Two infiltration trenches were constructed in a densely built-up area in central Copenhagen and equipped with on-line sensors measuring rain, runoff flow from the connected surfaces and water level in the trenches. The paper describes the field site, the measuring system and the results from an initial soil survey. Although the two trenches are placed close to each other they function rather differently, corresponding to effective soil permeabilities of 2·10−6 m/s in one trench and a factor 10 smaller in the other. During 2¾ years of measuring 89 events were recorded, of which 7 caused overflow. Analyses of falling water tables after rain indicated slight clogging, but this effect is less important than the general lack of knowledge about soil permeability for normal design situations. The results indicate that the stormwater infiltration in central urban areas with compressed soils and backfill is more feasible than previously anticipated.

The introduction of Artificial Neural Networks (ANNs) as a tool in the field of urban storm drainage is discussed. Besides some basic theory on the mechanics of ANNs and a general classification of the different types of ANNs, two ANN application examples are presented; The prediction of runoff coefficients and the restoration of rainfall data. From the results, it can be concluded that ANNs can deal with problems that are traditionally difficult for conventional modelling techniques to solve. Their advantages include good generalisation abilities, high fault tolerance, high execution speed, and the ability to adapt and learn. However, ANNs rely strongly on the quantity of data examples, their training is occasionally slow, and they are not transparent and obstruct any closer analysis and interpretation of their performance. Finally, it is expected that the future of ANNs will lie in its integration with other conventional and more advanced modelling techniques, creating so-called hybrid models.

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