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.

Modeling the behavior of water reservoir operators via eigenbehavior analysis

Giuliani, Matteo;
2018-01-01

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.
2018
Behavioral modeling; Data-driven modeling; Data-mining; Drought management; Water reservoir operations; Water Science and Technology
File in questo prodotto:
File Dimensione Formato  
Giuliani2018_eigCA.pdf

Accesso riservato

Descrizione: articolo principale
: Publisher’s version
Dimensione 2.94 MB
Formato Adobe PDF
2.94 MB Adobe PDF   Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1071510
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 16
  • ???jsp.display-item.citation.isi??? 14
social impact