نوع مقاله : مقاله پژوهشی

نویسندگان

1 کارشناسی ارشد، گروه علوم خاک، دانشکده کشاورزی، دانشگاه فردوسی، مشهد، ایران

2 دانشیار، گروه علوم خاک، دانشکده کشاورزی، دانشگاه فردوسی ، مشهد، ایران

3 دانشجوی دکتری، گروه علوم خاک، دانشکده کشاورزی، دانشگاه فردوسی مشهد، مشهد، ایران

4 استاد گروه علوم خاک، دانشکده کشاورزی، دانشگاه فردوسی مشهد،مشهد، ایران

5 استاد، گروه علوم خاک، دانشکده کشاورزی، دانشگاه فردوسی مشهد، مشهد، ایران

چکیده

افزایش تولید آلاینده‌ها از جمله فلزات سنگین یکی از مشکلات جدی و در حال گسترش جامعه بشری است. آلودگی به فلزات سنگین نه‌تنها بر خصوصیات فیزیکی و شیمیایی خاک تأثیرگذار است، بلکه برای سلامتی انسان از طریق ورود به چرخه غذایی و نفوذ به آب‌های زیرزمینی خطرناک است. مطالعه حاضر با هدف پیش‌بینی میزان سرب خاک به‌عنوان یکی از مهم‌ترین فلزات سنگین با استفاده از خصوصیات زود­یافت خاک به کمک مدل شبکه عصبی مصنوعی انجام شد. بدین منظور 63 نمونه از عمق صفر تا 30 سانتی‌متر خاک‌های مختلف واقع در حاشیه رودخانه کشف‌رود در شمال شهرستان مشهد برداشته شد. پارامترهای pH، هدایت الکتریکی، فراوانی نسبی ذرات، کربن آلی و سرب خاک اندازه‌گیری شدند. مدل شبکه عصبی مصنوعی نوع پرسپترون چندلایه برای پیش‌بینی غلظت سرب خاک مورداستفاده قرار گرفت. ارزیابی مدل با استفاده از پارامترهای آماری مانند ضریب تبیین (R2)، میانگین خطای مطلق (MAE) و همچنین مجذور میانگین مربعات خطا (RMSE) انجام شد. نتایج نشان داد که کارایی مدل شبکه عصبی مصنوعی مناسب است و می‌تواند به‌عنوان روشی دقیق جهت جایگزین شدن با روش پرهزینه و زمان‌بر اندازه‌گیری مستقیم آزمایشگاهی این فلز سنگین در خاک مورداستفاده قرار گیرد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Predicting Lead Concentration of Soil using Readily Available Properties Based on Artificial Neural Network Model

نویسندگان [English]

  • Saman Haji Namaki 1
  • Hojjat Emami 2
  • Ahmad Bazoobandi 3
  • Amir Fatovat 4
  • GholamHossein Haghnia 5

1 M.Sc., Soil Science Department, Faculty of Agriculture, Ferdowsi University, Mashhad, Iran

2 Associate Prof., Department of Soil Sciences, Faculty of Agriculture, Ferdowsi University, Mashhad, Iran

3 Ph.D. Scholar., Soil Science Department, Faculty of Agriculture, Ferdowsi University, Mashhad, Iran

4 Prof., Department of Soil Sciences, Faculty of Agriculture, Ferdowsi University, Mashhad, Iran

5 Prof., Department of Soil Sciences, Faculty of Agriculture, Ferdowsi University, Mashhad, Iran

چکیده [English]

Increased generation of pollutants such as heavy metals is one of the serious and developing environmental issues threatening human society. Heavy metal pollution not only affects the physical and chemical properties of the soil but also it is dangerous to human health through entering into the food chain and finding its way into the groundwater. The present study was conducted to predict soil lead concentration, as one of the most important heavy metals, using readily available soil properties based on artificial neural network model. For this purpose, 63 soil samples were collected from 60-cm depth of the land surrounding Kashafrud River located in Mashhad City. Measured parameters included pH, electrical conductivity, particle size distribution, organic carbon, and Pb content in soil. The multilayer perceptron (MLP) as an artificial neural network model was used to predict the Pb concentration in soil. The performance of this model was assessed by the coefficient of determination (R2), mean absolute error (MAE), and also root mean square error (RMSE). The results showed that artificial neural network model is a suitable method to determine Pb concentration in soil rather than the direct laboratory measurement, which is an expensive and time-consuming method.

کلیدواژه‌ها [English]

  • Lead
  • Artificial Neural Network
  • Heavy metal
  • Kashafrud
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