Experimental Studying of Extracting of Pedo Transfer Function by Regression and GMDH Method to Estimate Infiltration in Surface Irrigation

Document Type : Original Article

Authors

1 Assistant Professor of Department of Water Engineering, Faculty of Agricultural Sciences, University of Guilan., Rasht., Iran

2 M.Sc. student of Department of Water Engineering, Faculty of Agricultural Sciences, University of Guilan

3 Assistant Professor of Department of Water Engineering, Faculty of Agricultural Sciences, University of Guilan

Abstract

Infiltration is a major soil hydraulic property that has effective role on water and soil resources researches. Field methods of infiltration measuring are time consuming and costly. Therefore an indirect estimation method such as transfer functions is concerned. In this study were derived pedotransfer functions of infiltration of surface irrigation in both GMDH neural network and regression methods by the chemical and physical properties of soil and water. Due to this purpose, 17 soil samples were gathered from Foumanat plain of Guilan province. Physical and chemical properties of soil including soil texture, soil size distribution, particle density, bulk density, soil water retention curve, electrical conductivity, sodium absorption ratio, organic matter content and pH were measured and infiltration experiment were conducted in physical model to evaluate the effects of three treatments of water height on soil surface including 3, 5 and 7 cm and three treatments of sodium adsorption ratio including 1.7, 2.5 and 9. The results showed that GMDH neural network method estimate infiltration more accurate (R2 =0.82) than regression method (R2 =0.75). Regression equations showed that hydraulic parameters of irrigation water including water standing on soil surface and contact surface of water with soil had more effect on infiltration than chemical properties of water and soil. In GMDH method, SAR of water resource, sand percentage and soil moisture in 100 cm matric potential were recognized more effective parameter on infiltration estimation.

