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تهیۀ نقشه‌های سه‌بعدی اجزای بافت خاک با تلفیق الگوریتم جنگل رگرسیونی چندکی و تابع عمق اسپیلاین در استان گلستان | ||
| تحقیقات آب و خاک ایران | ||
| دوره 55، شماره 1، فروردین 1403، صفحه 51-68 اصل مقاله (2.52 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijswr.2023.366978.669594 | ||
| نویسندگان | ||
| مریم امامی1؛ فرهاد خرمالی* 2؛ محمدرضا پهلوان راد3؛ سهیلا ابراهیمی4 | ||
| 1گروه علوم خاک-دانشکده ی مهندسی آب و خاک-دانشگاه علوم کشاورزی و منابع طبیعی گرگان | ||
| 2گروه علوم خاک، دانشکده مهندسی آب و خاک، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، ایران. | ||
| 3تحقیقات خاک و آب، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی گلستان، گرگان | ||
| 4گروه علوم خاک، دانشکده مهندسی آب و خاک، دانشگاه علوم کشاورزی و منابع طبیعی گرگان. ایران. | ||
| چکیده | ||
| امروزه نیاز روزافزونی به اطلاعات مکانی پیوسته و کمی خاک در راستای مدل‌سازی و مدیریت محیطی، به‌ویژه در مقیاس ملی وجود دارد. این مطالعه با هدف پیش‌بینی نسبت اندازه ذرات خاک (PSF) در بخشی از اراضی استان گلستان با استفاده از تلفیق مدل جنگل رگرسیونی چندکی (QRF) و تابع اسپیلاین انجام شد. تابع عمق اسپیلاین با مساحت برابر برای تخمین PSFs در پنج عمق خاک (0-25، 25-50، 50-75، 75-100، و 100-125 سانتی‌متر) به داده‌های 105 خاکرخ از بانک اطلاعات دانشگاه علوم کشاورزی و منابع طبیعی گرگان برازش داده شد. متغیرهای کمکی اولیه در این تحقیق شامل 22 متغیر محیطی مشتق شده از DEM، 15 شاخص سنجش از دور از ماهواره لندست هفت سنجنده ETM+، نقشه‌های عمق ایستابی (پیزومتری) و بارندگی بودند. بر اساس روش تجزیه مؤلفه‌های اصلی (PCA)، 15متغیر انتخاب و وارد فرآیند مدل‌سازی اجزای بافت خاک (رس، سیلت و شن) شدند. عملکرد مدل QRF با استفاده از آماره‌های ضریب تبیین (R2)، ریشه میانگین مربعات خطا (RMSE)، و قدر مطلق میانگین خطا (MAE) مورد ارزیابی قرار گرفت. نتایج نشان داد میزان ضریب تببین برای رس، سیلت، و شن در عمق‌های مختلف به ترتیب از 12/0 تا 22/0، 07/0 تا 30/0، و 07/0 تا 28/0 متغیر بود. همچنین اهمیت نسبی متغیرهای محیطی نشان داد بارندگی (میانگین سی‌ساله)، عمق ایستابی (میانگین ده‌ساله)، B3/B7 و شاخص عمق دره، مهمترین پارامترهای کنترل‌کنندۀ اجزای بافت خاک در تحقیق حاضر بودند. به منظور بهبود عملکرد مدل و نتایج اعتبارسنجی نیاز به پرداختن به برخی عدم قطعیت‌های ساختاری در این مطالعه وجود دارد. | ||
| کلیدواژه‌ها | ||
| اجزای اندازه ذرات خاک (PFS)؛ اسپیلاین؛ آنالیز مولفه‌های اصلی (PCA)؛ مدل جنگل رگرسیونی چندکی (QRF) | ||
| عنوان مقاله [English] | ||
| Preparation of three-dimensional maps of soil particle size fractions by combining quantile regression forest algorithm and spline depth function in Golestan Province | ||
| نویسندگان [English] | ||
| Maryam Emami1؛ Farhad Khormali2؛ Mohammad reza Pahlavan Rad3؛ Soheila Ebrahimi4 | ||
| 1Department of Soil Science, Faculty of water and soil Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran. | ||
| 2Corresponding Author, Department of Soil Science, Faculty of water and soil Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran. | ||
| 3Soil and Water Research Department, Golestan Agricultural and Natural Resources Research and Education Center, AREEO, Gorgan, Iran. | ||
| 4Department of Soil Science, Faculty of water and soil Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran | ||
| چکیده [English] | ||
| There is an increasing need for continuous spatial and quantitative soil information for environmental modeling and management, especially at the national scale. This study was conducted to predict the soil particle size fraction (PSF) using the combination of quantile regression forest model (QRF) and spline function in a part of Golestan province. An equal area spline equation was fitted to the data of 105 soil profiles from the database of the Gorgan University of Agricultural Sciences and Natural Resources for estimating PSFs at five soil depths (0-25, 25-50, 50-75, 75-100, and 100-125 cm). The primary auxiliary variables in this research included 22 environmental variables derived from DEM, 15 remote sensing indicators obtained from the Landsat 7 ETM+ images, rainfall and piezometric maps. Based on principal component analysis (PCA), 15 variables were selected and entered into the modeling process of soil texture components (clay, sand, and silt). The efficiency of the quantile regression forest model was evaluated using the coefficient of determination (R2), the root mean squared error (RMSE), and the mean absolute error (MAE). The results indicated that the coefficient of determination for clay, silt, and sand at different depths varied from 0/12 to 0/22, 0/07 to 0/30, and 0/07 to 0/28, respectively. Also, the relative importance of environmental variables showed that rainfall (thirty-year average), piezometry (ten-year average), B3/B7, and valley depth were the most important factors in predicting soil texture components. To improve model performance and validation results, some structural uncertainties in this study should be addressed. | ||
| کلیدواژه‌ها [English] | ||
| Principal component analysis (PCA), Quantile regression forest (QRF), Soil particle size fraction (PFS), Spline | ||
| مراجع | ||
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