Examine the Possibility of Replacing the ANFIS Method Instead of Conventional Interpolation Methods and Statistics Land to Estimate in Spatial of the Underground Water

Document Type : Original Article

Authors

1 Associate professor of Water Engineering Department, Water and Soil Engineering College, Gorgan Agriculture Science and Natural Resource University., Gorgan., Iran

2 Ph.d. Student of Water Engineering Department, Sari Agriculture Science and Natural Resource University., Sari., Iran

Abstract

Changes in groundwater levels are important factors and effectiveness in study and management of groundwater resources use in agricultural, domestic and industrial. Lack of harvesting and reading points will limit access to this information due to financial and time constraints. Thus, application of  interpolation and approximation water level methods are based on information from neighboring regions and have a special place in the study of groundwater resources. In this study, conventional interpolation method, the kriging and artificial intelligence systems evolve neural - fuzzy adaptive were evaluated land Dashtenaz Sari city. The research shows that three minimum curvature method, kriging and neural-fuzzy inference system adaptive values RMSE, 56.32, 55.12 and 53.97 cm, and the mean absolute error of 43, 42.54 and 42.94 percent  and as well as the values of R2 0.56, 0.54 and 0.55, are the same accuracy in the interpolation of groundwater levels in the study area. However, due to large fluctuations in water levels in this area varies between 12 and 315 cm, None of these methods are of high accuracy and acceptable. In addition, a Neural-fuzzy inference system adaptive, Withdrawal of membership function trapmf the input layer and 5 for the membership function for each input and output layers with constant learning algorithm for hybrid and conventional interpolation and kriging parameters, the default get the best results by software surfur.

Keywords


دهقانی،ا. عسگری،م و مساعدی،ا . 1388. مقایسه سه روش شبکه عصبی مصنوعی، سیستم استنتاج فازی-عصبی تطبیقی و زمین آمار در میان­یابی سطح آب زیر زمینی(مطالعه موردی دشت قزوین). مجله علوم کشاورزی و منابع طبیعی ایران.16(1):517-529.
طباطبائی،ح و غزالی،م.1390. ارزیابی دقت روش­های میان­یابی در تخمین سطح ایستابی آب زیرزمینی (مطالعه موردی: آبخوان­های فارسان  جونقان و سفید دشت). مجله علوم و فنون کشاورزی و منابع طبیعی، علوم آب و خاک. سال پانزدهم. شماره پنجاه و هفتم.  ص:11-22.
عباس پلنگی،ج.، هاشمی نجفی،س. ف و مومنی،ب. 1386. کاربرد سیستمهای هوش مصنوعی به منظور تخمین تبخیر و تعرق گیاه مرجع (ET0) در شرایط تعداد داده محدود . نهمین سمینار سراسری آبیاری و کاهش تبخیر.
تقی زاده مهرجویی،ر.، زارعیان جهرمی،م.، محمودی،ش.، حیدری،ا و سرمدیان،ف.1387 .بررسی روش­های درون­یابی مکانی جهت تعین تغییرات مکانی ویژگی­های کیفی آب زیرزمینی دشت رفسنجان.مجله علوم و مهندسی آبخیزداری ایران.2(5):63-70.
 Alipour,Z.T., Mahdian,M.H., Pazira,E. Hakimkhani,S. Saeedi,M.2012.The Determination of the Best Rainfall Erosivity Index for Namak Lake Basin and Evaluation of Spatial Variations.  J. Basic. Appl. Sci. Res., 2(1):484-494
Bartels,F. 2000. FUZZEK. The Fuzzy Evaluation and Kriging System. http://www.fuzzeks.de Diamond, P. 1989.Fuzzy Kriging. Fuzzy Sets and System. 33, 315-332.
Farahmand,A.R., Manshouri,M. Liaghat,A  and Hossein Sedghi .2010. Comparison of kriging, ANN and ANFIS models for spatial and temporal distribution modeling of groundwater contaminants. Journal of Food, Agriculture & Environment Vol.8 (3&4): 1146-1155.
Golden software. Surfer. User manual. www.goldensoftware.com
Kurtulus,B and Nicolas,F. 2012.Hydraulic head interpolation using anfis—model selection and sensitivity analysis. Computers &Geosciences. 38(1): 43–51
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.
Nikroo,L., Z, Mazda Kompani., Sepaskhah,A and  FallahShamsi,S.R. 2010. Groundwater depth and elevation interpolation by kriging methods in Mohr Basin of Fars province in Iran. Environmental Monitoring and Assessment. Volume 166, Issue 1-4, pp 387-407.
Richard,M. and Jonathan D. Istok. ١٩٨٨. Geostatistics Applied to Groundwater Contamination. I: Methodology. Journal of Environmental Engineering, 114(2):270-286.