Abstract
Competitive optimization techniques have been developed to address the complexity of integrated water resources management (IWRM) modelling; however, model adaptation due to changing environments is still a challenge. In this paper we employ multi-variable techniques to increase confidence in model-driven decision-making scenarios. Here, water reservoir management was assessed using two evolutionary algorithm (EA) techniques, the epsilon-dominance-driven self-adaptive evolutionary algorithm (∈-DSEA) and the Borg multi-objective evolutionary algorithm (MOEA). Many objective scenarios were evaluated to manage flood risk, hydropower generation, water supply, and release sequences over three decades. Computationally, the ∈-DSEA's results are generally reliable, robust, effective and efficient when compared directly with the Borg MOEA but both provide decision support model outputs of value.
Original language | English |
---|---|
Article number | 2021 |
Number of pages | 23 |
Journal | Water |
Volume | 11 |
Issue number | 10 |
DOIs | |
Publication status | Published - 28 Sep 2019 |
Keywords
- self-adaptive technique
- many-objective
- multi-variable
- reservoir operation strategy