نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار، گروه مهندسی آب، دانشکده آب و خاک، دانشگاه زابل، زابل، ایران

2 کارشناسی ارشد، گروه مهندسی آب، دانشکده آب و خاک، دانشگاه زابل، زابل، ایران

چکیده

در ایـن پژوهش از روش زمین­آمار و روش­های هوش مصنوعی شامل شبکه عصبی مصنوعی، درخت تصمیم و ماشین بردار پشتیبان برای پیش­بینی شوری خاک اراضی قرقری شهرستان هیرمند استفاده شد. برای این منظور تعداد 130 نمونه خاک از عمق صفر تا cm 30  خاک برداشت شد. نمونه­های برداشت‌شده به آزمایشگاه منتقل و هدایت الکتریکی با استفاده از دستگاه هدایت­­سنج اندازه­گیری شد. مقادیر شوری خاک با استفاده از روش زمین­آمار و روش­های هوش مصنوعی، برآورد شد. روش­های زمـین­آمـاری و هوش مصنوعی برازش و بهترین مدل انتخاب و با استفاده از اعتبارسنجی مستقل دقت روش­ها باهم مقایسه شد. نتایج نشان داد روش­های هوش مصنوعی نسبت به روش زمین­آمار شوری خاک را بهتر برآورد می­کنند. بین روش­های هوش مصنوعی روش درخت تصمیم با توجه به ضریب تبیین 99/0 و آماره­های RMSE و MAE به­ترتیب برابر 26/0 و 18/0 به­عنوان روش برتر انتخاب شد. نتایج روند شوری نشان داد شوری خاک منطقه از غرب به شرق ابتدا کاهش و سپس افزایش و از شمال به جنوب کاهش می­یابد. بنابراین با توجه به نتایج برای حفظ محیط­زیست منطقه، باید زمینه کاشت گونه­های گیاهی سازگار با منطقه، منطبق با شوری خاک فراهم شود.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Comparison of Artificial Intelligence and Geostatistical Methods in Soil Surface Salinity Prediction in Ghorghori, Hirmand

نویسندگان [English]

  • Halimeh Piri 1
  • Mojtaba Mobaraki 2

1 Assist. Professor, Department of Water Engineering, College of Water and Soil, University of Zabol, Zabol, Iran

2 M. Sc. Alumni, Department of Water Engineering, College of Water and Soil, University of Zabol, Zabol, Iran

چکیده [English]

In this study, geostatistical methods and artificial intelligence models (artificial neural network, decision tree, and support vector machine) were used to simulate the soil salinity of Ghorghori lands in Hirmand city. A total of 130 soil samples were collected from 0-30 cm layers of the soil. The electrical conductivity of each sample was measured using an electrical conductivity device. Soil salinity values were estimated using Geostatistical methods and artificial intelligence methods. Geostatistical and artificial intelligence models were applied and the best model was selected; the accuracy of the methods was compared using independent validation. The results showed that the artificial intelligence methods outperformed the geostatistical method in estimating the soil salinity of the artificial intelligence methods, the decision tree model was the superior model due to its coefficient of determination of 0.99 and RMSE and MAE statistics of 0.26 and 0.18 respectively. The salinity trend showed that the salinity of the soil of the region decreases from west to east first and then increases and decreases from north to south. In order to preserve the environment of the region, the field of planting plant species compatible with the region should be provided in accordance with soil salinity.

کلیدواژه‌ها [English]

  • Decision Tree
  • Neural Network
  • Support Vector Machine
  • Zoning
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