نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری دانشکده آب و خاک پردیس کشاورزی و منابع طبیعی دانشگاه تهران

2 دانشیار گروه آبیاری و آبادانی دانشکده آب و خاک پردیس کشاورزی و منابع طبیعی دانشگاه تهران

3 دانشجوی کارشناسی ‌ارشد گروه مهندسی آب دانشکده کشاورزی دانشگاه تبریز

چکیده

ویژگی­های هیدرولیکی خاک همچون هدایت هیدرولیکی اشباع و غیراشباع در مطالعات زیست محیطی نقش مهمی را ایفا می­نمایند.  از آنجائی­که اندازه­گیری مستقیم این قبیل ویژگی­های هیدرولیکی خاک امری وقت­گیر و هزینه­بر است روش­های غیرمستقیمی چون توابع انتقالی و شبکه­های عصبی مصنوعی بر مبنای پارامترهای سهل الوصول خاک توسعه یافته­اند.  در این خصوص در این مطالعه، از شبکه عصبی مصنوعی به­ منظور تخمین هدایت هیدرولیکی اشباع خاک با استفاده از داده­های اندازه­گیری شده منحنی مشخصه رطوبتی خاک و جرم مخصوص ظاهری استفاده شده­ است.  با استفاده از داده­های اندازه­گیری شده جرم مخصوص ظاهری خاک، بعد فرکتالی منحنی مشخصة رطوبتی، مکش در نقطه ورود هوا، تخلخل مؤثر، مقادیر هدایت هیدرولیکی اشباع خاک با استفاده از شبکه عصبی مصنوعی تخمین زده شدند.  در مرحله آموزش مدل از 114 داده اندازه­گیری شده منحنی مشخصة رطوبتی و جرم مخصوص ظاهری خاک و در مرحله آزمون از 28 داده باقیمانده استفاده شد.  مقادیر MSE و R2 در مرحله آزمون مدل شبکه عصبی مصنوعی با چهار پارامتر ورودی به­ترتیب 0028/0 و 76/0 محاسبه شدند.  مقایسه عملکرد مدل شبکه عصبی مصنوعی با دو مدل ارائه شده توسط رائولز و همکاران نشان داد که مدل شبکه عصبی مصنوعی با دقت بالاتری هدایت هیدرولیکی اشباع خاک را پیش‌بینی می­نماید.   

عنوان مقاله [English]

Application of Artificial Neural Networks in Prediction of Saturated Hydraulic Conductivity Using Soil Physical Parameters

چکیده [English]

Soil hydraulic properties such as saturated and unsaturated hydraulic conductivity play an important role in environmental research. Since direct measurement of these soil hydraulic properties is time-consuming and costly, indirect methods such as pedotransfer functions and artificial neural networks (ANN) were developed based on readily available parameters. In this study, the use of ANN to predict saturated hydraulic conductivity using a measured soil moisture curve and bulk density was investigated. Measured bulk density and soil moisture curves were used to estimate saturated hydraulic conductivity from calculated fractal dimensions, air entry values, bulk density and effective porosity using ANN. In the training and testing steps of ANN, 114 and 28 measured soil samples were used, respectively. R2 and MSE were 0.76 and 0.0028, respectively, for the ANN method with four inputs. A comparison of ANN with Rawls et al. (1993, 1998) models showed that the neural network more accurately predicts saturated hydraulic conductivity.

کلیدواژه‌ها [English]

  • Artificial Neural Network
  • Saturated hydraulic conductivity
  • soil moisture curve
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