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انبوهش‌زدایی واحدهای نقشه خاک با استفاده از مدل دسمارت: ترکیب مدل‌های مبتنی بر سیستم درختی و داده‌های جدید خاک‌رخی | ||
| تحقیقات آب و خاک ایران | ||
| دوره 56، شماره 6، شهریور 1404، صفحه 1609-1629 اصل مقاله (2.13 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijswr.2025.391334.669894 | ||
| نویسندگان | ||
| زهرا رسائی1؛ فریدون سرمدیان* 2؛ اعظم جعفری3 | ||
| 1گروه علوم و مهندسی خاک، دانشکده کشاورزی، دانشکدگان کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران | ||
| 2عضو هیأت علمی گروه مهندسی علوم خاک، پردیس کشاورزی و منابع طبیعی دانشگاه تهران | ||
| 3بخش علوم و مهندسی خاک، دانشکده کشاورزی-دانشگاه شهیدباهنر کرمان | ||
| چکیده | ||
| نقشه‌های مرسوم خاک بصورت واحدهای چندضلعی می‌باشند که در آن‌ها، واحدهای خاک با مرزهای مشخص از یکدیگر تفکیک شده‌اند اما تغییرات کلاس‌های خاک در واحدها مشخص نمی‌باشد. با توجه به نیاز به اطلاع از تغییرات کلاس‌های خاک در واحدهای نقشه، هدف این مطالعه انبوهش‌زدایی واحدهای نقشه خاک با استفاده از روش دسمارت (DSMART) در منطقه آبیک می‌باشد. مدل دسمارت براساس مدل‌های درخت C5.0، جنگل تصادفی و تقویت گرادیان افراطی در دو سناریو انجام شد: (1) استفاده از اطلاعات واحدهای نقشه خاک موروثی یک میلیونیم کشور و (2) با اطلاعات 230 خاک‌رخ جدید در سطح زیرگروه‌های خاک. عملکرد مدل‌ها و عدم قطعیت آن‌ها با شاخص‌های کمی ارزیابی شدند. در سناریوی اول، میزان صحت کلی نقشه‌ها بین 29/0 تا 37/0 و مقدار کاپا بین 17/0 تا 29/0 متغیر بود که بهترین نتایج از مدل تقویت گرادیان افراطی با شاخص درهمی 74/0 بدست آمد. در سناریوی دوم، در مدل جنگل تصادفی صحت کلی نقشه‌ها از 51/0 تا 63/0 و کاپا از 44/0 تا 60/0 افزایش یافت و شاخص درهمی به 65/0 کاهش یافت. مقایسه این نقشه‌ها با توزیع مکانی زیرگروه‌های خاک منطقه بیانگر تطبیق خوب نقشه‌ها با هم بود، به‌طوریکه در سناریوی دوم به میزان 43 درصد افزایش نشان داد. در سناریوی اول، متغیرهای توپوگرافی، و در سناریوی دوم، میانگین بارندگی و شاخص پوشش گیاهی عمودی بیشترین اهمیت را در مدل‌سازی داشتند. ترکیب داده‌های جدید خاک‌رخی صحت مدل‌سازی را تا 26 درصد افزایش داد. این نتایج کارایی روش دسمارت با اطلاعات خاک‌رخی اضافی در انبوهش‌زدایی واحدهای نقشه خاک موروثی را تأیید می‌کنند. | ||
| کلیدواژه‌ها | ||
| دسمارت؛ ریزمقیاس‌سازی؛ نقشه‌برداری رقومی خاک؛ یادگیری ماشین | ||
| عنوان مقاله [English] | ||
| Improving the Disaggregation of Soil Map Units Using the DSMART Method: Integrating Tree-Based Models and New Soil Profile Data | ||
| نویسندگان [English] | ||
| zahra rasaei1؛ Fereydoon Sarmadian2؛ Azam Jafari3 | ||
| 1Soil Science Department, Faculty of Agricultural, University College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran | ||
| 2soil science department< faculty of agricultural engineering and technology, university of Tehran | ||
| 3Department of Soil Science, Faculty of Agriculture, Shahid Bahonar University of Kerman | ||
| چکیده [English] | ||
| Conventional soil maps consist of polygon units where soil types are delineated with clear boundaries, yet intra-unit variability of soil classes remains undefined. To address this, the present study aims to disaggregate the inherited one-millionth soil map units of Iran using the DSMART model in the Abyek region. The DSMART was applied using three tree-based algorithms: C5.0, Random Forest (RF), and Extreme Gradient Boosting (XGBoost), across two scenarios: (1) utilizing legacy soil map information, and (2) integrating 230 new soil profiles at the subgroup level. Model performance and uncertainty were evaluated using overall accuracy, Kappa coefficient, and confusion index. In Scenario 1, map accuracy ranged from 0.29 to 0.37, with Kappa values between 0.17 and 0.29. The highest performance was achieved by XGBoost, showing a confusion index of 0.74. In Scenario 2, accuracy improved to 0.51–0.63 and Kappa to 0.44–0.60, with the best results from the RF model, although the confusion index slightly dropped to 0.65. Spatial consistency with observed soil subgroup distribution improved significantly—by 43% in Scenario 2. Topography proved most influential in Scenario 1, while mean annual rainfall and vertical vegetation index dominated in Scenario 2. Incorporating new soil profile data enhanced model performance by up to 26%. These findings underscore the effectiveness of DSMART, particularly when enriched with new soil data, in refining legacy soil map units for more precise soil class delineation. | ||
| کلیدواژه‌ها [English] | ||
| DSMART, Downscaling, Digital soil mapping, Machine learning | ||
| مراجع | ||
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