Locating arrays (LAs) are experimental designs for screening interactions in engineered systems. LAs are often highly unbalanced, requiring advanced techniques to recover the terms that significantly influence system performance. While perfect recovery is achieved in the absence of noise, real systems are noisy. Therefore, in this paper, we study the robustness of recovery in the presence of noise. Using known models to generate synthetic data, we investigate recovery accuracy as a function of noise. Separation is introduced into LAs to allow more coverage for each t-way interaction; when separation is higher, recovery in noisy scenarios should improve. We find that locating arrays are able to recover the influential terms even with high levels of noise and that separation appears to improve recovery. Under the pessimistic assumption that noise depends on the range of responses, it is no surprise that terms with small coefficients become indistinguishable from noise.