Fuzzy inference systems for synthetic monthly inflow time series generation

DOI

10.2991/eusflat.2011.111How to use a DOI?

Keywords

Fuzzy inference systems, synthetic time series, inflow data, stochastic process.

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.

Copyright

© 2011, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Ivette Luna
AU  - Rosangela Ballini
AU  - Secundino Soares
AU  - Donato Da Silva Filho
PY  - 2011/08
DA  - 2011/08
TI  - Fuzzy inference systems for synthetic monthly inflow time series generation
BT  - Proceedings of the 7th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-11)
PB  - Atlantis Press
SP  - 1060
EP  - 1065
SN  - 1951-6851
UR  - https://doi.org/10.2991/eusflat.2011.111
DO  - 10.2991/eusflat.2011.111
ID  - Luna2011/08
ER  -