Abstract

Document Type : Original Article

Authors

1 Department of Water Engineer, Shiraz Unit, Islamic Azad University of Shiraz, Shiraz, Iran

2 Department of Water Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran

Abstract

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, Gauss2mf, Gsussmf and Gbell were evaluated separately.  It’s noticeable that at first consider 15 stations as a training in this system and 5 station 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.

Keywords


نوری،ف.، حقی­زاده،ع. 1394. شبیه‌سازی فرایند بارش- رواناب با استفاده از شبکه عصبی- مصنوعی و سیستم فازی عصبی تطبیقی و رگرسیون چند متغیره (مطالعه موردی: حوضه آبخیز خرم‌آباد). صفحه 233-243  
نورانی،و صالحی،ک. 1387. مدل­سازی بارش- رواناب با استفاده از شبکه عصبی فازی تطبیقی و مقایسه آن با روش­های شبکه عصبی و استنتاج فازی، مطالعه موردی حوضه آبخیز لیقوان­چای واقع در استان آذربایجان­شرقی. چهارمین کنگره ملی مهندسی عمران، دانشگاه تهران اردیبهشت 1387.
Banik,S., Anwer,M., Khodadad Khan,A.F.M., Ara Rouf,R., Chanchary,F. 2009. Forecasting Bangladeshi monsoon rainfall using neural network and genetic algorithm approaches. International Technology Management Review.2.1:1-18.
Dehghani,A.A., Asgari,M., Mosaedi,A. 2009. Comparison of Geostatistics, Artifitial Neural Networks and Adaptive Neuro-Fuzzy Inference System Approaches in Groundwater Level Interpolation (Case study: Ghazvin aquifer). J. Agric. Sci. Natur. Resour., Vol. J.6: 517-529.
Farahmand,A.R., Manshouri,M., Liaghat,A and Sedghi,H. 2010. Comparison of kriging, ANN and ANFIS models for spatial and temporal distribution modeling of groundwater contaminants. Journal of Food, Agriculture and Environment.8.3-4: 1146-1155.
Ghalhari,G.A., Shakeri,F. 2015. Prediction of winter rainfall using Adaptive Fuzzy Neural Networks, Case study: Khorasan Razavi Province, Iran. Advances in Environmental and Geological Science and Engineering. 2: 412-427
Jang,J.S.R., Sun,C.T and Mizutani,E. 1997. Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Prentice-Hall International. New Jersey.
Kurtulus,B and Nicolas,F. 2012. Hydraulic head interpolation using ANFIS-model selection and sensitivity analysis. Computers  and Geosciences. 38.1: 43-51.