Fuzzy inference systems for synthetic monthly inflow time series generation

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.