نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانش آموخته کارشناسی ارشد مهندسی علوم خاک، دانشگاه محقق اردبیلی (مکاتبه کننده)

2 دانشیار گروه علوم خاک، دانشگاه محقق اردبیلی

3 استادیار گروه علوم خاک، دانشگاه محقق اردبیلی

4 کارشناس ارشد آزمایشگاه گروه مهندسی علوم خاک، دانشگاه ارومیه

چکیده

اندازه­گیری مستقیم برخی ویژگی­های فیزیکی دیریافت خاک مثل پایداری خاکدانه وقت­گیر، هزینه­بر و گاهی اوقات به دلیل خطاهای آزمایشی و غیریکنواختی خاک غیرواقعی است. پایداری خاکدانه­ها به عنوان یک ویژگی دیریافت می‌توانند از ویژگی­های زودیافت خاک مانند بافت، جرم ویژه ظاهری، کربن آلی و کربنات کلسیم معادل با استفاده از توابع انتقالی رگرسیونی و شبکه عصبی مصنوعی برآورد شوند. هدف از این پژوهش ارائه مدل­هایی برای برآورد میانگین وزنی قطر (MWD) خاکدانه از روی ویژگی­های زودیافت با استفاده از مدل­های رگرسیونی و شبکه عصبی مصنوعی و همچنین ارزیابی کارآیی این مدل­ها در برآورد با استفاده از معیارهای آماری مانندضریب تبیین (R2) و جذر میانگین مربعات خطا (RMSE) بود. برای این منظور،100 نمونه خاک از مناطق مختلف دشت اردبیل برداشت وتجزیه­های فیزیکی و شیمیایی انجام شد. داده­ها به دو سری داده­های آموزشی (80 درصد داده‌ها) و داده­های آزمون (20 درصد داده‌ها) تقسیم شدند. نتایج نشان داد که هر دو روش می­توانند میانگین وزنی قطر خاکدانه را با دقت قابل قبولی برآورد کنند با این وجود شبکه عصبی مصنوعی از دقت بیش­تر و خطای کمتری (R2  و  RMSEبه ترتیب 88/0 و 042/0) در برآورد میانگین وزنی قطر خاکدانه نسبت به مدل­های رگرسیونی (R2  و RMSE به ترتیب 81/0 و 054/0) برخوردار بودند.

کلیدواژه‌ها

عنوان مقاله [English]

Prediction of Mean Weight Diameter of Aggregates using Artificial Neural Network and Regression Models

نویسندگان [English]

  • Hamed Amir Abedi 1
  • Shokr Allah Asghari 2
  • Tarahom Mesri Ghandomshin 3
  • Naser Balandeh 4

چکیده [English]

Direct measurement of soil physical properties is time consuming, costly and sometimes unreliable because of soil heterogeneity and experimental errors. Stability of aggregates could be estimated from surrogate data such as soil texture, bulk density, organic carbon and CaCO3 using pedotransfer function (PTF).The objective of this research was to present regression PTFs and artificial neural network models to predict mean weight diameter (MWD) of aggregate from limited sets of soil properties and to assess the efficiency of the presented models to predict the MWD with the statistical criteria including the coefficient of determination (R2) and root mean square deviation (RMSE). In total, 100 soil sample were collected from Ardabil plain and analyzed for their physicals and chemicals properties. Soil samples were divided into two groups, so that, 80 samples were used for the development and remaining 20 samples for the validation of PTFs. The values of R2 and RMSE for regression PTFs and artificial neural networks were, respectively, 0.88, 0.42 for neural networks and 0.81, 0.054 for regression PTF. Results showed that two methods could be applied to predict the MWD in Ardabil plain. However, artificial neural networks performed better than regression model in this study.

