Optimal reservoir operations for riverine water quality improvement: a reinforcement learning strategy
| dc.contributor.author | Rieker, Jeffrey Donald, author | |
| dc.contributor.author | Labadie, John W., advisor | |
| dc.contributor.author | Fontane, Darrell G., committee member | |
| dc.contributor.author | Frevert, Donald K., committee member | |
| dc.contributor.author | Anderson, Charles W., committee member | |
| dc.date.accessioned | 2007-01-03T05:43:17Z | |
| dc.date.available | 2007-01-03T05:43:17Z | |
| dc.date.issued | 2011 | |
| dc.description.abstract | Complex water resources systems often involve a wide variety of competing objectives and purposes, including the improvement of water quality downstream of reservoirs. An increased focus on downstream water quality considerations in the operating strategies for reservoirs has given impetus to the need for tools to assist water resource managers in developing strategies for release of water for downstream water quality improvement, while considering other important project purposes. This study applies an artificial intelligence methodology known as reinforcement learning to the operation of reservoir systems for water quality enhancement through augmentation of instream flow. Reinforcement learning is a methodology that employs the concepts of agent control and evaluative feedback to develop improved reservoir operating strategies through direct interaction with a simulated river and reservoir environment driven by stochastic hydrology. Reinforcement learning methods have advantages over other more traditional stochastic optimization methods through implicit learning of the underlying stochastic structure through interaction with the simulated environment, rather than requiring a priori specification of probabilistic models. Reinforcement learning can also be coupled with various computing efficiency techniques as well as other machine learning methods such as artificial neural networks to mitigate the "curse of dimensionality" that is common to many optimization methodologies for solving sequential decision problems. A generalized mechanism is developed, tested, and evaluated for providing near-real time operational support to suggest releases of water from upstream reservoirs to improve water quality within a river using releases specifically designated for that purpose. The algorithm is designed to address a variable number of water quality constituents, with additional flexibility for adding new water quality requirements and learning updated operating strategies in a non-stationary environment. The generalized reinforcement learning algorithm is applied to the Truckee River in California and Nevada as a case study, where the federal and local governments are purchasing water rights for the purpose of augmenting Truckee River flows to improve water quality. Water associated with those acquired rights can be stored in upstream reservoirs on the Truckee River until needed for prevention of water quality standard violations in the lower reaches of the river. This study shows that in order for the water acquired for flow augmentation to be fully utilized as a part of a longer-term strategy for water quality management, increased flexibility is required as to how those waters are stored and how well the storage is protected from displacement through reservoir spill during times of high runoff. The results show that with those flexibilities, the reinforcement learning mechanism has the ability to produce both short-term and long-term strategies for the use of the water, with the long-term strategies capable of significantly improving water quality during times of drought over current and historic operating practices. The study also evaluates a number of variations and options for the application of reinforcement learning methods, as well as use of artificial neural networks for function generalization and approximation. | |
| dc.format.medium | born digital | |
| dc.format.medium | doctoral dissertations | |
| dc.identifier | Rieker_colostate_0053A_10338.pdf | |
| dc.identifier.uri | http://hdl.handle.net/10217/49858 | |
| dc.language | English | |
| dc.language.iso | eng | |
| dc.publisher | Colorado State University. Libraries | |
| dc.relation.ispartof | 2000-2019 | |
| dc.rights | Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. | |
| dc.subject | reinforcement learning | |
| dc.subject | truckee | |
| dc.subject | reservoir operations | |
| dc.subject | artificial intelligence | |
| dc.subject | optimization | |
| dc.subject | water quality | |
| dc.title | Optimal reservoir operations for riverine water quality improvement: a reinforcement learning strategy | |
| dc.type | Text | |
| dcterms.rights.dpla | This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). | |
| thesis.degree.discipline | Civil and Environmental Engineering | |
| thesis.degree.grantor | Colorado State University | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy (Ph.D.) |
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