مدلسازی آلودگی خاک به فلزات سنگین با استفاده از روشهای یادگیری ماشین و داده‌های طیف سنجی

نوع مقاله : پژوهشی اصیل

نویسندگان
1 گروه سنجش ازدور و سیستم اطلاعات جغرافیایی، دانشکده علوم انسانی، دانشگاه تربیت مدرس، تهران، ایران
2 کارشناسی ارشد سنجش از دور و سیستم اطلاعات جغرافیایی، دانشگاه آزاد اسلامی، واحد علوم و تحقیقات تهران، ایران
چکیده
معادن و صنایع وابسته به آن، در زمان بهره‌برداری و پس از متروکه شدن، بر محیط زیست اطراف خود تأثیرگذارند. از جملۀ این تأثیرات می‌توان به آلودگی آب‌های زیرزمینی و سطحی، و نیز آلودگی خاک اشاره کرد. مدل‌سازی غلظت فلزات سنگین با استفاده از روش‌های مقرون‌به‌صرفه لازمۀ مدیریت و اصلاح آسیب‏های واردشده به محیط زیست است. هدف این تحقیق ارائۀ چارچوبی به‌منظور مدل‌سازی فلزات سنگین در خاک با استفاده از طیف‌سنجی و نیز روش‌های مدل‌سازی آماری است. بدین منظور با استفاده از طیف‌سنجی، نمودار طیفی مربوط به 53 نمونه خاک مربوط به منطقه‌ای در اطراف یک معدن متروکه در ایالت نیوساوث ولز استرالیا در طول موج‌های مرئی تا مادون قرمز میانی برداشت شد و مشتق دوم این داده‌ها محاسبه شد. سپس داده‌های طیفی مناسب برای مدل‌سازی غلظت فلزات سنگین شامل سرب، نقره، کادمیوم و جیوه با استفاده از روش انتخاب ویژگی جنگل تصادفی تعیین شدند و به‌عنوان ورودی برای مدل‌سازی غلظت فلزات سنگین با استفاده از روش‌های رگرسیون خطی چندمتغیره، جنگل تصادفی رگرسیون و ماشین‏بردار رگرسیون به‌کار گرفته شدند. نتایج نشان داد که طول موج‌های مادون قرمز میانی دارای اهمیت بیشتری به‌منظور مدل‌سازی غلظت فلزات سنگین در این تحقیق هستند. همچنین روش‌های غیرخطی یادگیری ماشین به‌خصوص جنگل تصادفی رگرسیون با مقادیر مجذور میانگین مربعات خطا ppm 8/0 و ضریب تعیین 51/0 برای سرب و ppm 4/9 و 46/0 برای کادمیوم دارای عملکرد بهتری نسبت‌به روش رگرسیون خطی چندمتغیره هستند.

کلیدواژه‌ها

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