برآورد هدایت هیدرولیکی اشباع خاک با استفاده از برنامه‌ریزی بیان ژنی و رگرسیون ریج (مطالعه موردی در استان آذربایجان شرقی)

نوع مقاله: مقاله پژوهشی

نویسندگان

1 عضو هیات علمی گروه علوم خاک دانشکده کشاورزی دانشگاه تبریز

2 دانشجوی سابق کارشناسی ارشد دانشگاه آزاد اسلامی واحد تبریز

3 دانشجوی دکتری، دانشگاه تبریز

چکیده

هدایت هیدرولیکی خاک از ویژگی­های مهم فیزیکی خاک است که در مدل‌سازی انتقال آب، املاح و آلاینده‌ها کاربرد دارد. اندازه‎گیری مستقیم هدایت هیدرولیکی خاک وقت‌گیر و پرهزینه بوده و گاهی اوقات به دلیل خطاهای آزمایشی و عدم یکنواختی خاک نتایج بدست آمده چندان قابل اعتماد نمی‌باشد. از طرف دیگر این پارامتر می‌تواند با استفاده از پارامتر‌های زودیافت خاک برآورد شود. هدف از این پژوهش، ارائه مدل‌های برنامه‌ریزی بیان ژنی و رگرسیون خطی بر اساس ویژگی‌های زودیافت هست. برای این منظور 160 نمونه خاک با خصوصیات متفاوت از مناطق مختلف استان آذربایجان‌شرقی برداشته شد. سپس برخی ویژگی‌های فیزیکی و شیمیایی آن‌ها همانند درصد شن، سیلت، رس و مواد آلی، جرم مخصوص ظاهری، pH و EC اندازه‌گیری شد. سپس داده‌ها بطور تصادفی به دو دسته داده‌های سری آموزش (75 درصد) و داده‌های سری آزمون (25 درصد) تقسیم شدند. شش تابع انتقالی (PTFs) با ترکیبی از عملگرهای ریاضی متفاوت توسط برنامه‌ریزی بیان ژنی طراحی شد. در نهایت یکی از توابع که از دقت و صحت بیشتری نسبت به بقیه برخوردار بود، انتخاب گردید. همچنین از رگرسیون ریج برای ارائه تابع انتقالی رگرسیونی استفاده شد. دقت و صحت توابع با معیارهای آماری R2، RMSE و MAE ارزیابی گردید. نتایج نشان داد که تابع انتقالی ارائه شده با روش برنامه‎ریزی بیان ژنی از دقت و صحت بیشتری نسبت به مدل رگرسیونی برخوردار می‌باشد. به‌طوری‌که مقادیر R2، RMSE (Cm h-1) و MAE (Cm h-1) برای تابع انتقالی برنامه‌ریزی بیان‎ژنی در داده‌های سری آموزش به‌ترتیب برابر 91/0، 82/1 و 23/1 و برای داده‌های سری اعتبار‌سنجی برابر 92/0، 27/2 و 59/1 بود. در حالی که مقادیر معیارهای فوق در مدل رگرسیونی، برای داده‌های سری آموزش به‎ترتیب برابر 70/0، 48/3 و 07/2 و برای داده‌های سری اعتبار‌سنجی برابر 76/0، 11/3 و 88/1 بود.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Estimation of saturated hydraulic conductivity by using gene expression programming and ridge regression (A case study in East Azerbaijan province)

نویسندگان [English]

  • Abbas Ahmadi 1
  • Peyman Palizvan zand 2
  • Habib Palivan zand 3
1 University of Tabriz
2 Islamic Azad University of Tabriz
3 University of Tabriz
چکیده [English]

