Monthly dam inflow forecasts using weather forecasting information and neuro-fuzzy technique

Abstract

Abstract The purpose of this study is to evaluate the applicability of monthly weather forecasting information to the improvement of monthly dam inflow forecasts. The ANFIS (Adaptive Neuro-Fuzzy Inference System) is used to predict the optimal dam inflow, since it has the advantage of tuning the fuzzy inference system with a learning algorithm. A subtractive clustering algorithm is adopted to enhance the performance of the ANFIS model, which has a disadvantage in that the number of control rules increases rapidly as the number of fuzzy variables increases. To incorporate weather forecasting information into the ANFIS model, this study proposes a method for converting qualitative information into quantitative data. The ANFIS model for monthly dam inflow forecasts was tested in cases with and without weather forecasting information. It can be seen that the model performances obtained with the use of both past observed data and future weather forecasting information are much better than those using past observed data only.