ارزیابی روش های رگرسیون درختی و خطی چندگانه در برآورد ظرفیت تبادل کاتیونی

نوع مقاله : مقالات پژوهشی

نویسندگان

• یاسر استواری ¹
• کامران عسگری ²
• حمیدرضا متقیان ¹

¹ دانشگاه شهرکرد

² دانشگاه آزاد اسلامی، واحد خوراسگان(اصفهان)، باشگاه پژوهشگران جوان، اصفهان، ایران

https://doi.org/10.22067/jsw.v0i0.31522

چکیده

با برآورد ظرفیت تبادل کاتیونی با استفاده از ویژگی‏های پایه ای و زودیافت خاک می‏توان در وقت و هزینه صرفه‏جویی کرد. هدف این بررسی، مقایسه دو روش رگرسیون درختی و رگرسیون خطی چندگانه در برآورد ظرفیت تبادل کاتیونی (CEC) با استفاده از ویژگی‏هایخاک است. برای این منظور از داده های 106 نمونه خاک UNSODA استفاده شد. جهت برآورد CEC با استفاده از روش‏های رگرسیون درختی و رگرسیون خطی از ویژگی های اجزای بافت خاک،pH، ماده آلی و چگالی ظاهری استفاده شد. کارآیی روش رگرسیون درختی در برابر روش رگرسیون خطی چندگانه در برآورد CEC مقایسه شدند. نتایج نشان داد که در برآورد CEC با استفاده از روش های رگرسیونی تنها ضرایب ماده آلی (183/3) و درصد رس (274/0) که بیشترین همبستگی را با CEC دارند معنی دار شده و مدل رگرسیونی بر اساس این دو پارامتر توسعه یافت. همچنین از میان پارمترهای ورودی در روش رگرسیون درختی تنها پارامترهای ماده آلی و درصد رس در درخت رگرسیون ظاهر شد. روش رگرسیون درختی در دو مجموعه داده صحت سنجی و اعتبار سنجی بر اساس آماره های ارزیابی R2، RMSE، ME و GMER کارآیی بالاتری نسبت به روش های رگرسیونی خطی در برآورد CEC داشت. از میان روش های رگرسیونی خطی، مدل پیشنهادی کارآیی بالاتری نسبت به مدل های بل و ونکولن و بروسما و همکاران داشت.

کلیدواژه‌ها

• توابع انتقالی
• درخت رگرسیون
• CEC
• UNSODA

20.1001.1.20084757.1394.29.3.16.3

عنوان مقاله [English]

Assessment of Tree and Multiple Linear Regressions in Estimation of Cation Exchange Capacity

نویسندگان [English]

• Y. Ostovari ¹
• K. Asgari ²
• H. R. Motaghian ¹

¹ Shahrekord University

² Khorasgan (Isfahan) Branch, Islamic Azad University

چکیده [English]

