استفاده از سطح ویژه برای بهبود تخمین ظرفیت تبادل کاتیونی خاک از طریق شبکه‌های عصبی مصنوعی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشگاه بوعلی سینا-همدان

2 مؤسسه تحقیقات برنج کشور

3 دانشگاه گیلان

چکیده

      ظرفیت تبادل کاتیونی (CEC) یکی از خصوصیات مهم خاک است که اندازه­گیری مستقیم  آن مشکل، وقت گیر و پر هزینه است. علی رغم تحقیقات زیاد در مورد تخمین CEC، چگونگی بهبود تخمین‌ها با معرفی متغیرهای جدید مورد بررسی کافی قرار نگرفته است. بر پایه بررسی انجام شده از منابع علمی داخلی و خارجی در هیچ تحقیقی از متغیر کمکی سطح ویژه برای تخمین  CEC استفاده نشده است. در این تحقیق 1662 نمونه خاک از نقاط مختلف استان گیلان جمع آوری گردید. رس، سیلت، شن، کربن آلی، pH و CEC برای نمونه­های فوق اندازه­گیری شدند. منحنی دانه بندی (PSD) با استفاده از بافت خاک به روش مدل اسکاگز و همکاران شبیه­سازی گردید. سپس سطح ویژه کل (TSS) و حاصل ضرب سطح ویژه جزء رس در کسر جرمی آن (SS1) از منحنی  PSDمحاسبه و برای تخمین CEC به عنوان ورودی در شبکه­های عصبی مصنوعی استفاده شدند.  همبستگی غیر خطی قوی و معنی­داری بین CEC با TSS و SS1 مشاهده شد. استفاده از TSS و SS1 در PTF ها موجب بهبود تخمین CEC گردید. SS1 بیشترین تاثیر را در تخمین CEC داشت. تقسیم داده­ها به هشت گروه بطور معنی­داری موجب بهبود عملکرد PTF ها شده و تاثیر TSS و SS1 بر تخمین CEC را افزایش داد. استفاده از این توابع انتقالی روشی آسان و مقرون به صرفه بوده و می­تواند گامی مهم در بهبود تخمین CEC خاک محسوب شود. 

کلیدواژه‌ها

عنوان مقاله [English]

Using of Specific Surface to Improve the Prediction of Soil CEC by Artificial Neural Networks

نویسندگان [English]

  • H Bayat 1
  • N Davatgar 2
  • S Moallemi 3
چکیده [English]

Cation exchange capacity (CEC) is one of the most important soil properties. Its direct measurement is difficult, costly and time-consuming. In spite of large number of researches done to predict CEC, its prediction improvement by adding new input variables, however, remains a challenging issue. To our knowledge no one has used the auxiliary variable of specific surface to predict CEC. In the present work, 1662 disturbed soil samples were collected from different parts of Guilan province. Soil properties including pH, sand, silt, clay, organic carbon, and CEC were measured. The entire particle size distribution (PSD) curve was extended from limited soil texture data. Using Skaggs et al moded. Then, total specific surface (TSS) and the product of the specific surface of clay fraction and its mass fraction (SS1) were calculated from the extended PSD curve to predict CEC by artificial neural networks. Strong nonlinear correlation was found between CEC, TSS and SS1. CEC predictions were improved by using TSS and SS1 in the PTFs. SS1 was the most important variable in the prediction of CEC. Partitioning the whole data into eight groups improved significantly the performance of the PTFs and increased the effect of TSS and SS1 in improving the CEC prediction. Using these PTFs is an easy and economical method and it would be a great step forward in improving the estimation of soil CEC.  

کلیدواژه‌ها [English]

  • Artificial Neural Networks
  • Cation exchange capacity
  • Pedotransfer functions
  • Soil specific surface

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