Lucas’s Presentation — Tackling Climate Change with Machine Learning
- The length of the hydrocarbon determines whether it is methane, kerosene, or gas
- C6 - C10 hydrocarbons, become crude oils
- A single barrel of oil 42 gallons turn into:
- 19 gallons of gasoline
- 11 gallons of diesel
- 7 gallons of other products (ink, crayons, dishwashing liquids)
- 4 gallons of highly refined gasoline
- Svante Arrhenius was the first to posit the greenhouse gas effect (as the quantity of carbonic acid increases, temperature will increase linearly)
- Earth re-reradiates heat as blackbody radiation, GHG particles catch that heat
- Carbon dioxide PPM is in cycles (peaks and valley in a year), peak is winter in northern hemisphere, dead trees are leaking our carbon, valley is in the middle of summer in the northern hemisphere
Positive and negative feedback loops:
- Negative feedback:
- Ocean absorbs CO2
- More plan growth in some areas
- Increase in dust, evaporation, volcanic activity
- Increase in volcanic activity is because as the earth warms, glaciers melt, causing change in geologic pressures
- Positive feedback loops:
- Poleward shift of forests
- Drying of peatlands and methanols bubbling in permafrost
- Decrease in biodiversity (biodiversity fixes carbon)
- Increase in forest temperatures
Main effects of climate change (affecting humans)
- Food instability, drought, hunger
- Increase in conflict following natural disasters
- Syrian war is a result of drought, something to follow up on
- Climate refugees
- Rightward shift in many governments of the world
- More extreme weather events
- Greater spread of disease vectors
Enter Machine Learning
Summarizing the paper
Tackling Climate Change with Machine Learning Useful Machine Learning Techniques
- Generative modeling: Statistical models that create simulated “observations” of real world phenomena
- Applications:
- Generate structural model of buildings with less carbon intense material
- Generate energy signatures of people to help model in data poor environments
- Dynamic price generation of grid prices to help optimize for lowered GHG emissions
- Applications:
- Personalization:
- ML grid price signals
- NLP has been used to extract plane ticket info and shopping receipts from email to quantify a person’s carbon impact
- Counterfactual AI has been used to create what-if scenarios
- Psychological research - Distance from climate change psychologically is a big determinant on someone’s climate change policies
- Digital Twin models
- Make a good representation of machinery in a computer base, and use that representation to do (and model) things
- Image ML
- Precision agriculture
- Scan for disease, yield, identify spots for fertilizer
- There is a digital revolution going on in agriculture going on right now. (Farmers tend to like flying drones)
- Identify and count species
- Scan satellite photos and identify good spots for solar panels or predict good wire siting (work in India used minimum spanning trees)
- Agriculture is fundamental to climate change: Ensuring that we have high yields is essential for making the most of the environmental consequences that are occurring
- Can also visualize the effects of flooding on homes, can change your perception of climate change
- Precision agriculture
- Natural language processing
- Venugopalan (2015) applied this to analyzing solar patent applications to build a general model of solar innovation
- Provide personalized recommendations for people who want to reduce their carbon footprints
- Analyze social media to understand discourse around climate change
- Automated identification and scoring of climate risks in company’s public disclosures
[went to make dinner, so don’t have much information from 4-2 unfortunately]
- Reinforcement Learning and Optimization
- Control the charging of EVs to help stabilize price grid
- Combinatorial ML for material discovery
- Basically search ML, search the space of materials experiments faster
uuid: 202005072038 date: May 7, 2020 tags: #raise #presentation