The accuracy of rainfall runoff modeling plays a crucial role in the
simulation and evaluation of hydrological processes. Numerous physical
parameters need to be determined when modeling using SWMM. Automatic
calibration helps to achieve improved model accuracy. However, the SWMM
model does not have automatic way of calibration. For this purpose, this
study is dedicated to combining the NSGA II optimization algorithm with
pyswmm to achieve automatic parameter calibration for the swmm model.
The swmm model was calibrated and validated using rainfall and runoff
measurements from a monitoring catchment located in Taipei City, Taiwan.
The results of the study show that the calibrated parameters help to
improve the accuracy of the SWMM model quickly compared to relying on
manual determination. The relative error of the peak flow rate is lower
than 10%. However, the baseflow has a significant impact on the
validation of the results. Based on the current research results, it is
expected that the proposed method will be exerted on other similar
environmental modeling problems.