title: |
Fuzzy inference systems for synthetic monthly inflow time series generation |
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publication: |
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part of series: |
Advances in Intelligent Systems Research | |
| pages: | 1060 - 1065 | |
DOI: |
To be assigned soon (how to use a DOI) | |
author(s): |
Ivette Luna, Rosangela Ballini, Secundino Soares, Donato da |
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publication date: |
July 2011 |
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keywords: |
Fuzzy inference systems, synthetic time series, inflow data, stochastic process. |
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abstract: |
Inflow data plays an important role in water and energy resources planning and management. In general,
due to the limited availability of historical inflow data,
synthetic streamflow time series have been widely used
for several applications such as mid- and long-term hydropower scheduling and the identification of hydrological processes. This paper explores the use of fuzzy inference systems for the identification of two hydrological processes, and its use in the generation of synthetic
monthly inflow sequences. Experiments using Brazilian monthly records show that fuzzy systems provide a
promising approach for synthetic streamflow time series
generation. |
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copyright: |
©
Atlantis Press. This article is distributed under the
terms of the Creative Commons Attribution License, which permits
non-commercial use, distribution and reproduction in any medium,
provided the original work is properly cited. |
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full text: |