نقشه‌برداری رقومی اجزای بافت خاک با استفاده از رویکردهای یادگیری ماشین و متغیرهای محیطی

آب و خاک

مقاله 7، دوره 37، شماره 6 - شماره پیاپی 92، بهمن و اسفند 1402، صفحه 923-942   

نوع مقاله: مقالات پژوهشی

شناسه دیجیتال (DOI): 10.22067/jsw.2023.84413.1331

نویسندگان

پگاه خسروانی¹؛ مجید باقرنژاد^{* 1}؛ سید علی اکبر موسوی¹؛ سید رشید فلاح شمسی²

¹گروه علوم و مهندسی خاک، دانشکده کشاورزی، دانشگاه شیراز، شیراز، ایران

²گروه مهندسی منابع طبیعی و محیط‌زیست، دانشگاه شیراز، شیراز، ایران

چکیده

بافت به­عنوان یکی از ویژگی­های موثر بر وضعیت هیدرولوژیکی و مکانیکی خاک از اهمیت ویژه­ای برای رشد گیاهان برخوردار است. بنابراین، مطالعه حاضر با هدف مدل­سازی مکانی ذرات خاک شامل شن، سیلت و رس با استفاده از مدل­های یادگیری ماشین جنگل تصادفی (RF)، رگرسیون بردار پشتیبان (SVR) و مدل زمین­اماری کوکریگینگ (Co-Kriging) و نقشه مدل رقومی ارتفاع و تصاویر ماهواره­ای با وضوح مکانی بالا در دشت مرودشت استان فارس انجام گرفت. مطالعات میدانی شامل حفر 200 پروفیل مشاهداتی براساس روش نمونه‌برداری ابر مکعب لاتین (cLHS) و نمونه‌برداری از دو عمق، سطحی (30 سانتی‌متر) و زیرسطحی (60-30 سانتی­متر) بود. طیف وسیعی از متغیرهای کمکی با دسترسی آسان شامل پارامترهای توپوگرافی و شاخص­های سنجش از دور به‌عنوان پیش­بینی کننده­های محیطی با وضوح مکانی 30 متر تهیه گردید. مناسب­ترین متغیرهای کمکی با استفاده از آزمون هم‌خطی توسط شاخص تورم واریانس (VIF) و سپس رویکرد انتخاب متغیر باروتا انتخاب شد. نتایج نشان داد که مدل RF نسبت به مدل­های SVR و Co-Kriging دارای عملکرد بهتر و دقت بیشتر برای پیش­بینی هر سه ذره شن، سیلت و رس است. ضریب تبیین (R²) پیش­بینی شده برای رس در عمق سطحی و زیر سطحی به‌ترتیب 52/0 و 49/0، برای سیلت 48/0 و 64/0 و برای شن 54/0 و 36/0 درصد محاسبه شد. همچنین متغیر عمق دره مهمترین متغیر در پیش­بینی توزیع مکانی شن، سیلت و رس شناسائی شد. تهیه نقشه توزیع مکانی اندازه ذرات خاک حاصل می‌تواند منبع مهمی از اطلاعات برای مدیریت کارآمد منابع زمین و نظارت بر محیط‌زیست باشد. به­طور کلی رویکرد مورد استفاده در این تحقیق به‌دلیل استفاده از منابع اطلاعاتی با دسترسی آسان و ارزان می­تواند به­عنوان راهنمایی برای تهیه­ی نقشه­های مدیریت­پذیر خاک در دشت­های کشاورزی کشور با قابلیت به­روز رسانی در حداقل زمان ممکن مورد استفاده قرار گیرد.

کلیدواژه‌ها

توزیع اندازه ذرات خاک؛ جنگل تصادفی؛ کوکریجینگ؛ نقشه برداری رقومی؛ یادگیری ماشین

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