Leveraging Machine Learning to Reduce Reliance on Physics-Based Climate Modeling
To replicate flood simulation outputs, ClimateIQ uses a machine learning (ML) approach. For example, an ML model for flood predictions uses spatial features representing city morphology (e.g. elevation) and temporal features describing rainfall patterns to predict flood height at two meter resolution given a pattern of rainfall (aggregated up to 10m on the ClimateIQ dashboard). Our powerful and unique approach allows the ML model to learn from both spatial and temporal features.
Data
Open Data
We are actively working with our City partners and other stakeholders to obtain local municipal and regional datasets to further train and validate our model.
Datasets used include:
Building Footprint Data
Land Cover/Land Use Data
Digital Elevation Model (DEM)
Soil Data
Meteorological Data
