The direct consequence of time consuming simulators is that researchers work less efficiently and that results can be questionable because parameter values are chosen on an ad-hoc basis and not based on all the available evidence.
There are four approaches to deal with this problem: i) discard systems analysis and intelligent control strategies, ii) work only with utterly simplified models, iii) use high-performance computing (HPC), and iv) construct fast surrogate models. The first two are inefficient and the third, using HPC, often requires considerable investment in HPC know-how and IT equipment. It often also requires re-programming simulation software, which is not always the best strategy for all projects and problems. Therefore, constructing fast surrogate models, so-called “emulators”, to speed up slow simulators is very attractive. It does not require a huge investment in new hardware and software, and the same tool can be used to solve very different problems.
However, although we have obtained promising results for the emulation of receiving waters and urban drainage systems, we find that the emulation tools we have developed so far are not yet ready to tackle nonlinear problems. Also, it is unclear in how far they can improve the operation of urban water systems and how they can be used to fuse the information from wireless sensor networks, with potentially hundreds of sensors.