CALCULATING RISK SCORE ASSOCIATED WITH A PHYSICAL STRUCTURE FOR A NATURAL DISASTER PERIL USING HAZARD AND VULNERABILITY MODELS

Examples described herein include methods and computing systems which may include examples of calculating risk scores for certain natural disasters perils based on machine learning model outputs. For example, a machine learning model may weight each of the pixels of a map in accordance with the set of weights associated with a structure, to calculate a risk score for a particular natural disaster peril associated with that structure. A plurality of risk selections may be provided to a user computing device for selection by a user, with those risk selections being associated with that risk score. Advantageously, the computing system facilitates the interaction of datasets with different measurement parameters in a machine learning model. In normalizing datasets before providing the datasets to input nodes of a machine learning model, a computing system may efficiently provide hazard and vulnerability outputs of the machine learning model.