Frej believes AI empowers smaller companies to challenge the status quo and build impactful solutions.
- The technology can potentially be expanded to predict other natural disasters.
8. Notable Quotes
- Rocio Frej: "I have been in research my whole life, and I have never done something that really had an impact."
- Kevin Speer: "The work here is at the forefront of wildland fire modeling by bringing together the traditional models with new computational techniques and putting it together in a package that produces, for a user, a rapid and useful assessment of risk."
- Rocio Frej: "AI gives companies like us, smaller companies, the opportunity to break the status quo and build something amazing."
9. Technical Terms
- Rothermel Equation: A mathematical model used to predict the rate of spread of a surface fire.
- Atmospheric Boundary Layer: The lowest part of the atmosphere, directly influenced by the Earth's surface.
- Fuel Models: Classifications of vegetation types and their characteristics that influence fire behavior.
- Machine Learning: A type of artificial intelligence that allows computer systems to learn from data without being explicitly programmed.
- Cloud Run: A managed compute platform that enables you to run stateless containers via web requests or Pub/Sub events.
- Kubernetes: An open-source container orchestration system for automating application deployment, scaling, and management.
- BigQuery: A fully-managed, serverless data warehouse that enables scalable analysis over petabytes of data.
- Vertex AI: A unified machine learning platform that allows you to build, deploy, and scale ML models.
- Gemini: A family of multimodal large language models developed by Google AI.
10. Synthesis/Conclusion
The video highlights the limitations of traditional wildfire models in predicting ember-caused spot fires and presents a solution leveraging machine learning and Google Cloud technologies. By integrating real-time data from various sources and utilizing the computational power of the cloud, the team has developed a system that provides near real-time predictions to firefighters in the field, enabling more effective response and mitigation efforts. The project demonstrates the potential of AI to revolutionize natural disaster prediction and response.
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