Universal approximators for direct policy search in multi-purpose water reservoir management: A comparative analysis

Giuliani, Matteo; Mason, Emanuele; Castelletti, Andrea; Pianosi, Francesca; Soncini-Sessa, Rodolfo

doi:10.3182/20140824-6-za-1003.01962

Cited by 18 publications

(12 citation statements)

References 22 publications

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“…• Basic and Improved Operating Policies : we designed both the BOP and IOP sets by solving Problem 3 with the Evolutionary Multi-Objective Direct Policy Search (EMODPS) method , an approximate dynamic programming approach that combines direct policy search, nonlinear approximating networks, and multi-objective evolutionary algorithms. In particular, we parameterized the operating policy as Gaussian radial basis functions, because they have been demonstrated to be effective in solving this type of multi-objective policy design problems ( Giuliani et al, 2014a;2014b ), particularly when exogenous information is directly used for conditioning the operations ( Giuliani et al, 2015 ). To perform the optimization, we use the self-adaptive Borg Multi-Objective Evolutionary Algorithm (MOEA) ( Hadka and Reed, 2013 ), which has been shown to be highly robust in solving multi-objective optimal control problems, where it met or exceeded the performance of other state-of-the-art MOEAs ( Zatarain-Salazar et al, 2016 ).…”

Section: Experiments Strategy and Settingmentioning

confidence: 99%

Informing the operations of water reservoirs over multiple temporal scales by direct use of hydro-meteorological data

Denaro

Anghileri

Giuliani

et al. 2017

Advances in Water Resources

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Water reservoir systems may become more adaptive and reliable to external changes by enlarging the information sets used in their operations. Models and forecasts of future hydro-climatic and socio-economic conditions are traditionally used for this purpose. Nevertheless, the identification of skillful forecasts and models might be highly critical when the system comprises several processes with inconsistent dynamics (fast and slow) and disparate levels of predictability. In these contexts, the direct use of observational data, describing the current conditions of the water system, may represent a practicable and zero-cost alternative. This paper contrasts the relative contribution of state observations and perfect forecasts of future water availability in improving multipurpose water reservoirs operation over short- and long-term temporal scales. The approach is demonstrated on the snow-dominated Lake Como system, operated for flood control and water supply. The Information Selection Assessment (ISA) framework is adopted to retrieve the most relevant information to be used for conditioning the operations. By explicitly distinguishing between observational dataset and future forecasts, we quantify the relative contribution of current water system state estimates and perfect streamflow forecasts in improving the lake regulation with respect to both flood control and water supply. Results show that using the available observational data capturing slow dynamic processes, particularly the snow melting process, produces a 10% improvement in the system performance. This latter represents the lower bound of the potential improvement, which may increase to the upper limit of 40% in case skillful (perfect) long-term streamflow forecasts are used

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Section: Experiments Strategy and Settingmentioning

confidence: 99%

Informing the operations of water reservoirs over multiple temporal scales by direct use of hydro-meteorological data

Denaro

Anghileri

Giuliani

et al. 2017

Advances in Water Resources

Self Cite

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“…Their accuracy strongly depends on the choices of the class of functions used to parameterize the operating policy and on the efficiency of the algorithm used to optimize the policy parameters. In this work, we use Gaussian Radial Basis Functions (RBFs) to parameterize the operating policy as they are capable of representing functions for a large class of problems [e.g., Mhaskar and Micchelli , ; Busoniu et al ., ] and have been demonstrated to be more effective than other universal approximators [ Giuliani et al ., ]. The number of basis of the RBFs is set equal to N = M + 1, where M is the dimension of the policy input vector

{scriptI}_{t} = (t, {boldx}_{t}, {boldI}_{t})

.…”

Section: The Hoa Binh Reservoir Case Studymentioning

confidence: 99%

Making the most of data: An information selection and assessment framework to improve water systems operations

Giuliani

Pianosi

Castelletti

2015

Water Resources Research

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Advances in Environmental monitoring systems are making a wide range of data available at increasingly higher temporal and spatial resolution. This creates an opportunity to enhance real-time understanding of water systems conditions and to improve prediction of their future evolution, ultimately increasing our ability to make better decisions. Yet, many water systems are still operated using very simple information systems, typically based on simple statistical analysis and the operator's experience. In this work, we propose a framework to automatically select the most valuable information to inform water systems operations supported by quantitative metrics to operationally and economically assess the value of this information. The Hoa Binh reservoir in Vietnam is used to demonstrate the proposed framework in a multiobjective context, accounting for hydropower production and flood control. First, we quantify the expected value of perfect information, meaning the potential space for improvement under the assumption of exact knowledge of the future system conditions. Second, we automatically select the most valuable information that could be actually used to improve the Hoa Binh operations. Finally, we assess the economic value of sample information on the basis of the resulting policy performance. Results show that our framework successfully select information to enhance the performance of the operating policies with respect to both the competing objectives, attaining a 40% improvement close to the target trade-off selected as potentially good compromise between hydropower production and flood control.

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“…As demonstrated in Figure , the inherent robustness of an optimal solution found by the RBF model in this case allows for a measure of uncertainty in the knowledge of the future climate exposure when determining which management alternative should be implemented. This supports the evidence that the policies identified using the RBF model represent reservoir decisions well [ Giuliani et al ., , ], but for other water resource systems, alternative models may be more appropriate. Where possible, the model chosen should not overcalibrate to a particular hydrometeorological state, and provide robustness across neighboring states in the same manner as the RBFs, to reduce the reliance on exact knowledge of the climate state should a decision be implemented.…”

Section: Discussionmentioning

confidence: 99%

A bottom‐up approach to identifying the maximum operational adaptive capacity of water resource systems to a changing climate

Culley

Noble

Yates

et al. 2016

Water Resources Research

101

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Many water resource systems have been designed assuming that the statistical characteristics of future inflows are similar to those of the historical record. This assumption is no longer valid due to large‐scale changes in the global climate, potentially causing declines in water resource system performance, or even complete system failure. Upgrading system infrastructure to cope with climate change can require substantial financial outlay, so it might be preferable to optimize existing system performance when possible. This paper builds on decision scaling theory by proposing a bottom‐up approach to designing optimal feedback control policies for a water system exposed to a changing climate. This approach not only describes optimal operational policies for a range of potential climatic changes but also enables an assessment of a system's upper limit of its operational adaptive capacity, beyond which upgrades to infrastructure become unavoidable. The approach is illustrated using the Lake Como system in Northern Italy—a regulated system with a complex relationship between climate and system performance. By optimizing system operation under different hydrometeorological states, it is shown that the system can continue to meet its minimum performance requirements for more than three times as many states as it can under current operations. Importantly, a single management policy, no matter how robust, cannot fully utilize existing infrastructure as effectively as an ensemble of flexible management policies that are updated as the climate changes.

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Universal approximators for direct policy search in multi-purpose water reservoir management: A comparative analysis

Cited by 18 publications

References 22 publications

Informing the operations of water reservoirs over multiple temporal scales by direct use of hydro-meteorological data

Informing the operations of water reservoirs over multiple temporal scales by direct use of hydro-meteorological data

Making the most of data: An information selection and assessment framework to improve water systems operations

A bottom‐up approach to identifying the maximum operational adaptive capacity of water resource systems to a changing climate

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