automatic calibration of hec-hms model using multi-objective fuzzy optimal models

estimation of parameters of a hydrologic model is undertaken using a procedure called “calibration” in order to obtain predictions as close as possible to observed values. this study aimed to use the particle swarm optimization (pso) algorithm for automatic calibration of the hec-hms hydrologic model, which includes a library of different event-based models for simulating the rainfall-runoff process. since a flood hydrograph has different characteristics such as time to peak, peak discharge and total runoff volume, the calibration process is addressed using a single-objective or multi-objective optimization model. in this context, the fuzzy set theory can be used to combine different objective functions and convert the multi-objective model to a single-objective one. in this research, the tamar basin, a sub-basin of the golestan-dam basin in north of iran, was selected as the case study with four reliable measured flood events. the first three events were used for calibration and the fourth one for verification. as most of the models built in the hec-hms software were event-based, the concept of recalibration of parameters related to a basin initial condition was also introduced. the comparison of results obtained from the single and multi-objective scenarios showed the efficiency of the proposed hms-pso simulation-optimization approach in the multi-objective calibration of event-based hydrologic models.