پیش بینی ظرفیت تبادل کاتیونی خاک های ایران با استفاده از روش های گوناگون

نوع مقاله: مقاله پژوهشی

نویسندگان

1 استادیار دانشکده کشاورزی و منابع طبیعی دانشگاه اردکان

2 استاد گروه مهندسی علوم خاک، پردیس کشاورزی و منابع طبیعی دانشگاه تهران

3 استادیار دانشکده کویرشناسی دانشگاه سمنان

4 استادیار گروه مهندسی علوم خاک دانشگاه شهید باهنر کرمان

چکیده

اندازه گیری ظرفیت تبادل کاتیونی خاک در سطوح وسیع، بسیار پرهزینه و وقت گیر است. برآورد این خصوصیت به­وسیله ویژگی‌های زودیافت خاک، از طریق روش‌های پارامتریک و غیرپارامتریک (توابع انتقالی) می‌تواند رویکرد مناسبی باشد. در این پژوهش، روش پارامتریک رگرسیون‌های خطی و غیر خطی و روش‌های غیر پارامتریک شبکه‌های عصبی مصنوعی، رگرسیون درختی و فازی عصبی در تخمین ظرفیت تبادل کاتیونی خاک مورد استفاده قرار گرفت. برای این منظور 1770 نمونه خاک از مناطق مختلف ایران انتخاب شدند که 1414 عدد برای آموزش و 356 عدد از آن­ها به عنوان داده­های آزمون مدل­ها استفاده شدند. بررسی همبستگی‌ها نشان داد که پارامترهای رس و درصد ماده آلی خاک بیش­ترین ارتباط را با ظرفیت تبادل کاتیونی خاک دارند؛ بنابراین این ویژگی­ها به عنوان متغیر مستقل ورودی (ویژگی زود­یافت) و ظرفیت تبادل کاتیونی به عنوان متغیر وابسته خروجی انتخاب شدند. ریشه میانگین مربعات خطا و ضریب تبیین در روش رگرسیون خطی برابر 74/4 و 50/0 و در روش رگرسیون غیر خطی برابر 71/4 و 52/0 بود که نشان می‌دهد که هر دو روش با دقت نسبتاً خوب و یکسانی قادر به پیش‌بینی ظرفیت تبادل کاتیونی خاک می‌باشند؛ همچنین نتایج نشان داد که روش رگرسیون غیر خطی فقط سبب بهبود 6/0 درصدی دقت پیش‌بینی ظرفیت تبادل کاتیونی خاک شده است. نتایج نشان داد که استفاده از روش‌های شبکه عصبی مصنوعی سبب بهبود معنی‌داری در دقت برآورد ظرفیت تبادل کاتیونی خاک نمی‌شود. بیش­ترین بهبود در پیش‌بینی مدل به نسبت توابع انتقالی خطی در روش شبکه عصبی پس انتشار مشاهده شد. این روش سبب بهبود 3 درصدی پیش‌بینی ظرفیت تبادل کاتیونی خاک گردید. دقت برآورد روش درخت تصمیم، اندک بهتری از روش‌های شبکه عصبی مصنوعی بود. بهبود نسبی این روش نسبت به رگرسیون خطی برابر با 4/4 درصد بود؛ اما بیشترین بهبود نسبی در روش فازی عصبی مشاهده شد. این روش سبب کاهش 15 درصدی خطا به نسبت معادلات رگرسیونی خطی گردید؛ لذا این نتایج نشان می­دهد که یکی از  بهترین روش­ها در پیش‌بینی ظرفیت تبادل کاتیونی خاک‌های ایران، روش فازی عصبی می­باشد. 

