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عدم قطعیت و نقشه‌برداری مکانی شوری و قلیا بودن خاک با استفاده از روش‌های یادگیری ماشین در سه عمق مدیریتی مختلف در منطقه آبیک | ||
| تحقیقات آب و خاک ایران | ||
| دوره 56، شماره 3، خرداد 1404، صفحه 607-629 اصل مقاله (3.27 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijswr.2025.382783.669797 | ||
| نویسندگان | ||
| اعظم جعفری1؛ فریدون سرمدیان* 2؛ زهرا رسائی3 | ||
| 1بخش علوم و مهندسی خاک، دانشکده کشاورزی-دانشگاه شهیدباهنر کرمان | ||
| 2عضو هیأت علمی گروه مهندسی علوم خاک، پردیس کشاورزی و منابع طبیعی دانشگاه تهران | ||
| 3گروه علوم و مهندسی خاک، دانشکده کشاورزی، دانشکدگان کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران | ||
| چکیده | ||
| شور و سدیمی شدن خاک یکی از مهم‌ترین فرآیندهای مخرب خاک مناطق خشک و نیمه‌خشک می‌باشد. این دو عارضه می‌توانند علاوه بر کاهش میزان باروری خاک‌ها، این اراضی را مستعد تخریب کرده و تهدیدی جدی برای توسعه پایدار منابع باشند. تهیه نقشه‌های پراکنش این ویژگی‌ها در طول خاک‌رخ می‌تواند به مدیریت بهتر این اراضی کمک کند. مطالعه حاضر با هدف بررسی تغییرات شور و سدیمی بودن خاک‌های منطقه خشک و نیمه‌خشک آبیک قزوین اجرا شده است. به منظور آگاهی از نحوه پراکنش سطحی و عمقی این دو ویژگی، سه عمق مهم از نظر کشت محصولات کشاورزی شامل 0-50، 0-100 و 0-150 سانتی‌متر بررسی شدند. مدل‌سازی‌ها بر اساس اطلاعات 281 خاک‌رخ و متغیرهای کمکی محیطی با دقت مکانی 5/12 متر انجام شدند. مدل‌سازی و پیش‌بینی مقادیر هدایت الکتریکی (شوری) و نسبت جذب سدیم (قلیا بودن) بر اساس چهار مدل تعلیم ماشین کوبیست، جنگل تصادفی، شبکه عصبی مصنوعی و گرادیان بوستینگ صورت گرفت که مدل ترکیبی وزن‌دار ساده ترکیب این مدل‌ها به عنوان نقشه‌های نهایی شوری و سدیمی بودن در نظر گرفته شدند. عدم قطعیت مدل از روش بوت‌استرپینگ با 50 تکرار بدست آمد. نتایج نشان داد که پستی و بلندی، اقلیم و پوشش گیاهی اصلی‌ترین عوامل کنترل کننده شوری و قلیا بودن در منطقه می‌باشند. مقدار ضریب تبیین مدل‌های نهایی پیش‌بینی شوری و سدیمی در هر سه عمق مورد بررسی در محدوده 61/0 تا 81/0 بوده و بیانگر کارایی خوب مدل‌ها می‌باشد. بیشترین میزان عدم قطعیت مدل‌ها در قسمت‌های جنوبی منطقه با تغییرات زیاد مقادیر هدایت الکتریکی و نسبت جذب سدیم در فاصله کم، تعداد کمتر مشاهدات خاک، توپوگرافی کمتر مشاهده شد که این مقدار برای مدل‌های پیش‌بینی کننده سدیمی بودن در تمامی عمق‌ها نسبت به شوری کمتر بود. کارایی مدل‌ها برای هر دو ویژگی با افزایش عمق افزایش یافته است. بیش از 65% منطقه بصورت غیر شور می‌باشد درحالی‌که مناطق بدون قلیا 70% منطقه را پوشش می‌دهند. دستیابی به این نقشه‌ها گامی موثر در بهبود مدیریت بهره‌برداری از اراضی مطابق با استعدادهای آن‌ها می‌باشد. | ||
| کلیدواژه‌ها | ||
| توزیع مکانی؛ شوری و قلیائیت؛ بوت‌استرپینگ؛ نقشه‌برداری رقومی خاک | ||
| عنوان مقاله [English] | ||
| Uncertainty and Spatial mapping of soil salinity and sodicity using machine learning methods in three different management depths in Abyek region | ||
| نویسندگان [English] | ||
| Azam Jafari1؛ Fereydoon Sarmadian2؛ Zahra Rasaei3 | ||
| 1Soil Science Department, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran | ||
| 2soil science department< faculty of agricultural engineering and technology, university of Tehran | ||
| 3Soil Science Department, Faculty of Agricultural, University College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran | ||
| چکیده [English] | ||
| Soil salinity and sodicity are affecting soils in arid and semi-arid regions, reducing soil fertility and leading to land degradation, posing threats to the sustainable development of resources. Creating maps of these soil variables throughout soil profile is crucial for effective land management. This study aims to investigate the spatial variability of soil salinity and sodicity in a part of the arid and semi-arid region of Abyek. Three critical depths for the cultivation of important agricultural products (0-50, 0-100, and 0-150 cm) were examined. The models were conducted using 281 soil data and environmental covariates. The modeling and prediction of soil electrical conductivity (salinity) and sodium absorption ratio (sodicity) were performed using four machine learning models: Cubist, random forest, artificial neural network, and XGBoost. The final maps were derived from a simple weighted ensemble model. The model uncertainty was assessed using bootstrapping with 50 repetitions. The results indicated high spatial variability of EC and SAR (exceeding 35%), with an increase from north to south of the region and from surface to deeper soil layers. Results showed that topography, climate, and vegetation are primary controlling factors of spatial distribution of soil salinity and alkalinity. R² for the final models predicting both EC and SAR across all three depths ranged from 0.61 to 0.81, demonstrating the models’ high efficiency, increasing with depth. The highest level of model uncertainty was observed in the southern parts of the region with high variability in EC and SAR values in short distances, fewer soil observations, and less topography, which was lower for models predicting sodium content at all depths compared to salinity. More than 65% of the area was non-saline, while non-alkaline areas covered 70% of it. Acquiring these maps represents a significant step towards improving land management practices based on the land’s potential. | ||
| کلیدواژه‌ها [English] | ||
| Spatial distribution, salinity and alkalinity, bootstrapping, digital soil mapping | ||
| مراجع | ||
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