Reproducing human decisions in reservoir management: The case of lake Lugano

Abstract

The objective of this study is to identify a model able to represent the behavior of the historical decision maker (DM) in the management of lake Lugano, during the period 1982-2002. The DM decides every day how much water to release from the lake. We combine hydrological knowledge and machine learning techniques to properly develop the model. As a predictive tool we use lazy learning, namely local linear regression. We setup a daily predictor, which achieves good accuracy, with a mean absolute percentage error around 8.5%. Yet, the behavior of the model is not fully satisfactory during the floods. In fact, from an interview with a domain expert, it appears that the DM can even update the release decision every 6 hours during emergencies. We have therefore developed a refined version of the model, which works with a variable time step: it updates the release decision once a day in normal conditions, and every 6 hours during emergencies. This turns out to be a sensible choice, as the error during emergencies (which represent about 5% of the data set) decreases from 9 to 3 m3/sec. Reproducing human decisions in reservoir management. © Springer-Verlag Berlin Heidelberg 2009.