تحقیقات کاربردی خاک

شماره جاری

بر اساس شماره‌های نشریه

بر اساس نویسندگان

بر اساس موضوعات

نمایه نویسندگان

نمایه کلیدواژه ها

درباره نشریه

اهداف و چشم انداز

اعضای هیات تحریریه

اصول اخلاقی انتشار مقاله

بانک ها و نمایه نامه ها

پیوندهای مفید

پرسش‌های متداول

فرایند پذیرش مقالات

اخبار و اعلانات

برآورد پایداری خاکدانه‌های تر از ویژگی‌های زودیافت خاک در شمال‌غرب دریاچه ارومیه

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشگاه محقق اردبیلی

2 دانش آموخته کارشناسی ارشد، گروه علوم و مهندسی خاک، دانشکده کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی، اردبیل، ایران

3 گروه مهندسی آب، دانشکده کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی، اردبیل، ایران

چکیده

اندازه­گیری مستقیم میانگین وزنی قطر (MWD) خاکدانه­های تر در آزمایشگاه کاری بسیار وقت­گیر و پرهزینه است. هدف از این پژوهش ارائه توابع رگرسیونی، شبکه عصبی مصنوعی (ANN) و نروفازی برای برآورد MWD تر در شمال غرب دریاچه ارومیه بود. در مجموع 100 نمونه خاک دست­خورده و دست­نخورده از عمق 0 تا 10 سانتی­متری اراضی کشاورزی و بایر منطقه شبستر برای تعیین برخی ویژگی­های فیزیکی و شیمیایی زودیافت خاک برداشته­شد. متغیر MWD به روش الک تر در آزمایشگاه اندازه­گیری­شد. برای آموزش توابع از 80 درصد داده­ها و برای آزمون توابع از 20 درصد داده­ها استفاده گردید. همبستگی مثبت و معنی­دار بین شن با کربن آلی (**43/0) و رس با نسبت جذبی سدیم (SAR) (**60/0) یافت شد. همبستگی مثبت و معنی­دار بین MWD با کربن آلی (**58/0) و شن (**60/0) و همبستگی منفی و معنی­دار بین MWD با رس (**48/0-) و SAR (**57/0-) تعیین گردید. نتایج توابع انتقالی نشان داد کربن آلی، شن و SAR مهم­ترین متغیرهای زودیافت در برآورد MWD بودند. مقادیر ضریب تبیین (R2)، مجذور میانگین مربعات خطا (RMSE) و میانگین خطا (ME) به­ترتیب 84/0، mm 192/0،mm  122/0- و 84/0،  mm154/0 ، mm 030/0 و 87/0،mm 215/0 و mm 161/0-  به­ترتیب برای بهترین تابع رگرسیونی، ANN و نروفازی در داده­های آزمون به­دست آمد. بنابراین توابع ANN به­دلیل داشتن RMSE پایین و ME نزدیک به صفر در مقایسه با توابع رگرسیونی و نروفازی از دقت بیشتری در برآورد MWD تر در خاک­های منطقه مورد مطالعه برخوردار بودند. 

کلیدواژه‌ها

عنوان مقاله [English]

Estimating Wet Aggregates Stability from Easily Available Soil Properties in North West of Lake Urmia

نویسندگان [English]

  • Mozhgan Hatamvand 2
  • Mahsa Hasanpour Kashani 3

2 Graduated MSc student, Department of Soil Sciences and engineering, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran.

