برآورد میانگین وزنی قطر خاکدانه‌ها با استفاده از مدل‌های رگرسیونی و شبکه‏ عصبی مصنوعی

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
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