The aim of this study was to suggest a sensitivity analysis technique that can reliably predict effluent quality and minimize calibration efforts without being seriously affected by influent composition and parameter uncertainty in the activated sludge models No. 1 (ASM1) and No. 3 (ASM3) with a settling model. The parameter sensitivities for ASM1 and ASM3 were analyzed by three techniques such as SVM-Slope, RVM-SlopeMA, and RVM-AreaCRF. The settling model parameters were also considered. The selected highly sensitive parameters were estimated with a genetic algorithm, and the simulation results were compared as ΔEQ. For ASM1, the SVM-Slope technique proved to be an acceptable approach because it identified consistent sensitive parameter sets and presented smaller ΔEQ under every tested condition. For ASM3, no technique identified consistently sensitive parameters under different conditions. This phenomenon was regarded as the reflection of the high sensitivity of the ASM3 parameters. But it should be noted that the SVM-Slope technique presented reliable ΔEQ under every influent condition. Moreover, it was the simplest and easiest methodology for coding and quantification among those tested. Therefore, it was concluded that the SVM-Slope technique could be a reasonable approach for both ASM1 and ASM3.

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