Just finished work on my second patent, one for a machine-learning based framework capable of quantifying the effectiveness of adaptive change in infrastructural systems. This is a tricky problem because as hazards get more severe, investments in infrastructural systems can appear to be less effective than they actually are.
The trick is to build a weather-related impact model that is also trained on various technical aspects of the infrastructure. If the training data is good enough, the model will learn how these aspects influence the reliability of the system, and then you can use that model to test out various configurations of infrastructure.
We’re hoping that this new approach will be useful for power utilities, regulators, and others to optimize how they plan and adapt their systems to climate change.