کاربرد روش درختان تصمیم‌گیری تصادفی در پیش‌بینی کلاس‌های خاک در اراضی با پستی و بلندی کم ( مطالعه موردی: شهرستان هیرمند)

نوع مقاله: مقاله پژوهشی

نویسندگان

1 گروه علوم و مهندسی خاک، دانشگاه زابل، زابل، ایران

2 گروه علوم خاک، دانشگاه زابل

3 بخش تحقیقات خاک و آب، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی سیستان، سازمان تحقیقات، آموزش و ترویج کشاورزی، زابل، ایران

چکیده

سابقه و هدف: شناسایی و نقشه برداری خاک، به عنوان روشی برای تعیین الگوی پراکنش خاک، توصیف و نمایش آن به شکل قابل فهم و تفسیر برای کاربران مختلف، پایه و اساس اطلاعات خاک برای مدل سازی های محیطی می باشد. نقشه‌برداری رقومی خاک شامل ایجاد ارتباط بین کلاس‌ها یا خصوصیات خاک با فاکتورها‌ی محیطی دخیل در تشکیل و تکامل خاک با استفاده از مدل‌ها‌ی ریاضی است که می‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌تواند نقشه‌ها‌‌‌ی خاک دقیق‌تر و یکدست‌تر در زمان کمتر با ارائه میزان دقت و صحت ایجاد نماید و باعث کاهش هزینه‌های پروژه‌های شناسایی و تهیه نقشه‌های خاک گردد. این پژوهش جهت تهیه نقشه کلاس‌‌ها‌‌‌ی گروه‌های بزرگ و زیرگروه‌های خاک با استفاده از تکنیک درختان تصمیم گیری تصادفی در اراضی شهرستان هیرمند در دشت سیستان انجام گرفت.
مواد و روش‌ها: در این مطالعه 108 پروفیل خاک در سطحی حدود 60000 هکتار از اراضی شهرستان هیرمند حفر گردید. 16متغیر محیطی شامل خصوصیات زمین، شاخص شوری و شاخص پوشش گیاهی به عنوان تخمین‌گر برای تهیه نقشه خاک، مورد استفاده قرار گرفته شدند. پس از تعیین گروه‌های بزرگ و زیرگروه‌های خاک، نقشه کلاس‌‌ها‌‌‌ی خاک با استفاده از روش درختان تصمیم گیری تصادفی (RF) تهیه شد. شایان ذکر است که 80 درصد داده در آموزش مدل و 20 درصد برای اعتبارسنجی مستقل استفاده شدند
یافته‌ها: نتایج مطالعات خاکشناسی نشان داده که خاک‌های تشکیل شده در دشت سیستان تکامل زیادی نداشتند و عمدتا در رده‌های انتی‌سول و اریدی‌سول قرار دارند. بیش‌ترین تعداد خاکرخ در گروه‌های بزرگ مربوط به Torrifluvents، و بیش‌ترین تعداد خاکرخ در زیرگروه‌های بزرگ مربوط به Typic Torrifluvents بود. همچنین نتایج روش RF نشان داد که کمترین مقدار خطای تخمین نمونه‌های خارج از سبد در گروه‌های بزرگ و زیرگروه‌های خاک به ترتیب53/43 و 59/50 بود. نتایج اعتبار سنجی مستقل نشان داد که بهترین دقت بدست آمده برای گروه‌های بزرگ و زیرگروه‌های بزرگ خاک به ترتیب 48 و 53 درصد بود. بین متغیرهای مختلف محیطی عمق شیارها، شاخص همگرایی، شبکه کانال‌ها و شوری در گروه‌های بزرگ خاک و عمق شیارها، ارتفاع و سطح حوزه در زیرگروه‌های خاک دارای بیشترین اهمیت در تخمین کلاس‌های خاک بودند.
نتیجه‌گیری: نتایج نشان داد که در مناطق خشک با پستی و بلندی کم خاک‌ها عمدتا جوان هستند و همچنین در این مناطق تنوع خاک کم است. در چنین مناطقی روش نقشه‌برداری رقومی و تکنیک درختان تصمیم گیری تصادفی می‌تواند برای پیش‌بینی کلاس‌های خاک و تهیه نقشه‌های خاک بسیار مفید بوده و مورد استفاده قرار گیرد.
کلمات کلیدی: نقشه‌برداری رقومی خاک، تکنیک درختان تصمیم‌گیری تصادفی، دقت نقشه، مناطق خشک، دشت سیستان

کلیدواژه‌ها

عنوان مقاله [English]

Application of Random Forest method for predicting soil classes in low relief lands (case study: Hirmand county)

نویسنده [English]

  • Ali Shahriari 2

چکیده [English]

Abstract
Background and Objectives: Base of soil information for environmental modeling is soil survey and mapping as a way to determine soil distribution patterns, describe and display it to understood and interpreted for different users. Digital soil mapping creates link between classes or soil characteristics and environmental factors affected soil formation and development by using mathematical models which can provide more precise and accurate soil maps and reducing cost of soil survey and mapping projects. This study was done to mapping soil great groups and subgroups by using Random Forest technique in the Hirmand county lands in Sistan plain.
Materials and Methods: In this study 108 soil profiles were dug on about 60.000 hectares of Hirmand county lands. Sixteen environmental variables were used as estimator for soil mapping including land properties, salinity and vegetation index. After classification of soil profiles to great groups and subgroups, soil classes map provided by using random forest (RF) method. It should be mentioned 80 percent of data was used for model training and 20 percent for independent validation.
Results: Pedological studies showed soils that formed in Sistan plain haven’t high development and most of them are Entisol and Aridisol. Most soil profiles classified as Torrifluvents on great groups and Typic Torrifluvents as subgroups. Also the result of RF showed the lowest estimation error of out of bag (OOB) samples in soil great groups and subgroups were 43.53 and 50.59 respectively. Independent validation results showed the best accuracy obtained for soil great groups and subgroups were 48 and 53 percent respectively. Grooves depth, convergence index, channel network between and salinity in soil great groups and grooves depth, elevation and catchment area in soil subgroups were the most important environmental variables to estimate soil classes.
Conclusion: The results showed most soils are young in the low relief lands in arid regions and these regions have also low soil diversity. Soil digital mapping and random forest technique could be useful for soil classes prediction and soil mapping in this kind of regions.
Results: Pedological studies showed soils that formed in Sistan plain haven’t high development and most of them are Entisol and Aridisol. Most soil profiles classified as Torrifluvents on great groups and Typic Torrifluvents as subgroups. Also the result of RF showed the lowest estimation error of out of bag (OOB) samples in soil great groups and subgroups were 43.53 and 50.59 respectively. Independent validation results showed the best accuracy obtained for soil great groups and subgroups were 48 and 53 percent respectively. Grooves depth, convergence index, channel network between and salinity in soil great groups and grooves depth, elevation and catchment area in soil subgroups were the most important environmental variables to estimate soil classes.
Conclusion: The results showed most soils are young in the low relief lands in arid regions and these regions have also low soil diversity. Soil digital mapping and random forest technique could be useful for soil classes prediction and soil mapping in this kind of regions.
Keywords: Soil digital mapping, Random forest technique, Map accuracy, Arid regions, Sistan plain

کلیدواژه‌ها [English]

  • Soil digital mapping
  • Random forest technique
  • Map accuracy
  • Arid regions
  • Sistan plain
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