Estimating the parameters of Philip infiltration equation using artificial neural network

Document Type : Full Article


Department of Irrigation, College of Agriculture, Shiraz University, Shiraz, I. R. Iran


Infiltration rate is one of the most important parameters used in irrigation water management. Direct measurement of infiltration process is laborious, time consuming and expensive. Therefore, in this study application of some indirect methods such as artificial neural networks (ANNs) for prediction of this phenomenon was investigated. Different ANNs structures including two training algorithms (TrainLM and TrainBR), two transfer functions (Tansig and Logsig), and different combinations of the input variables such as sand, silt, and clay fractions, bulk density (BD), soil organic matter (SOM), cumulative infiltration (CI) and elapsed time were used to predict sorption coefficient (S) and hydraulic conductivity (A) in Philip equation (I=S*t0.5+A*t), which corresponded to 30 soil samples from study areas located in the Agricultural College, Shiraz University, (Bajgah). A two-hidden layer ANNs with two and three neurons in the hidden layers, respectively and TrainLM algorithm performed the best in predicting S when Logsig and Tansig were used. Silt+ clay+ sand+ time+ CI combination was the most basic influential variables for the S prediction. Furthermore, a two-hidden layer ANNs with two and three neurons in the hidden layers, respectively and TrainBR algorithm performed the best in predicting A when Tansig and Tansig were used. Silt +clay +sand +BD + OM+ time+ CI combination was the most basic influential variables for A prediction. Results showed that increasing the hidden layers and input variables significantly improved the ANNs performance. The coefficient of determination (R2) confirmed that the ANNs predictions for A (84.6 %) fit data better than S (77.5 %).


Article Title [Persian]

شبکه عصبی مصنوعی هدایت هیدرولیکی معادله فلیپ ضریب جذب نفوذ آب

Authors [Persian]

  • نازنین ابریشمی شیرازی
  • علیرضا سپاسخواه
بخش مهندسی آب ، دانشکده کشاورزی، دانشگاه شیراز، شیراز، ج. ا. ایران
Abstract [Persian]

نفوذپذیری آب در خاک یکی از مهم‌ترین پدیده‌های فیزیکی خاک است. روش‌های تجربی تعیین معادله‌های نفوذ، نیازمند انجام آزمایش‌های زمان بر و پرهزینه است، لذا در این پژوهش از روش غیرمستقیم شبکه عصبی مصنوعی برای تخمین مقادیر ضریب جذب (S)  و فاکتور انتقال(A)  معادله فیلیپ استفاده شد. ساختارهای مختلف شبکه عصبی مصنوعی متشکل از الگوریتم های آموزش TrainLM  و TrainBR و توابع انتقال لوگ سیگموئید و تانژانت سیگموئید برای لایه‌های میانی و تابع تبدیل خطی برای لایه خروجی و ترکیبات متفاوتی از ورودی‌ها، شامل مقادیر نفوذ تجمعی و زمان‌های مربوط به هرکدام، به‌عنوان ورودی ثابت و درصد شن، درصد سیلت، درصد رس، چگالی ظاهری و ماده آلی به عنوان ورودی‌های متغیر، برای 30 نقطه در دانشکده کشاورزی واقع در منطقه باجگاه بررسی گردید. برای تخمین ضریب جذب بهترین ساختار دارای دو لایه مخفی و 3 ورودی (درصد شن، درصد سیلت و درصد رس) با دو نرون در لایه اول و سه نرون در لایه دوم و الگوریتم آموزش TrainLM بود. برای تخمین فاکتور انتقال بهترین ساختار دارای دو لایه مخفی و 5 ورودی (چگالی ظاهری، مقدار ماده آلی، درصد شن، درصد سیلت و درصد رس) با دو نرون در لایه اول و سه نرون در لایه دوم و الگوریتم آموزش Train BR بود. افزایش تعداد لایه‌های مخفی و تعداد ورودی‌ها تاثیر به سزایی در بهبود نتیجه داشت و شبکه عصبی در تخمین مقادیر فاکتور انتقال عملکرد بسیار بهتری نسبت به ضریب جذب را نشان داد. مقدار ضریب تعیین (R2) نشان داد که پیشبینی های شبکه عصبی برای A (% 6/84) بهتر از S (% 5/77) می‌‌باشد.

Keywords [Persian]

  • شبکه عصبی مصنوعی
  • نفوذ آب
  • معادله فلیپ
  • ضریب جذب
  • هدایت هیدرولیکی
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