Modeling the behavior of water reservoir operators via eigenbehavior analysis. (December 2018) - Giuliani, Matteo; Herman, Jonathan D.
Highlights: We extract typical behaviors of water reservoir operators from observational data. We cluster the reservoir operators based on their behavioral profile. We discover behavioral profiles that are vulnerable to drought conditions. Abstract: The large number of dammed rivers worldwide emphasizes the need to couple models of natural processes with models describing human behaviors. However, such behavioral models are often simplistic and lack proper validation against observational data. In this work, we contribute a new approach to infer the typical operations of water reservoirs from historical observations, using data-driven behavioral modeling based on eigenbehavior analysis. The approach is demonstrated using monthly storage data from 172 reservoirs in California, USA. Results show that the proposed method identifies four typical behavioral profiles, which are strongly linked to key features of the reservoirs. Moreover, we show how the identified models can be used for discovering behavioral profiles, and associated reservoir characteristics, that are vulnerable to drought conditions.