Fuzzy inference systems for synthetic monthly inflow time series generation
- Ivette Luna, Rosangela Ballini, Secundino Soares, Donato Da Silva Filho
- Corresponding Author
- Ivette Luna
Available Online August 2011.
- https://doi.org/10.2991/eusflat.2011.111How to use a DOI?
- Fuzzy inference systems, synthetic time series, inflow data, stochastic process.
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 PB - Atlantis Press SP - 1060 EP - 1065 UR - https://doi.org/10.2991/eusflat.2011.111 DO - https://doi.org/10.2991/eusflat.2011.111 ID - Luna2011/08 ER -