3 Water Engineering Department, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran.

چکیده [English]

Direct measurement of mean weight diameter (MWD) of wet aggregates in the laboratory is time consuming, laborious and expensive. The objective of this study was to derive regression, artificial neural networks (ANNs) and neuro-fuzzy pedotransfer functions (PTFs) to estimate the wet MWD in the northwest of Lake Urmia. Total of 100 disturbed and undisturbed soil samples were taken from 0-10 cm soil depth for determining some readily available soil variables in bare and agricultural lands of Shabestar region. The MWD of wet aggregates was measured by wet sieving in the laboratory. The data were divided into two series, so that 80 data points were applied for training and remaining 20 data points as testing set. There were found positive and significant correlations between sand and organic carbon (OC) (0.43**) and also between clay and sodium adsorption ratio (SAR) (0.60**). There were found positive and significant correlations between the MWD with sand (0.60**) and OC (0.58**) and negative and significant correlations between the MWD with clay (-0.48**) and SAR (-0.57**). The results of PTFs showed that OC, sand and SAR were the most important readily available soil variables to estimate the MWD. The values of R2, root mean square error (RMSE) and mean error (ME) were obtained to be 0.84, 0.192 mm, -0.122 mm and 0.84, 0.154 mm, 0.030 mm and 0.87, 0.215 mm, -0.161 mmfor the best regression, ANNs and neuro-fuzzy PTFs, respectively, in estimating the MWD according to testing data set. Therefore, the performance of the ANNs in estimating the MWD was more than regression and neuro-fuzzy PTFs in the soils of studied region, since they had lower RMSE and ME values.

کلیدواژه‌ها [English]

