Skip to main content
Journal cover image

Policy Representation Learning for Multiobjective Reservoir Policy Design With Different Objective Dynamics

Publication ,  Journal Article
Zaniolo, M; Giuliani, M; Castelletti, A
Published in: Water Resources Research
December 1, 2021

Most water reservoir operators make use of forecasts to inform their decisions and enhance water systems flexibility and resilience by anticipating hydrological extremes. Yet, despite numerous candidate hydro-meteorological variables and forecast horizons may potentially be beneficial to operations, the best information set for a given problem is often not evident. Additionally, in multipurpose systems characterized by multiple demands with varying vulnerabilities and temporal scales, this information set might change according to the objective tradeoff. In this work, we contribute a novel method to learn the optimal policy representation (i.e., policy input set) by combining a feature selection routine with a multiobjective Direct Policy Search framework in order to retrieve the best policy input set online (i.e., while learning the policy) and dynamically with the objective trade-off. The selected policy search routine is the Neuro-Evolutionary Multi-Objective Direct Policy Search (NEMODPS) which generates flexible policy shapes adaptive to online changes in the input set. This approach is demonstrated on the case study of Lake Como (Italy), where the operating objectives are highly heterogeneous in their dynamics (fast and slow) and vulnerabilities (wet and dry extremes). We show how varying objectives, and tradeoffs therein, benefit from a different policy representation, ultimately yielding remarkable results in terms of conflict mitigation between different users. More informed policies, moreover, show higher robustness when re-evaluated across a suite of different hydrological conditions.

Duke Scholars

Altmetric Attention Stats
Dimensions Citation Stats

Published In

Water Resources Research

DOI

EISSN

1944-7973

ISSN

0043-1397

Publication Date

December 1, 2021

Volume

57

Issue

12

Related Subject Headings

  • Environmental Engineering
  • 4011 Environmental engineering
  • 4005 Civil engineering
  • 3707 Hydrology
  • 0907 Environmental Engineering
  • 0905 Civil Engineering
  • 0406 Physical Geography and Environmental Geoscience
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Zaniolo, M., Giuliani, M., & Castelletti, A. (2021). Policy Representation Learning for Multiobjective Reservoir Policy Design With Different Objective Dynamics. Water Resources Research, 57(12). https://doi.org/10.1029/2020WR029329
Zaniolo, M., M. Giuliani, and A. Castelletti. “Policy Representation Learning for Multiobjective Reservoir Policy Design With Different Objective Dynamics.” Water Resources Research 57, no. 12 (December 1, 2021). https://doi.org/10.1029/2020WR029329.
Zaniolo M, Giuliani M, Castelletti A. Policy Representation Learning for Multiobjective Reservoir Policy Design With Different Objective Dynamics. Water Resources Research. 2021 Dec 1;57(12).
Zaniolo, M., et al. “Policy Representation Learning for Multiobjective Reservoir Policy Design With Different Objective Dynamics.” Water Resources Research, vol. 57, no. 12, Dec. 2021. Scopus, doi:10.1029/2020WR029329.
Zaniolo M, Giuliani M, Castelletti A. Policy Representation Learning for Multiobjective Reservoir Policy Design With Different Objective Dynamics. Water Resources Research. 2021 Dec 1;57(12).
Journal cover image

Published In

Water Resources Research

DOI

EISSN

1944-7973

ISSN

0043-1397

Publication Date

December 1, 2021

Volume

57

Issue

12

Related Subject Headings

  • Environmental Engineering
  • 4011 Environmental engineering
  • 4005 Civil engineering
  • 3707 Hydrology
  • 0907 Environmental Engineering
  • 0905 Civil Engineering
  • 0406 Physical Geography and Environmental Geoscience