Introduction: Estimation of cation exchange capacity (CEC) with reliable soil properties can save time and cost. Pedotransfer function (PTF) is a common method in estimating certain soil properties (e.g. CEC) that has been wieldy used for many years. One of the common techniques that have been used to develop PTFs is multiple linear regressions. In this method, all easily obtained soil properties are linearly related to certain soil properties. In addition to multiple linear regressions method, more complex techniques such as artificial neural networks and regression tree have been used to develop PTFs. The regression tree method is a well-known method for analyzing the environmental science which determines optimal separation point of independent variables.The purposes of this study were to evaluate and compare tree and multiple linear regressions in estimating cation exchange capacity with reliable soil properties.
Materials and Methods: For this work, 106 soil samples of Unsaturated Soil hydraulic database (UNSODA), which contain a wide range of soil texture classes, were used. The examples were divided into 2 sets including 81 and 25 soil samples for developing and validating multiple linear regression and tree regression, respectively. For estimating CEC with tree and multiple regressions, soil texture properties, organic matter, pH and bulk density were used. To develop multiple linear regressions and create the tree structure, at first, correlation between cation exchange capacity with other soil properties were evaluated; then, soil properties that had significant correlation were chosen to introduce software. As well, the suggested linear function and tree structure were compared with 2 famous pedotranser functions including Bell and Van-kolen and Breeuwsma et al., which have been used for estimating CEC.For investigating the performance of multiple linear regression and tree regression to estimate CEC 1:1 lines, determination coefficient (R2), mean error (ME), root mean square error) RMSE), and geometric mean error (GMER) were used. Statistica 8.0 software that was developed by ESRI was used to develop multiple linear regressions and generate tree structure.
Results and Discussion: The results showed for developing multiple linear regression model to estimate CEC among all inputs parameters (sand, silt, clay, organic matter, pH and bulk density) only just two parameters including organic (with r=0.70) and clay percentage (with r=0.59) had a significant coefficient, so organic and clay percentage appeared, and suggested multiple linear regression models based on this two parameters, with coefficient of 3.183 and 0.274, respectively, were developed. Also, only organic matter and clay percentage from inputs parameter in tree were shown. In tree structure most nods were divided into 2 Childs nods based on organic matter and only in the left side of tree structure in the second level clay percentage was appeared. Regression tree in two data sets (validation and development) based on R2, RMSE, ME and GMER had a high quality for CEC estimation than regression methods. Proposed linear regression model had high performance than Bell and Van-kolen and Breeuwsma et al. to estimate CEC.
Conclusions: The main aim of this study was to investigate the efficiency of multiple linear regression model and regression tree to predict cation exchange capacity (CEC) based on relationships between CEC and easily measurable soil properties. For this work, 106 soil samples of UNSODA data set were used. Results showed that just clay percentage and organic matter that had higher correlation with CEC appeared in suggested linear regression and tree structure. Based on 1:1 lines, R2 ,RMSE, ME and GMER, tree regression model had higher performance than all linear regression models (suggested function , Bell and Van-kolen and Breeuwsma et. al.) to estimate cation exchange capacity. As well, suggested function had more efficiency than Bell and Van-kolen and Breeuwsma to predict CEC.

کلیدواژه‌ها [English]

• Cation exchange capacity
• Regression tree
• Transfer Function
• UNSODA

مراجع

1- Abbasi Y., Ghanbarian-Alavijeh B., Liaghat A.M., and Shorafa M. 2011. Evaluation of pedotransfer functions for estimating soil water retention curve of saline and saline-alkali soils of Iran. Pedosphere,21(2): 230–237.

2- Bell M.A., and Van Kulen H. 1995. Soil pedotransfer function for four Mexican soils. Soil Science Society of American Journal, 59: 865- 871.

3- Bouma J. 1989. Using soil survey data for quantitative land evaluation. Advance Soil Science, 9:177–213

4- Breeuwsma A., Wosten J.H.M., Vleeshouwer J.J., Van Slobbe A.M., and Bouma J. 1986. Derivation of land qualities to assess environmental problemsfrom soil surveys. Soil ScienceSocietyof AmericanJournal, 50:186-190.

5- Dehghani Baniani s, Ghorbani Dashtaki .Sh, Mohamadi J., and Khodaverdilo H. 2011. Comparing the Performance of Multiple Linear Regression and Regression Tree to Predict Saturated Hydraulic Conductivity and the Inverse of Macroscopic Capillary Length. Iranian water research, 9:193-204.(In Persian with English abstract).

6- Foladmand H.R. 2008. Estimation of CEC using soil properties. Jornal of agricultural science and environmente, 1:1-8. (In Persian).

7- Herbst M., and Diekkruger B. 2002. Statistical regression. Chapter 1 In: Vereecken H and Herbst M (Editors). Developments in Soil Science. Volume 30. Development of pedotransfer functions in soil hydrology. Elsevier Science, Pp: 3-18.