  • Artificial neural network
  • Mean weight diameter of wet aggregates
  • Neuro-Fuzzy
  • Regression
  • Soil pedotransfer functions
مراجع
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. Water and Soil Science, 21(3):153-162. (In Persian)
Amirabedi H., Asghari Sh., Mesri T., and Balandeh N. 2016. Prediction of mean weight diameter of aggregates using artificial neural network and regression models. Applied Soil Research, 4(1): 39-53. (In Persian)
Annabi M., Raclot D., Bahri H., Bailly G.S., Gomez C., and Bissonnais Y.L. 2017. Spatial variability of soil aggregate stability at the scale of an agricultural region in Tunisia. Catena, 153: 157–167.
Asghari Sh., Roozban E., and Khodaverdiloo H. 2016. Derivation of pedotransfer functions for estimating penetration resistance, aggregate stability and parameters of van Genuchten moisture curve model in Fandoglou forest lands of Ardabil. Water and Soil Science, 26(1):129-148. (In Persian)
Azamathulla H.M., Chang C.K., Ghani A.A., Ariffin J., Zakaria N.A., and Hasan Z.A. 2009. An ANFIS-based approach for predicting the bed load for moderately sized rivers. Journal of Hydro-environment Research, 3 (1): 35–44.
Besalatpour A.A., Ayoubi S., Hajabbasi M.A., Mosaddeghi M.R., and Schulin R. 2013. Estimating wet soil aggregate stability from easily available properties in a highly mountainous watershed. Catena, 111: 72–79.
Blake G.R., and Hartge K.H. 1986a. Bulk density, In: Klute, A. (Ed). Methods of Soil Analysis. Part 1. 2nd Ed. Agronomy. Monograph. 9. Madison, WI: Soil Science Society of America; pp. 363-375.
Blake G.R., and Hartge K.H. 1986b. Particle Density. In: Klute, A. (Ed). Methods of Soil Analysis. Part 1. 2nd Ed. Agronomy. Monograph. 9. Madison, WI: Soil Science Society of America; pp. 377-382.
Danielson R, E., and Sutherland P.L. 1986. Porosity. In׃ Klute A (Ed). Methods of Soil Analysis. Part 1, 2 nd Ed. Agronomy Monograph. 9. Madison, WI: Soil Science Society of America; pp. 443-461.
Evrendilek F., Celik I., and Kilic S. 2004. Changes in soil organic carbon and other physical soil properties along adjacent Mediterranean forest, grass land, and crop land ecosystem Turkey. Journal of Arid Environments, 59: 743–752.
Gee G.W., and or D. 2002. Particle-size analysis. In: Dane J. H., and Topp G. C. (Eds.). Methods of Soil Analysis. Part 4. SSSA Book Series No. 5. Madison, WI: Soil Science Society of America; pp. 255–293.
Ghorbani M.A., Deo R.C., Kashani M.H., Shahabi M., and Ghorbani S. 2019. Artificial intelligence-based fast and efficient hybrid approach for spatial modeling of soil electrical conductivity. Soil and Tillage Research, 186: 152–164.
Hamzehpoura N., and Bogaert P. 2017. Improved spatiotemporal monitoring of soil salinity using filtered kriging with measurement errors: An application to the West Urmia Lake, Iran. Geoderma, 295: 22–33.
Hillel D. 2004. Environmental Soil Physics. New York, USA: Academic Press.
Kozak E., Pachepsky Y.A., Sokolowski S., Sokolowska Z., and Stepniewski W. 1996. A modified number-based method for estimating fragmentation fractal dimensions of soils. Soil Science Society of America Journal, 60: 1291-1297.
Marashi M., Mohammadi Torkashvand A., Ahmadi A., and Esfandyari M. 2017. Estimation of soil aggregate stability indices using artificial neural network and multiple linear regression models. Spanish Journal of Soil Science, 7(2):122-132.
Marashi M., Mohammadi Torkashvand A., Ahmadi A., and Esfandyari M. 2019. Adaptive neuro-fuzzy inference system: Estimation of soil aggregates stability. Acta Ecologica Sinica, 39: 95–101.
Mollaei M., Bashari H., Basiri M., and Mosaddeghi M.R.  2015. Soil structural stability assessment using wet-sieving method in selected rangeland sites in Isfahan province. Journal of Water and Soil Science, 18 (70):121-133. (In Persian)
Moghaddamnia A., Remesan R., Hasanpour Kashani M., Mohammadi M., Han D., and Piri J. 2009. Comparison of LLR, MLP, Elman, NNARX and ANFIS Models—with a case study in solar radiation estimation. Journal of Atmospheric and Solar-Terrestrial Physics, 71: 975–982.
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.
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.
Nelson D.W., and Sommers L.E. 1982. Total carbon, organic carbon, and organic matter. In A.L. Page et al. (Ed.) Methods of Soil Analysis. Part 2. 2nd Ed. Agron. Monogr. 9. Madison, WI: Soil Science Society of America; pp. 539–579.
Neaman A., and Singer A. 2011. The effects of palygorskite on chemical and physico-chemical properties of soils: a review. Geoderma, 123(3): 297-303.
Page A.L. 1985. Methods of Soil Analysis. Part 2. Chemical and Microbiological Methods. Agron.Monog.9. ASA and SSSA, Madison, WI.
Raheli B., Aalami M., T., El-Shafie A., Ghorbani M.A., and Deo, R.C. 2017. Uncertainty assessment of the multilayer perceptron (MLP) neural network model with implementation of the novel hybrid MLP-FFA method for prediction of biochemical oxygen demand and dissolved oxygen: A case study of Langat River. Environmental Earth Sciences, 76(14): 503.
Richards L.A. 1954. Diagnosis and improvement of saline and alkali soils. Agricultural Handbook No. 60. U.S. Salinity Laboratory Riverside, California.
Tajik F. 2004. Evaluation of soil aggregate stability in some regions of Iran. Journal of Water and Soil Science, 8 (1):107-123. (In Persian)
Yazdani A., Mosaddeghi M.R., Khademi H., Ayoubi S., and Khayamim F. 2014. Relationship between surface aggregate stability and some soil and climate properties in Isfahan province. Soil Management, 3(2): 23-31. (In Persian)
Yoder R.E. 1936. A direct method of aggregate analysis of soils and a study of the physical nature of erosion losses. Journal of American Society Agronomy, 28: 337-35.
تحقیقات کاربردی خاک
دوره 9، شماره 2
شهریور 1400
صفحه 102-115
فایل ها
سابقه مقاله
  • تاریخ دریافت: 03 اسفند 1398
  • تاریخ بازنگری: 11 مرداد 1399
  • تاریخ پذیرش: 30 مرداد 1399
  • تاریخ اولین انتشار: 01 شهریور 1400
اشتراک گذاری
ارجاع به این مقاله
آمار