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کاربرد شبکه عصبی مصنوعی در پیشبینی هدایت هیدرولیکی اشباع با استفاده از پارامترهای فیزیکی خاک | ||
| تحقیقات مهندسی صنایع غذایی | ||
| مقاله 7، دوره 10، شماره 1، خرداد 1388، صفحه 97-112 اصل مقاله (512 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| نویسندگان | ||
| بهزاد قنبریان علویجه* 1؛ عبدالمجید لیاقت2؛ سمانه سهرابی3 | ||
| 1دانشجوی دکتری دانشکده آب و خاک پردیس کشاورزی و منابع طبیعی دانشگاه تهران | ||
| 2دانشیار گروه آبیاری و آبادانی دانشکده آب و خاک پردیس کشاورزی و منابع طبیعی دانشگاه تهران | ||
| 3دانشجوی 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 | ||
| مراجع | ||
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