Forecasting of the Alavian Dam Inflow water Using Optimized Adaptive Neuro-Fuzzy Inference System (OANFIS)

In this study, Optimized Adaptive Neuro-Fuzzy Inference System (OANFIS) was employed on a set of daily, weekly, 10-days and monthly data of inflow water into the Alavian Dam to predict the real-time inflow of the reservoir. Sequential and exhaustive search algorithms were used to determine the numbers and time steps of the model inputs and also reducing the prediction’s errors. In sequential search stage, several inputs series in daily, weekly, 10 days and monthly scales were developed as inputs and those were compared with outflows in time t as expressed by V (t). Also in exhaustive search phase, combinations of 2 from 10 and 3 from 10 which was included 45 and 120 models of time scale of V (t-1) to V (t-10) as inputs were developed and compared with outputs in time t as Vt. Statistical techniques including goodness of fit was used to evaluate the developed models performance. In sequential algorithm with daily scale, in the first step the input of V (t-1) with RSME=0.211 MCM, in the second step the input combination of V (t-1) to V (t-8) with RSME=0.187 MCM and also in the third step V (t-1), V (t-3) and V (t-4) with RSME=1.525 MCM were selected. Also in weekly scale, in the first step the input of V (t-1) with RSME=0.175 MCM, in the second step the input combination of V (t-1) to V (t-8) with RSME=0.192 MCM and also in the third step V (t-1), V (t-3) and V (t-4) with RSME=0.391 MCM were selected. In all of the optimized models of the studied time steps, the inputs of the V(t-1) was recognized as an effective factor and models outputs were sensitive to this variable at this time step which had the least time difference with output.