A new approach for sensitivity analysis of the StormWater Management Model applied in an airport

Abstract

Sensitivity analysis of urban flood model parameters is important for efficient and accurate flood simulation. In order to explore the problems of large sampling parameters and nonlinear correlation between input and output variables, this paper proposed a new correlation analysis approach. The type, strength, and the order of sensitive parameters to the four outputs are analyzed using the proposed approach. The results show that the R values of Manning-N are biggest, its distribution is linear in heat maps, and the Manning-N has a strong linear correlation with Average Depth, Hour of Maximum Flooding, and Time to Peak. For Average Depth, the second sensitive parameter is Conductivity. For Hour of Maximum Flooding, the second and third more sensitive parameters are Conductivity and N-perv; however, there are certain nonlinear correlations from heat maps. For Total Inflow, the R values of each parameter are between 0.021 and 0.534. Most sensitive parameters are none; however, the more sensitive parameters are Conductivity, N-perv, and initial deficit. For Time to Peak, the second and third more sensitive parameters are N-perv and N-Imperv; however, there are certain non-linear correlations from heat maps. The results can provide theoretical guidance for application and parameter calibration of SWMM in airport. HIGHLIGHTS A new correlation analysis approach was proposed.; The type, strength, and order of sensitive parameters to the four outputs are analyzed.; Manning-N has a strong linear correlation with Average Depth, Hour of Maximum Flooding, and Time to Peak.; The heat maps can be used to determine whether the parameters are linear or nonlinear correlation.; The Latin hypercube sampling and Python programming were applied.;

Keywords

• airport airfield area
• latin hypercube sampling
• parameter sensitivity analysis
• python programming
• swmm