کلیدواژه‌ها [English]

  • Aggregate stability
  • neural networks
  • Pedotransfer function
References
Alijanpour Shalmani A., Shabanpour M., Asadi H., and Bagheri F. 2011. Estimation of soil aggregate stability in forest`s soils of Guilan province by artificial neural networks and regression pedotransfer functions. Journal of Soil and Water, 21 (3):153-162. (In Persian)
Amezketa E., Arguos R., Carranza R., and Urgel B. 2003.Macro and microaggregate stability of soils determined by a combination of wet sieving and laser-ray diffraction. Spanish Journal of Agriculture Research, 4 (1): 83-94.
Amirabedi H., Asghari Sh., Mesri T., and Keivanbehjo F. 2013. Estimating of field capacity, permanent wilting point and available water content in Ardabil plain using regression and artificial neural network models. Applied Soil Research, 1(1):60-72.
Angers D.A., Peasant A., and Vigneus J. 1992. Early croppingincluded changes in soil aggregation, organic carbon, and microbial biomass. Soil Science Society of American Journal, 56: 115-119.
Aringhieri R. and Sequi P. 1979. The arrangement of organic matter in a soil crumb. In: Emerson WW (Eds.), Modification of Soil Structure. John Willy and Sons, Chichester, pp. 145-150.
Arshad M.A., and Coen G.M. 1992. Characterization of soil quality: physical and chemical criteria. America Journal of Alternative Agriculture, 7:25-31.
Barzegar A.R., Oades J.M., Rengasamy P., and Giles L. 1994. Effect of sodicity and salinity on disaggregation and tensile strength of an Alfisol under different cropping systems. Soil and Tillage Research, 32: 329- 345.
Basalatpour A., Haj Abbasi M., and Ayyoubi Sh. 2011. Estimation of some physical and mechanical characteristics of the soil using artificial neural networks. Sixth National Congress of Civil Engineering, 26-27 April, Semnan. (In Persian)
Bear M.H., Hendrix P.F., and Coleman D.C. 1994. Water stable aggregates and organic carbon fractions in conventional and no tillage soils. Soil Science Society of American Journal, 58: 777-786.
Blair N. 2000. Impact of cultivation and sugarcane green trash management on carbon fractions and aggregate stability for a cromicluvisol Queensland, Australia. Soil and Tillage Research, 55: 183-191.
Boix-Fayos C., Calvo-Cases A., Imeson, A.C., and Soriano-Soto, MD. 2001. Influence of soil properties on the aggregation of some Mediterranean soils and the use of aggregate size and stability as land degradation indicators. Catena, 44: 47-67.
Calero N., Barron V. and Torrent J. 2008.Water dispersible clay in calcareous soil south western in Spain. Catena, 74: 22 30.
Campbell G.S. 1985. Soil Physics with Basic: Transport Models for Soil–Plant System. Elsevier, New York.
Celik I. 2005. Land-use effects on organic matter and physical properties of soil in a southern Mediterranean highland of Turkey. Soil and Tillage Research, 83: 270-277.
Chan K.Y., and Heenan D.P. 1996. The influence of crop rotation on soil structure and soil physical properties under conventional tillage. Soil and Tillage Research, 37: 113- 125.
Chenu C., LeBissonnias Y., and Arrouays D. 2000. Organic matter influence on clay wettability and soil aggregate stability. Soil Science Society of America Journal, 64: 1479-1486.
Emerson, W.W. 1991. Structural decline of soils, assessment and prevention. Australian Journal of Soil Research, 24: 905-921.
Etminan S., Kiani F., and Habashi H. 2011. Effect of soil properties with different parent materials on aggregate stability in Shastkola watershed, Golestan province. Journal of Soil Management and Sustainable Production, 1(2): 39-60. (In Persian)
Ghorbani-Dashtaki Sh., Homaee M., and Mahdian M. 2009. Estimating soil water infiltration parameters using Artificial Neural Networks. Journal of Water and Soil, 23(1):185-198. (In Persian)
Hillel D. 2004. Introduction to Environmental Soil Physics. Elsevier,AcademicPress, Amsterdam, 494p.
Ismail I., Blevins R.L., and Frye W.W. 1994. Long-term no-tillage effects on soil properties and continuous corn yields. Soil Science Society of America Journal, 58:193-198.
Kemper A., and Rosenau R.C. 1986. Aggregate stability and size distribution. In׃ Klute A. (Eds.), Methods of Soil Analysis. Part 1, America Society of Agronomy and Soil Science Society of America, Madison, WI.