8- Hezarjaribi A., Nosrati Karizak F., Abdollahnezhad K., and Ghorbani Kh. 2013. The Prediction Possibility of Soil Cation Exchange Capacity by Using of Easily Accessible Soil Parameters. Journal of Water and Soil, 27(4):712-719. (in Persian with English abstract)

9- Horn R., Fleige F.H., Richter E. A., Czyz A. Dexter E., Diaz-Pereira Damitru R., Enarche, F., Mayol K., Rajkai D., Delarosa and Simota C. 2005. SIDASS project 5: Prediction of mechanical strength of arable soils and its effects on physical properties atvarious map scales. Soil and Tillage Research, 82:47-56.

10- Keller A., Von Steiger B., Van der Zee S.T., and Schuline R. 2001. A stochastic empirical model for regional heavymetal balances in agroecosystems.Journal of Environtal Quality, 30:1976-1989.

11- Keshavarzi A., Sarmadian F., and Labbafi R. 2011. Modeling of Soil Cation Exchange Capacity Based on Fuzzy TableLook-up Scheme and Artificial Neural Network Approach. Modern Applied Science, 5(1):153-165.

12- Krogh L., Madsen H.B., and Greve M.H. 2000. Cation exchange capacitypedotransfer functions for Danish soils. Acta AgriculturalScienceand Sect. B, Soil and Plant Science, 50:1–12.

13- Manrique L.A., Jones C.A., and Dyke P.T. 1991. Predicting cation exchange capacity from soil physical andchemical properties. Soil Science Society of American Journal, 55:787-794.

14- McBratney A.B., Minasny B., Cattle S.R., and Vervoort R.W. 2002. From pedotransfer function to soil inference systems. Geoderma, 93:225-253.

15- McKenzie N.J., Jacquier D.W.1997. Improving the Field Estimation of SaturatedHydraulic Conductivity in Soil Survey. Australian Journal of Soil Research, 35:803-825.

16- Memarianfar M. and Beigi H. 2008. Comparison of artificial neural network and regressionpedotransfer functionsmodels for prediction of soil cation exchange capacity in Chaharmahal - Bakhtiari province. Water and soil science, 23(4):90-99. (In Persian with English abstract).

17- Mehrabanian M., mehrjedi R., and Dehghani F. 2009. Assessing the Efficiency of Pedotransfer Functions for Estimating CECin some Calcareous and Gypsiferous Soils of Yazd Province. Jornal of Water and Soil Conservation, 17(1):113-129. (in Persian with English abstract)

18- Mirkhani R., ShabanPoor Shahrestani M.V. and Saadat S. 2004. Prediction of soil retention curve using PTFs. Joournal of Agriculture science, 15(3):151-162.(In Persian).

19- Pachepsky Y., and Schaap M. G.,(Editors).2004. Data mining and exploration techniques. Chapter 2 In: Developments in Soil Science. Volume 30. Development of pedotransfer functions in soil hydrology. Elsevier Science, Pp: 21-31.

20- Pachepskya Y.A., and Rawls W. J. 2006. Hydropedology and pedotransfer functions. Geoderma, 131:308– 316.

21- Schaap M. G. 2004. Accuracy and uncertainty in PTF predictions. Chapter 3 In:Pachepsky Y and Rawls W.J (Editors). Developments in Soil Science. Volume 30. Development of pedotransfer functions in soil hydrology. Elsevier Science, Pp: 33-46.

22- Shirani H., and Rafieinejad N. 2011. Prediction of Some Difficult-to-measure Soil properties in Kerman state soils using regrestion and artifitial nural networks. Iranian Journal of Soil Research, 25(4):349-359. (In Persian).

23- StatSoft .2011. STATISTICA (data analysis software system). Version 10. www.statsoft.com.

24- Wosten J.H.M., Pachepsky Y.A., and Rawls W.J. 2001. Pedotransfer functions: bridging the gap between available basic soil data and missing soil hydraulic characteristics. Journal of Hydrology, 251: 123-150.