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

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MISAGHI FARHAD

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Journal: Iranian Journal of Soil and Water Research Year:2016 | Volume:47 | Issue:3 Page(s): 439-448

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Title

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

Author(s)

MISAGHI FARHAD | Issue Writer Certificate

Keywords

forecastingQ1

OANFISQ1

SequentialQ1

Exhaustive Search AlgorithmsQ1

Abstract

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.

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APA: Copy

MISAGHI, FARHAD. (2016). Forecasting of the Alavian Dam Inflow water Using Optimized Adaptive Neuro-Fuzzy Inference System (OANFIS). IRANIAN JOURNAL OF SOIL AND WATER RESEARCH, 47(3 ), 439-448. SID. https://sid.ir/paper/225936/en

Vancouver: Copy

MISAGHI FARHAD. Forecasting of the Alavian Dam Inflow water Using Optimized Adaptive Neuro-Fuzzy Inference System (OANFIS). IRANIAN JOURNAL OF SOIL AND WATER RESEARCH[Internet]. 2016;47(3 ):439-448. Available from: https://sid.ir/paper/225936/en

IEEE: Copy

FARHAD MISAGHI, “Forecasting of the Alavian Dam Inflow water Using Optimized Adaptive Neuro-Fuzzy Inference System (OANFIS),” IRANIAN JOURNAL OF SOIL AND WATER RESEARCH, vol. 47, no. 3 , pp. 439–448, 2016, [Online]. Available: https://sid.ir/paper/225936/en

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