عنوان مقاله [English]
Runoff in urban areas causes flooding of roads. This issue always has many problems with itself. Identifying areas prone to urban floods and flood-prone areas can greatly assist in planning to prevent and control possible floods. In this study, with using SWMM model, Shahrekord surface runoff collection network was simulated in 2, 5 and 10 year return periods. Model calibration was performed on 2 rainfall events on runoff depth parameter in several canal and random nodes. Sensitivity analysis was performed on the parameters affecting the total runoff of the catchment, and the equivalent width parameter was identified as the most sensitive parameter of the catchment. After calibration, validation was performed with optimum values in 2 other rainfall events. NSE, RMSE, and BIAS% coefficients were used to determine the modeling error in the calibration and validation steps that for example, the values of the coefficient of NSE obtained more than 0.8 in calibration and more than 0.9 in validation. These results showed that the simulation has a good accuracy. Results of SWMM model showed that surface runoff collection network is not sufficient for passing surface runoff during different return periods and the sub catchments 20, 90, 25, 39 and 99 have the highest amount of runoff, respectively. The results of TOPSIS method also showed that the most critical sub catchments are 92, 20, 25, 39 and 90, respectively. Most of these sub catchments are located in the southern part of the city. Due to the high density of residential and commercial areas and the lack of green enough space, the percentage of impermeable areas has been developed and as a result, the production of runoff has been increased. Comparison of SWMM model and TOPSIS method results shows 80% compliance in the selection of critical sub catchments. Therefore, using multi-criteria decision making algorithms such as TOPSIS can increase the accuracy of SWMM model in selecting and prioritizing of the critical sub catchments. As a result, using this approach improves the decision making process in critical times.