Controlling Stormwater Detention Ponds under Partial Observability

Storm water detention ponds play an important role in urban water management by collecting and conveying rainfall runoff from urban catchment area to nearby streams. Their purpose is not only to avoid flooding but also to reduce stream erosion and degradation caused by the direct discharge of pollutants to the stream.

We model the problem of controlling the discharge rate of water from the ponds as a partially observable hybrid Markov decision process, using UPPAAL Stratego for synthesizing safe and near optimal control strategies. The generated strategies are based on noisy sensor measurements of the water height in the pond, hence the underlying system is only partially observable. We present preliminary results analyzing how sensitive the synthesized strategies are wrt. the accuracy of the sensors used for measurements. These types of analyses not only provide insight into the robustness of the generated strategies, but they can also used for deciding on which measurement sensors to use, balancing sensor cost and accuracy.