Comparative Evaluation of Adaptive Neuro-fuzzy Inference System (ANFIS) and Common Interpolation Methods of Rainfall Estimation in Fars Province

In examination of hydrologic issues and water resources, analysis of rainfall information has special importance. Due to various restriction, lack of harvest and visit reading rate of rainfall, limited us to access these information. So apply the methods of estimating water level in specific points is important in the various studies, on the base information of contiguous points. In this research, the common methods of interpolation, Kriging ground statistics and adaptive neuro-fuzzy ablation system were evaluated in Fars province. In this study, 20 synoptic stations of Fars province has been used during 29 years statistical period from 1981-1982 until 2009-2010. Through the investigation was done, December in years of 1992-1993 and 2004-2005 as the best pattern of wetness and April in years of 2008-2009 and 2009-2010 as the best pattern of drought period and also April and November in the 2006-2007 was chose as the annual normal pattern. In Adaptive neuro-fuzzy inference system (ANFIS) for each of the above years, the number of membership, Gaus 2mf, Gsussmf and Gbell were evaluated separately. It’s noticeable that at first consider 15 stations as a training in this system and 5station Tongab, Shourjeh, Baba Arab, Shiraz and Chamriz were evaluated. In this project rating of RMSE, R2 and EF evaluated and compared by two methods of Kriging and Adaptive neuro-fuzzy inference system. According to the obtained results it became clear that in the wetness periods Adaptive neuro-fuzzy inference system, provided more acceptable results. Also during the drought period for predict the rainfall, Kriging method is suggested. The most accurate results are obtained in normal periods in April by Kriging method and in November by Adaptive neuro-fuzzy inference system method.