Open AccessADAPTIVE NEURO-FUZZY COMPUTING TECHNIQUE FOR PRECIPITATION ESTIMATION
Author(s) -
Dalibor Petković,
Milan Gocić,
Shahaboddin Shamshirband
Publication year - 2016
Publication title -
facta universitatis. series: mechanical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.483
H-Index - 16
eISSN - 2335-0164
pISSN - 0354-2025
DOI - 10.22190/fume1602209p
Subject(s) - adaptive neuro fuzzy inference system , artificial neural network , computer science , neuro fuzzy , fuzzy logic , matlab , process (computing) , artificial intelligence , inference system , data mining , soft computing , machine learning , precipitation , fuzzy control system , meteorology , physics , operating system
The paper investigates the accuracy of an adaptive neuro-fuzzy computing technique in precipitation estimation. The monthly precipitation data from 29 synoptic stations in Serbia during 1946-2012 are used as case studies. Even though a number of mathematical functions have been proposed for modeling the precipitation estimation, these models still suffer from the disadvantages such as their being very demanding in terms of calculation time. Artificial neural network (ANN) can be used as an alternative to the analytical approach since it offers advantages such as no required knowledge of internal system parameters, compact solution for multi-variable problems and fast calculation. Due to its being a crucial problem, this paper presents a process constructed so as to simulate precipitation with an adaptive neuro-fuzzy inference (ANFIS) method. ANFIS is a specific type of the ANN family and shows very good learning and prediction capabilities, which makes it an efficient tool for dealing with encountered uncertainties in any system such as precipitation. Neural network in ANFIS adjusts parameters of membership function in the fuzzy logic of the fuzzy inference system (FIS). This intelligent algorithm is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method.