مدل‌سازی رقومی تغییرات سه‌بعدی شوری خاک با استفاده از الگوریتم‌های یادگیری ماشین در اراضی خشک و نیمه‌خشک دشت قزوین

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری گروه مهندسی علوم خاک، دانشکده مهندسی و فناوری کشاورزی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران

2 استاد گروه مهندسی علوم خاک، دانشکده مهندسی و فناوری کشاورزی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران

3 استاد گروه مهندسی ماشینهای کشاورزی، دانشکده مهندسی و فناوری کشاورزی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران

4 استاد دانشکده محیط زیست و علوم زمین، دانشگاه کاتولیک لوون، لوون، بلژیک

چکیده

 شوری خاک به­ عنوان یکی از مهم‌ترین شاخص­های کیفیت خاک، نقش مهمی در برنامه­ریزی­های کاربری و مدیریت اراضی در مناطق خشک و نیمه‌خشک دارد. این پژوهش با هدف مدل‌سازی رقومی تغییرات سطحی و عمقی شوری خاک در پنج عمق استاندارد پروژه جهانی نقشه برداری رقومی خاک (5-0، 15-5، 30-15، 60-30 و 100-60 سانتی­متر) در 60 هزار هکتار از اراضی دشت قزوین با وضوح مکانی 15 متر صورت پذیرفت. مطالعات میدانی شامل نمونه­برداری از 278 خاکرخ بود و هدایت الکتریکی خاک­ها در آزمایشگاه اندازه­گیری شد. انتخاب متغیرهای محیطی، شامل پارامترهای مستخرج از داده­های تصاویر لندست 8، توپوگرافی و لایه­های اقلیمی، طبق روش حذف ویژگی برگشتی (RFE) صورت پذیرفت. چهار الگوریتم یادگیری ماشین جنگل تصادفی (RF)، کوبیست (CB)، رگرسیون درخت تصمیم (DTr) و k- نزدیک‌ترین همسایگی (k-NN) برای تهیه نقشه پیش­بینی شوری خاک استفاده شد. بر اساس نتایج RFE درنهایت 10 متغیر کمکی در هر عمق انتخاب شدند. نتایج نشان داد که مدل CB در اعماق استاندارد 5-0 و 30-15 سانتیمتر با R2 برابر 92/0 و 85/0 و RMSE برابر 77/4 و 90/7 دسی زیمنس بر متر و مدل RF در اعماق 15-5، 60-30 و 100-60 سانتی­متر مدل با R2 به­تریتب برابر 93/0، 94/0، 96/0 و RMSE 65/6، 10/5 و 20/3 دسی زیمنس بر متر بالاترین مقادیر صحت را نسبت به دو مدل DTr و k-NN داشتند. همچنین در اعماق سطحی متغیرهای کمکی مستخرج از داده­های سنجش دور و در اعماق زیرسطحی پارامترهای اقلیمی و توپوگرافی بیشترین ارتباط را با تغییرات شوری داشتند. بطور کلی مدل­های RF و CB به همراه متغیرهای محیطی مناسب بخوبی توانستند تغییرات شوری را در اعماق استاندارد موردمطالعه ارائه نمایند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Digital Modeling of Three-Dimensional Soil Salinity Variation Using Machine Learning Algorithms in Arid and Semi-Arid lands of Qazvin Plain

نویسندگان [English]

  • Sayed Roholla Mousavi 1
  • Fereydoon Sarmadian 2
  • Mahmoud Omid 3
  • Patrick Bogaert 4
1 Ph.D. Student of Soil Resources Management,, ,Science and soil Engineering Department,, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University, of Tehran. Karaj, Iran.
2 Professor of Soil and Science Engineering Department,, Faculty of Agricultural Engineering and Technology, University College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
3 Professor of Agricultural Machinery Engineering Department, Faculty of Agricultural Engineering and Technology, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran
4 Professor of Earth and Life Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium
چکیده [English]

Soil salinity, as one of the most important indicators of soil quality, has crucial roles in land use planning and land management in arid and semi-arid regions. The aim of this study was to model soil salinity at five standard depth (0-5, 5-15, 15-30, 30-60, and 60-100 cm) of global digital soil mapping project in 60,000 hectares of Qazvin plain with spatial resolution of 15m. Field studies included a sampling of 278 soil profiles and then the EC was measured in the laboratory. The recursive feature elimination (RFE) method was employed to select environmental covariates including parameters extracted from Landsat 8 image (OLI/TIRS) data, topography, and climatic parameters. Four machine learning algorithms as random forest (RF), cubist (CB), decision tree regression (DTr), and k-nearest neighbors (k-NN) were applied for predicting and mapping soil salinity. According to RFE, 10 covariates were chosen for each standardized depth. The results of modeling showed that the CB model at the depth of 0-5 and 15-30 cm with R2 values of 0.92 and 0.85 and RMSE 4.77 and 7.90 dS/m and the RF model at depths of 5-15, 30-60, and 60-100 cm with R2 values of 0.93, 0.94, 0.96 and RMSE 6.65, 5.10 and 3.20 dS/m, respectively, had the highest accuracy compared to two other models i.e., DTr and k-NN. Furthermore, the covariates extracted from RS data had more impact on topsoil salinity prediction while the climate and topographic attributes influence subsurface soil salinity. Generally, The RF and CB models along with appropriate environmental covariates were able to present salinity variation of study standard depths.

کلیدواژه‌ها [English]

  • Soil Salinity
  • Environmental Covariates
  • digital soil mapping
  • Machine learning
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