There is a well-known Sabermetric tool called Similarity Score which has the intent of the intent of discovering who the most similar historical players are to a certain player in baseball and basketball. The scale goes from 0 to 1000 and is used to evaluate current players compared to Hall of Fame players. We attempt to construct a similar scale for SWMM models based on the process flags, length of simulation, number of various elements and output statistical tools based on the renowned missive Rules for Responsible Modeling from CHI. The goal is to classify a new model based on a database of scrubbed existing models for the purpose of classification in Machine Learning. The similarly score will be a "classifier" sometimes also refers to the mathematical function, implemented by a classification algorithm, that maps input data to a category. The categories will be groups such as single event, continuous, hydraulics only, complex hydrology etc. The output will be based on the System output Graphs of various models.
PS Bill James is also the creator of the Baseball Similarity Score
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