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نقشه‌برداری رقومی ضخامت خاک سطحی و عدم قطعیت وابسته به آن با استفاده از رویکرد یادگیری ماشین در بخشی از اراضی خشک و نیمه‌خشک دشت قزوین | ||
| تحقیقات آب و خاک ایران | ||
| دوره 53، شماره 3، خرداد 1401، صفحه 585-602 اصل مقاله (1.61 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijswr.2022.338007.669195 | ||
| نویسندگان | ||
| اصغر رحمانی1؛ فریدون سرمدیان* 2؛ حسین عارفی3 | ||
| 1گروه علوم و مهندسی خاک، پردیس کشاورزی و منایع طبیعی، دانشگاه تهران، کرج، ایران | ||
| 2گروه مهندسی علوم خاک، پردیس کشاورزی و منابع طبیعی دانشگاه تهران، کرج، ایران | ||
| 3گروه سنجش از دور و فتوگرامتری،دانشکده مهندسی نقشه برداری و اطلاعات مکانی، پردیس دانشکده های فنی، دانشگاه تهران، تهران، ایران. | ||
| چکیده | ||
| پژوهش حاضر باهدف مدل‌سازی رقومی ضخامت خاک سطحی با استفاده از مدل‌های یادگیری ماشین جنگل تصادفی (RF) و شبکه عصبی مصنوعی (ANN) در حدود 60000 هکتار از اراضی دشت قزوین ( حد واسط آبیک و نظرآباد) با تراکم مشاهداتی 278 پروفیل در بازه زمانی 1395-1399 و تعداد 17 متغیر محیطی مستخرج از تصاویر ماهواره لندست8، مشتقات اولیه و ثانویه مدل رقومی ارتفاع، داده‌های اقلیمی، نقشه کاربری اراضی و زمین‌شناسی اجرا گردید. برای انتخاب متغیرهای کمکی از الگوریتم نظارت‌شده باروتا (Boruta) به همراه نظر کارشناس استفاده شد. از دو تابع "nnet" و "random forest" و بسته "caret" در محیط نرم‌افزار R برای مدل‌سازی بر اساس 80 درصد داده‌ها در مرحله واسنجی و 20 درصد برای اعتبارسنجی استفاده شد و عدم قطعیت نقشه‌های نهایی با دو روش بوتسراپت (bootstrapping) و کا-مرتبه (k-fold) کمی سازی گردید. نتایج بیانگر انتخاب 10 متغیر کمکی از میان 17 متغیر بود و متغیرهای شاخص سبزینگی، تأثیر باد، تابش پخشیده و شاخص همواری دره باقدرت تفکیک بالا(Mrvbf) به‌عنوان مهم‌ترین متغیرهای کمکی مشخص گردیدند. نتایج اعتبارسنجی مدل RF بیانگر ضریب تبیین (R2) 8/0 و ریشه میانگین مربعات خطای (RMSE) کمتر از 3 سانتی‌متر و اریبی (Bias) 63/0 سانتی‌متر است. در مدل شبکه عصبی مقادیرR2 ، RMSE و Bias به ترتیب برابر43/0، 05/0 سانتی‌متر و 004/0 سانتی‌متر حاصل گردید، همچنین ضریب همبستگی توافق (CCC) برای مدل RF در مقایسه با ANN به میزان 50 درصد افزایش نشان می‌دهد، عدم قطعیت برآورد شده توسط روش bootstrapping در مقایسه با k-fold به در مناطق با ضخامت 10 تا15 سانتی به میزان 7 سانتی متر بیشتر است و در بخش زیادی از منطقه میزان پایین و دارای الگوی مکانی یکسانی می‌باشند. مدل جنگل تصادفی به همراه متغیرهای محیطی انتخاب‌شده و عدم قطعیت‌های کمی شده نقشه‌های خروجی می‌توانند برای مدل‌سازی ضخامت خاک سطحی در نواحی مشابه با این پژوهش در مطالعات آتی استفاده گردد. | ||
| کلیدواژه‌ها | ||
| جنگل تصادفی؛ شبکه عصبی مصنوعی؛ متغیرهای محیطی؛ ضخامت خاک سطحی؛ کمی سازی عدم قطعیت | ||
| عنوان مقاله [English] | ||
| Digital Mapping of Top-soil Thickness and Associated Uncertainty Using Machine Learning Approach in Some Part of Arid and Semi-arid Lands of Qazvin Plain | ||
| نویسندگان [English] | ||
| Asghar Rahmani1؛ Fereydoon Sarmadian2؛ Hossein Arefi3 | ||
| 1Soil science and engineering department, College of agriculture and natural resources, University of Tehran, Karaj, Iran. | ||
| 2Soil science department, College of agriculture and natural resources, university of Tehran | ||
| 3Remote sensing and Photogrammetry Department, Faculty of Surveying and Spatial Information Engineering, Campus of Technical Colleges, University of Tehran, Tehran, Iran. | ||
| چکیده [English] | ||
| The present study was carried out to model topsoil thickness using machine learning models (MLM) including random forest (RF) and artificial neural network (ANN) in around 60,000 hectares of Qazvin plain lands (intermediate of Abyek and Nazarabad) with an observational density of 278 profiles during 2016 until 2020, and 17 environmental covariates extracted from Landsat 8 satellite images, primary and secondary derivatives from Digital elevation model, climate data, land use and geology maps. Boruta supervised algorithm and expert knowledge were used to select the best relevant environmental covariates. Two functions include "nnet" and "random forest" (RF) by "caret" package in the R software were used. Modeling of topsoil thickness carried out based on 80% of the data in the calibration subset and 20% of the data was used for model validation. The uncertainty of the output maps was quantified using two methods of “bootstrapping and k-fold”. A number of 10 environmental covariates selected among 17 variables, and the relative importance introduced the greenness index, wind effect, diffused radiation, and Mrvbf as the most important covariates, respectively. The validation results indicate that the RF model with R2 of 0.8 and RMSE less than 3 cm and the bias is 0.63 cm in compare to the ANN, With R2, RMSE, and Bias 0.43, 0.05, and.004, respectively was outperform. Also, the CCC for the RF model increased by 50% compared to the ANN. The uncertainty estimated by the bootstrapping method was 7 cm lower compared to k-fold in the regions with 10-15 cm thickness and both of two methods show the same spatial pattern in other parts. The RF model along with selected covariates environmental variables and quantified uncertainties of output maps can be used to model the topsoil thickness and management decision making in areas similar to this study in future studies. | ||
| کلیدواژه‌ها [English] | ||
| Random Forest, Artificial Neural Network, Environmental variables, Top-Soil thickness, Uncertainty | ||
| مراجع | ||
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