The hydraulic conductivity of soil is an important physical characteristic, which is used for water modeling and the modeling of solutes and pollutants transport. The direct measurement of soil hydraulic conductivity is a time-consuming and costly process, and due to experimental errors and soil heterogeneity, the results are sometimes unrealistic. Besides, it could be estimated by easily measurable soil properties. The purpose of this study is to develop genetic programming and linear regression models to estimate the saturated hydraulic conductivity of soil using readily available soil properties. With this purpose, 160 soil samples with different properties were gathered from various areas of East Azerbaijan province of Iran. Then some physical and chemical characteristics of soil such as the proportions of sand, silt and clay in the soil, and organic matter, bulk density, pH and EC values were measured. Then the data was divided into two different data sets, namely training (75% of data) and testing (25% of data) datasets. GeneXproTools 4.0 and Statistica softwares were used to calibrate Genetic programming and regression models, respectively. Six pedotranfer functions (PTFs) with a combination of different mathematical operators were designed by the genetic programming. Finally, one of the PTFs which was more accurate than the others was selected. Also, the ridge regression was utilized to develop regression PTFs. The accuracy and reliability of PTFs were determined by R2, RMSE, and MAE criteria. The research results showed that the genetic programming PTF (GP-PTF) is more accurate and reliable in comparison with the regression-PTF. In a way that the R2, RMSE (Cm h-1) and MAE (Cm h-1) of GP-PTF were 0.91, 1.82 and 1.23 for the training dataset, respectively, and for the test dataset, the values were 0.92, 2.27 and 1.59, respectively; whereas the values of the above mentioned criteria of regression-PTF for the training dataset were 0.70, 3.48 and 2.07, respectively, and for the test dataset were 0.76, 3.11 and 1.88, respectively.

کلیدواژه‌ها [English]

  • Genetic programming
  • modeling
  • Pedotransfer functions
Aimrun, W. and Amin, M.S.M. (2009) Pedo-transfer function for saturated hydraulic conductivity of lowland paddy soils. Paddy and Water Environment, 7, 217-225.

Alizadeh, A. (2004) Soil physics. Mashhad: Ferdowsi University of Mashhad Publications.

Alvisi, S., Mascellani, G., Franchini, M. and Bardossy, A. (2005) Water level forecasting through fuzzy logic and artificial neural network approaches. J. Hydrol. Earth Sys. Sci. 2, 1107-1145.

Azamathulla, H. M. and Jarrett, R. D. (2013) Use of gene-expression programming to estimate Manning’s roughness coefficient for high gradient streams. Water Resources Management, 27, 715-729.

Babovic, V. and Abbott, M. B. (1997) Evolution of equation from hydraulic data. Part 1: Theory. J. Hydraul. Res. 35, 1–14.

Baybordi, M. (2006). Soil Physics. Tehran: University of Tehran Publications.

Blake, G. R. and Hartge, K. H. (1986) Bulk density. In: Klute, A. Eds. Methods of soil analysis. part 1. (pp. 363-375). 2nd ed. Agron. Monogr. 9. ASA. Madison. WI.

Bouma, J. (1989) Using soil survey data for qualitative land evaluation. Advances in Soil Science, 9, 177-213.

Campbell, G.S. (1985). Soil physics with basic. Elsevier. New York.

Cosby, B. J., Hornberger, G. M., Clapp, R. B. and Ginn, T. (1984). A statistical exploration of the relationships of soil moisture characteristics to the physical properties of soils. Water Resources Research, 20, 682-690.

Dane J. H. and Puckett, W. (1994). Field soil hydraulic properties based on physical and mineralogical information. In: Proceeding of the International Workshop on Indirect Methods for Estimating the Hydraulic Properties of Unsaturated Soils. University of California, Riverside.

Ferreira, C. (2001) Gene Expression Programming in Problem Solving. Invited Tutorial of the 6th Online World Conference on Soft Computing in Industrial Applications, September 10-24.

Ferreira, C. (2006) Gene expression programming: mathematical modeling by an artificial intelligence. Springer. Berlin, Heidelberg New York. USA. 478p.

Gee G. W. and Or D. (2002) Particle-size analysis. In: Warren, A.D. Eds. Methods of Soil Analysis. Part 4. Physical Methods. (pp.255-295). Soil Sci. Soc. Am. Inc.

Guilkey, D. K. and Murphy, J. L. (1975) Directed ridge regression techniques in case of multicollinearity. Journal of the American Statistical Association. 70, 769-775.

Guven, A. (2009) Linear genetic programming for time-series modeling of daily flow rate. J. Earth Syst. Sci, 118, 157-173.

Hashmi, M. Z., Shamseldin, A. Y., Melville, B. W. (2011) Statistical downscaling of watershed precipitation using gene expression programming (GEP). Environ. Modell. Softw. 26, 1639-1646.

Hillel, D. (1982) Introduction to soil physics. Academic Press, Inc. San Dieoga, California.

Hong Y. S. White P. A. and Scott D. M. (2005) Automatic rainfall recharge model induction by evolutionary computational intelligence. Water Resour. Res. 41:W08422.