Keywords


ابراهیمی،ک. و نایب‌لویی،ف. 1388. تخمین نفوذپذیری نهایی خاک‌ها با استفاده از مدل شبکه عصبی مصنوعی (مطالعه موردی: مزرعه پردیس ابوریحان). مجله پژوهش‌های حفاظت آب و خاک. 16. 1: 57-37.
حق‌نیا،غ. 1374. دشواری‌های نفوذ آب در خاک. انتشارات دانشگاه فردوسی مشهد. شماره 183. 225 صفحه.
علیزاده،ا. 1378. رابطه آب و خاک و گیاه. انتشارات دانشگاه امام رضا (ع). 484 صفحه.
داغبندان،ا و اکبری‌زاده،م. 1393. طراحی ساختارهای ANFIS و شبکه های عصبی GMDH برای پیش بینی میزان بهینه مصرف ماده منعقدکننده در فرایند تصفیه آب، مطالعه موردی: تصفیه خانه بزرگ آب گیلان. نشریه آب و فاضلاب.5: 41-32.
دواتگر،ع.، سپاس­خواه،ن.، نیشابوری،م،ر.، رضایی،ل.، بیات،ح. و نریمان‌زاده،ن. 1394. ارزیابی کارایی الگوریتم مدیریت داده‌ها به روش گروهی (GMDH) برای پیش‌بینی شاخص‌های نگهداری آب در خاک‌های شالیزاری. نشریه پژوهش‌های خاک. 29. 2: 188-175.
قادری،ک.، عرب،ک،ر.، تشنه‌لب،م و قزاق،آ. 1389. مدل­سازی بهره‌برداری هوشمند از مخازن با استفاده از برخورد گروهی با داده­ها (GMDH). تحقیقات منابع آب ایران. 6. 3: 67-55.
نوابیان،م.، اشرف‌تالش،س.ح.، اسمعیلی‌ورکی،م و جمالی،ع. 1390. مقایسه توابع انتقالی رگرسیون و شبکه عصبی مصنوعی با ANFIS در تخمین هدایت آبی اشباع. دوازدهمین کنگره علوم خاک ایران. دانشگاه تبریز. 14-12 شهریور ماه. صفحه 5-1.
Abdolrahimi,S., Nasernejad,B., Pazuki,G. 2014. Prediction of partition coefficients of alkaloids in ionic liquids based aqueous biphasic systems using hybrid group method of data handling (GMDH) neural network. Journal of Molecule Liquids. 191: 79-84.
Atashrouza,S., Pazukia,G and Seyfi Kakhkib,S. 2015. A GMDH-type neural network for prediction of water activity in glycol and Poly (ethylene glycol) solutions. Journal of Molecular Liquids. 202: 95-100.
Bashour,I., Sayegh,A.H. 2007. Methods of analysis for soils of arid and semi-arid regions. Food and Agriculture Organization of the United Nations. 119 pages.
Bondurant,J.A. 1957. Developing a furrow infiltrometer. Agricultural Engineering. 38.8: 602-605.
Bowles, J.E. 1992. Engineering properties of soils and their measurement. McGraw-Hill, Inc. 481 pages.
Dane,J.H., Topp,C., Campbell,G., Horton,W.A., Jury,D.R., Nielsen,H.M., van,H., Wierenga,P and Top,G.C. 2002. Part 4-Physical methods. Methods of Soil Analysis.1163 pages.
Ekhmaj,A.I. 2010. Predicting soil infiltration rate using Artificial Neural Network. Pages 117-121 in Proceeding. Environmental Engineering and Applications (ICEEA), International Conference on IEEE.
Haise,H.R. 1956. Use of cylinder infiltrometers to determine the intake characteristics of irrigated soils. 20 pages.
Ivakhnenko, A. and G. Ivakhnenko. 1995. The review of problems solvable by algorithms of the group method of data handling (GMDH). Pattern recognition and image analysis c/c of raspoznavaniye obrazov i analiz izobrazhenii. 5: 527-535.
Jamieson,P.D., Porter,J.R.,  Wilson, D.R. 1991. A test of computer simulation model ARC-WHEAT1 on wheat crops grown in New Zealand. Field Crops Research. 27:337-350.
Jaynes,D and Hunsaker,D. 1989. Spatial and temporal variability of water content and infiltration on a flood irrigated field. Transactions of the ASAE. 32.4: 1229-1238.
Klute,A. 1986. Methods of soil analysis. Part 1.Physical and mineralogical methods. American Society of Agronomy, Inc. 1188 pages.
Lambe,T.W. 1951. Soil testing for engineers. Soil Science. 72.5: 406.
Mazaheri,M.R and Mahmoodabadi,M. 2012. Study on infiltration rate based on primary particle size distribution data in arid and semiarid region soils. Arabian Journal of Geosciences. 5.5: 1039-1046.
McClymont,D and Smith,R. 1996. Infiltration parameters from optimization on furrow irrigation advance data. Journal of Irrigation science. 17.1: 15-22.
Merdun,H., ÖÇInar,R., Meral,M. 2006. Comparison of artificial neural network and regression pedotransfer functions for prediction of soil water retention and saturated hydraulic conductivity. Soil and Tillage Research. 90.1: 108-116.
Nariman-Zadeh,N., Darvizeh,A and Ahmad-Zadeh,G. 2003. Hybrid genetic design of GMDH-type neural networks using singular value decomposition for modeling and prediction of the explosive cutting process. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture. 217.6: 779-790.
Page,A.L. 1982. Methods of soil analysis. Part 2. Chemical and microbiological properties. American Society of Agronomy, Soil Science Society of America. 1143 pages.
Pansu,M., and Gautheyrou,J. 2006. Handbook of soil analysis: mineralogical, organic and inorganic methods. Springer Publication. 993 pages.
Rawls,W. and Brakensiek,D. 1989. Estimation of soil water retention and hydraulic properties. Unsaturated flow in hydrologic modeling. Theory and practice. The Netherlands: Kluwer Academic Publishers. pp. 275-300.
Sy,N.L. 2006. Modeling the infiltration process with a multi-layer perceptron artificial neural network. Hydrological Sciences Journal. 51.1: 3-20.
Tomasella,J., Pchepsky,Ya. Crestana,S. and Rawls,W.J. 2003. Comparison of two techniques to develop pedotransfer functions for water retention. Soil Science. Society. American  Journal. 67: 1085-1092.
Ungaro,F., Calzolari,C and Busoni,E. 2005. Development of pedotransfer functions using a group method of data handling for the soil of Pianura Padano-Veneta region of North Italy: Water retention properties. Geoderma, 124: 293-317.
Valiantzas,J., Aggelides,S. and Sassalou,A. 2001. Furrow infiltration estimation from time to a single advance point. Agricultural Water Management. 52.1: 17-32.
Wagner,B., Tarnawski,V., Hennings,U., Mller,G. Wessolek,V and Plagge,R. 2001. Evaluation of pedotransfer functions for unsaturated soil hydraulic conductivity using an independent data set. Geoderma. 102.3: 275-297.
Walker,W.R., Skogerboe,G.V. 1987. Surface irrigation. Theory and practice. Prentice-Hall. 386 pages.