Easily create new projects on any place, any size, at the resolution of your choice.

Just name your new project, select the model to use (in this example, the L-GrID flooding model), and set a budget to work with for solution-building.

Then, enter a location anywhere in the world, set the resolution you need, and drag the cursor across the area you want to model. 

Now you have a landscape to work on and simulate!

Modify the landscape you created.

Fora.ai assigns landscape data automatically (e.g., ground cover, elevation), but you might need to correct or change them.

Click in the changes and you’re ready!

Show how the impacts you’re modeling matter to you.

All participants have their unique “concern profile.”

Each user can set their priorities by dragging and dropping the model outputs (concerns) from highest to lowest (e.g. for flooding, this might include property damage, flooded streets, and runoff downstream).

You can also modify concern weights by entering their numbers and the ranking will adjust automatically.

The concern profiles are used to visualize the impacts of each solution by what people care about (see below under “compare simulation results…”).

Collaboratively add intervention tokens on your project map to create a solution you want to simulate.

Everyone in a team will meet in the collaborative map and work together to build solutions (in this example, green infrastructure to deal with flooding).

The sidebar on the left shows the different intervention tokens (e.g., green infrastructure types).

Click on the token type you want to place and then click on the map to add it there. Toggle data layers to inform your choices. If you made a mistake, you can delete it!

Continue creating a solution together until everyone is ready to try it out in a simulation.

Explore the simulations

The video on the left shows how the solution works for this place (e.g., how flooding reduces flooding).

Impacts for each concern are summarized on the right relative to no intervention.

Make informed decisions by comparing different solutions across metrics and concerns.

Each simulation will show how that solution works relative to a starting point (without any interventions).

Score bars show how each solution performs for each user.

As people learn together, solutions get better across metrics (bars get longer) and for everyone (bars get more even across teammates).