Background

Automated optimization can be a valuable tool, but it should not replace an understanding of the software, modeling methods and assumptions, impacts from individual model parameters, and knowledge of the watershed. A modeler should not rely solely on an automated optimization tool, otherwise unrealistic parameter values or parameter combinations are possible. Understanding the runoff response for a watershed, parameter impacts, and minimum/maximum parameter ranges are required to successfully set up an optimization trial and critically evaluate results. An optimization routine relies on an objective function to evaluate how well the simulated model results meet the modeling goal, which is in general to accurately re-produce a historical event. Objective functions reduce the judgement of the model's performance to a single number. Different objective functions summarize the model performance in different ways, and emphasize different things, but no objective function can re-produce the judgment of an expert modeler.

An HEC-HMS optimization trial is a simulation type that can be used to identify reasonable model parameters which improve the ability of a model application to predict the precipitation runoff response. An optimization trial includes a basin model, meteorological model, and information about the simulation time window and time step. The trial also includes selections for the objective function, search method, and parameter adjustments. Optimization involves automated parameter adjustments that improves the objective function. The objective functions measure the goodness of fit between the simulated results and observed data. The search method is responsible making parameter adjustment as the trial searches for an optimal parameter set.

The Differential Evolution search method was added in HEC-HMS Version 4.9. This search method is more robust than the univariate gradient and simplex search methods. The Differential Evolution search has more parameter sets that span the parameter space and the search varies in a random method, instead of the deterministic approach the other search methods follow. It is more likely that the Differential Evolution search method will find a global optimum parameter set due to the increased number of parameter sets (referred to as samples within the population) and random search component.

In this tutorial, you will see different options for applying the Differential Evolution search method and how to evaluate results from the optimization trial.