Ketcheson J. 1980. Long range effects of intensive cultivation and monoculture on the quality of southern Ontario soils. Canadian Journal Soil Science, 60: 403-410.
Khalilmoghadam B., Afyuni M., Abbaspour K.C., Jalalian A., Dehgani A., and Schulin R., 2009. Estimation of surface shear strength in Zagros region of Iran- A comparison of artificial neural networks and multiple- linear regression models. Geoderma, 153: 29-36.
Khashei Siuki A., Jalali Moakhar V., Noferesti A., and Ramazani Y. 2015. Comparing nonparametric k-nearest neighbor technique with ANN models for predicting soil saturated hydraulic conductivity. Journal of Soil Management and Sustainable Production, 5(3): 81-95. (In Persian)
Khazaee A., Mosaddeghi M.R., and Mahboubi A.A. 2008.Structural stability assessment using wet sieving method and its relations with some intrinsic properties in 21 soil series from Hamadan province. Agricultural Research, 8(1):171-181.
Klute A. 1986. Methods of Soil Analysis. Part 1. Physical and Mineralogical Methods. 2nd edition.American Society of Agronomy and Soil Science Society of America, Madison, WI.
Koutikka L.S., Bartoli F., Andreux C.C., Cerri G., Burtin T.C., and Philippy R. 1997.Organic matter dynamics and aggregation in soils under rain forest and pastures of increasing age in the eastern Amazon basin. Geoderma, 76: 87-112.
Lado M., Paz A., and Ben-Hur M. 2004. Organic matter and aggregate size interactions in infiltration, seal formation and soil loss. Soil Science Society of America Journal, 68: 935-942.
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.
Mesri Gundoshmian T. 2009. The use of intelligent systems to optimize drop combine. PhDthesis, Engineering of Agriculture Machinery and Mechanization Department, University of Tabriz. (In Persian)
Minasny B., and Mcbartney A.B. 2002. The neuro-m method for fitting neural network parametric pedotransfer functions. Soil Science Society of America Journal, 66: 352-361.
Pachepsky Y.A., Timlin D.J., and Varallyay G. 1996. Artificial neural networks to estimate soil water retention from easily measurable data. Soil Science Society of America Journal, 60: 727–773.
Rezai A., and Soltani A. 1998. Introduction to Applied Regression Analysis. Isfahan University Press. (In Persian)
Sarmadian F., GhanbarianAlavijeh B., Taghizadeh Mehrjardi R., and Keshavarzi A. 2011. Comparison of linear and nonlinear pedotransfer functions with artificial neural networks in prediction of surface fractal dimension. Journal of Range and Watershed Management, 64(1): 53-64.
Schaap M.G., Leij F.J., and Van Genuchten M.T. 1998. Neural network analysis for hierarchical prediction of soil hydraulic properties. Soil Science Society of America Journal, 62:847–855.
Seta A., and Karathanasis A. 1996. Water dispersible colloids and factors influencing their dispersibility from soil aggregates. Geoderma, 74: 255-266.
Shrestha B.M., Singh B.K., Sitaula R.L., and Barjacharya R.M. 2007. Soil aggregate and particle associated organic carbon under different land use in Nepal. Soil Science Society of America Journal, 71: 1194- 1203.
Skidmore C.A., and Layton J.B. 1992. Dry- soil aggregate stability as influenced by selected soil properties. Soil Science Society of America Journal, 75: 557-561.
Skidmore E.L., and Layton J.B. 1992. Dry-soil aggregate stability as influenced by selected soil properties. Soil Science Society of America Journal, 56(2): 557-561.
Tajik F., Pazira A., and Rahimi A. 1998.The effect of organic matter on soil physical and mechanical properties.Articles Collections specialized scientific and technical research Agricultural Engineering, 3(10): 1-20. (In Persian)
Tajik F. 2004. Evaluation of aggregates stability in the some parts of Iran. Water and Soil Science, 8(1): 134-125. (In Persian)
Tamari S., Wosten J.H., and Ruiz-Suarez JC. 1996. Testing an artificial neural network for predicting soil hydraulic conductivity. Soil Science Society of America Journal, 60: 1732–1741.
Watts C.W., and Dexter, A.R. 1997. The influence of organic matter in reducing the destabilization of soil bay simulated tillage. Soil and Tillage Research, 42: 253-275.
Yilmaz I., and Yuksek G. 2009. Prediction of the strength and elasticity modulus gypsum using multiple regression, ANN, and ANFIS models. International Journal of Rock Mechanics and Mining Sciences, 46: 803-810.