Jabro, J. D. (1992) Estimation of saturated hydraulic conductivity of soils from particle size distribution and bulk density data. Transactions of the ASAE, 35, 557-560.

Jarvis, N. J., Zavattaro, L. K., Reynolds, W. D., Olsen, P. A., McGechan, M., Mecke, M., Mohanty, B., Leeds-Harrison, P. B. and Jacques, D. (2002). Indirect estimation of near-saturated hydraulic conductivity from readily available soil information. Geoderma, 108, 1-17.

Khu, S. T., Liong, S. Y., Babovic, V., Madsen, H. and Muttil, N. (2001) Genetic programming and its application in real‐time runoff forecasting1. Journal of the American Water Resources Association, 37, 439-451.

Klute, A. (1986) Methods of Soil Analysis. Part 1, Physical and Mineralogical Methods. Madison, Wisconsin, USA.

Koza, J. (1992) Genetic Programming: on the Programming of Computers by Means of Natural Selection. MIT Press.

Li, Y. F., Min, X. and Thong-ngee, G. (2010) Adaptive ridge regression system for software cost estimating on multi-collinear datasets. The Journal of System and Software. 83, 2332-2343.

Liong, S. Y., Gautam, T. R., Khu, S. T., Babovic, V., Keijzer, M. and Muttil, N. (2002). Genetic programming: A new paradigm in rainfall runoff modeling. J. Am. Water Res. Assoc. 38, 705-718.

Makkeasorn, A., Chang, N. B., Beaman, M., Wyatt, C. and Slater, C. (2006). Soil moisture estimation in a semiarid watershed using RADARSAT- 1 satellite imagery and genetic programming. Water Resour. Res. 42, W09401.

Mansoorfar, K. (2008) Advanced statistics methods. Tehran: University of Tehran Publications.

Merdun, H., Cinar, O., Meral, R. and Apan, 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: 108-116.

Morales, A., Clup, G. L. and Johnson, P. R. (2007) The soil hydrological behavior to irrigation with drainage water. pp. 129-134.

Nelson, D. W. and Sommer, L. E. (1982) Total carbon, organic carbon, and organic matter. In: Sparks, D. L., Page, A. L., Helmke, P. A., Loeppert, R. H., Soltanpour, P. N., Tabatabai, M. A., Johnston, C. T., Sumner, M. E. Eds. Methods of soil analysis: part 3. chemical and microbiological properties. pp. 539–579. Soc. Agron., Madison.

Parasuraman ,K., Elshorbagy, A. and Carey, S. K. (2007a). Modeling the dynamics of the evapotranspiration process using genetic programming. Hydrol. Sci. J. 52, 563–578.

Parasuraman, K., Elshorbagy, A. and Si, B. C. (2007b). Estimating Saturated Hydraulic Conductivity Using Genetic Programming. Soil Sci. Soc. Am. J. 71, 1676–1684.

Rezaei, A. and Soltani, A. (2004) An introduction to applied regression analysis. Isfahan University of Technology Publications. 294p.

Savic, D.A., Walters, G. A. and Davidson, J. W. (1999) Genetic programming approach to rainfall–runoff modelling. Water Resour. Manage. 13, 219–231.

Sillon, J. F., Richard, G. and Cousin, I. (2003) Tillage and traffic effects on soil hydraulic properties and evaporation. Geoderma. 116, 29–46.

Soltani, A., Gorbani, M. A. Fakheri- Fard A., Darbandi, S. and Farsadizadeh, D. (2011). Genetic programming and its application in rainfall-runoff modeling. Water and Soil Science. 20, 61-71.

Vereecken, H., Maes, J. and Feyen, J. (1990) Estimating unsaturated hydraulic conductivity from easily measured soil properties. Soil Science, 149, 1-12.

Wagner, B., Tarnawski, V.R., Hennings, V., Muller, U., Wessolek, G. and Plagge, R. (2001) Evaluation of pedotransfer function for unsaturated soil hydraulic conductivity using an independent data set. Geoderma, 102, 275-297.

Wösten, J. H. M. (1997) Pedotransfer functions to evaluate soil quality. In: Gregorich, E. G., Carter, M. R. (eds.) Developments in Soil Science, Elsevier. 25, 221-225.

Wösten J. H. M., Pachepsky, Y. A. A. and Rawls, W. J. (2001) Pedotransfer functions: bridging the gap between available basic soil data and missing soil hydraulic characteristics. J. Hydrol. 251, 123-150.