کلیدواژه‌ها


عنوان مقاله [English]

Prediction Cation Exchange Capacity using Different Methods in Soils of Iran

نویسندگان [English]

  • R Taghizadeh-Mehrjardi1 1
  • F Sarmadian 2
  • A. A Zolfaghari 3
  • A. Jafari 4
چکیده [English]

Introduction: Cation exchange capacity (CEC) has long been input parameter of many environmental models (Manrique et al., 1991). Added to this, CEC data can give more clear and complete interpretation of soil, plant nutrition process and consequently fertilizer and soil amendment requirements. Laboratory analysis is the most accurate method for direct measurement of CEC. However, direct measurement of CEC is difficult, particularly in the soils of arid and semi-arid regions of Iran, due to large amounts of calcium carbonate that makes measuring expensive, laborious, and time-consuming (Amini et al., 2005). It can be an appropriate approach to predict CEC from readily available properties via developing nonparametric or parametric methods (Minasny et al., 1999). Therefore, the objectives of this study were to compare and apply different data mining approches including multi-linear regression (MLR), multi-nonlinear regression (MNR), cascade neural network (CNN), two radial base functions (RBF), multi-layer perceptron neural network (MLP), and adaptive neuro-fuzzy inference system (ANFIS) to estimate cation exchange capacity in different soils of Iran.
Materials and Methods: For this purpose, 1770 soil samples were selected from different sites in Iran from which 356 samples were used as the testing data, and the remaining 1414 soils were employed as the training. The soil samples were dried, crushed and passed through a 2 mm sieve to prepare for physical and chemical analyses. The percentages of sand (50 -2000 mμ), silt (2-50 mμ) and clay (<2μm) were determined using the hydrometer method according to USDA soil textural classification system. The soil organic carbon was determined using Walkly-Black method and the CEC was measured by the standard method. Then the data mining techniques (i.e. MLR, MNR, CNN, RBF, MLP, ANFIS) were applied to predict CEC from readily available data (i.e. soil organic carbon and clay percentages). Finally, to compare efficiencies of these techniques, different error criteria including root mean square error (RMSE), mean error (ME), coefficient of determination (R2) and relative improvement (RI) were applied. In the present research, an effort was made to calculate the uncertainty of pedotransfer functions using Monte Carlo technique.
Results and Discussion: Statistical analyses indicated the soil organic matter and soil texture have the highest variation. For example, variation of SOM has ranged from 0.01 to 2.94. Investigation of correlation coefficients shows that CEC is more related to the parameters, clay and soil organic matter content. Thus, the parameters, clay, silt, sand and organic carbon content were the input independent variables (readily available properties), and the CEC was an output dependent variable in this study. Root mean square error (RMSE) of linear and nonlinear regression was 4.74 and 4.71 meq 100g-1, respectively. This indicates that both methods are able to properly and equally predict CEC. Nonlinear recession equation increased the accuracy of prediction by 0.6 %. Results show that nonparametric artificial neural networks do not increase the accuracy of prediction CEC, significantly. The best result of neural networks was obtained using MLP. Nonparametric regression tree accuracy was slightly better than artificial neural network methods (4.53 and 4.61 meq 100g-1, respectively). The best method for prediction of CEC was ANFIS (RMSE=4.02 meq 100g-1). The accuracy of prediction using this method was 15 % more than linear regression. Moreover, the ANFIS model on the partitioned data by fuzzy k-means cloud enhances the prediction accuracy up to 26%. Monte Carlo results indicate the highest and lowest uncertainty belongs to MLR and ANFIS models, respectively.
Conclusion: In the present research, different data mining techniques were applied to predict CEC in various ranges of soils. The data base related to 1770 soil samples was gathered from all over Iran. Results of the comparison indicate the highest prediction accuracy belongs to ANFIS model. Moreover, partitioning the data base to four groups enhances the accuracy of models. This result confirms that pedotransfer functions are more reliable only on the range of existing data. Overall, our efforts resulted only in R2 of 0.58. This means that soil organic matter and clay percentage could only model the 58% CEC variation. This suggests we should incorporate more input data including kind of clay mineral, percentage of calcium carbonate, gypsum, and etc.

کلیدواژه‌ها [English]

  • Cation Exchange Capacity
  • Pedotransfer function
  • Neural Network
  • Neuro-fuzzy
  • Regression tree
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