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[D] How much may this RNN for scalable-ecosystem-regeneration-design cost?

Problem 1: Lots of places suck… they’re simultaneously losing ecology, soil, habitability, jobs, profitability and carbon. We’ve got effective, profitable methodologies which could greatly improve many of these ecosystems, yet our scarce allocation of restoration resources, time, and willpower compels project designs with higher IRL effectiveness per effort.

Problem 2: Disaster-response-ecological-restoration benefits immensely from rapid analysis and planing yet designers are booked, expensive and slow.

Solution: An AI tool which triages options and creates effective ecology restoration designs and plans, quickly.

My Questions: About how much time and money may it take ML professionals to build and train a working version of this AI that’s worth having regenerative design professionals use as a tool?

What should it be called?

Is this a realistic candidate project for a paid programming challenge?

What are your thoughts?

Here’s more details.

Example of Hypothetical AI Results: Perhaps after training, the AI identifies some part of the Mojave Desert in Southern California as being the best place in the world to restore. It suggests we buy this barren 300 acre parcel of land called “Rancho Desertification AF” that has problems with flashfloods, less-and-less ecology and supports zero jobs. It suggests that we then make 400 yards of compost from local rice straw and food waste that’s inoculated with indigenous microorganisms and biochar, that we use equipment to dig large amounts of big swales on contour and that we spread the compost, compost tea, native grass seeds and trees in the swales. That we drip irrigate the trees temporarily while we wait for rain which it forecasts to be in the late summer monsoon season. That the swales will recharge the groundwater in the expected flashflood allowing the grasses and trees to grow, creating hundreds of acres of relative oasis. That we’d do planned holistic grazing the following year with cattle (using short duration, high intensity stocking that’s dynamically modified by herders) who would eat the grass creating more compost, better microorganisms and more mulch. That this would create lots of jobs, profit, biodiversity, bio-productivity and we’d have the option of selling the ranch at 300-1000% profit in 1.5-3 years to a holistic planned grazing rancher or to continue ranching it ourselves. It estimates this project would reduce GHG emissions by say 3,000 tons of carbon annually, which may also be worth $20,000 on the Nori carbon removal marketplace. That this project would provide jobs to 20 people for initial construction and 10 people for herding, climate-solution tourism and eco-tourism.

Ecosystem Training/Evaluation Data Types: Topographical, LiDAR, Forestry, Ecology, Climate, Rainfall, Wind, Geology, Soil Chemistry, Soil Microbiology, Local Industries, Local Population, Local Economics, Local Politics, Local and Global Funding Available, NORI carbon removal marketplace, Grants Available, Tax Breaks Available, Labor Available, Comet-Farm, Current Real Estate Prices, Real Estate Projections, Stuff I haven’t figured out yet

Methodology Training/Evaluation Data Types: Data from software like Granular which farmers who get paid per ton of carbon sequestered use. Organic farming ERP data. Data used by the authors of high impact journal articles on agroecology, conservation, ecological restoration, and organic agriculture. Data used by environmental agencies, and large conservation NGOs. Experimental data generated in-house as needed. Stuff I haven’t figured out yet

Draft Program Training Parameters (these are how the AI would compare location and restoration design options): Increase Biodiversity, Increase Bioproductivity, Increase Human Wellness, Increase Ecosystem and Human Resilience, Improve Groundwater Levels, Reduce Disaster Risks, Stabilize Rainfall, Improve Downwind Clouds, Lower Cost, Lower Negative Externalities, Lower Greenhouse Gas Emissions, Reduce Endangered Species Losses, Lower Risk of Implementation Failure or Unintended Consequences, Increase Scalability, Increase Profitability, Decrease Implementation Time etc.

Thank you!

submitted by /u/Pal_Ol_Buddy
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.