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ارزیابی روشهای یادگیری ماشین در ریزمقیاس نمایی مکانی میانگین سالانة دمای سطح زمین و دمای هوا | ||
نشریه محیط زیست طبیعی | ||
دوره 75، شماره 4، آبان 1401، صفحه 551-569 اصل مقاله (1.12 M) | ||
نوع مقاله: مقاله پژوهشی | ||
شناسه دیجیتال (DOI): 10.22059/jne.2022.340875.2416 | ||
نویسندگان | ||
آزاده عتباتی* 1؛ حامد ادب2 | ||
1علوم و مهندسی محیط زیست، دانشکده جغرافیا و علوم محیطی، دانشگاه حکیم سبزواری، سبزوار، ایران | ||
2گروه سنجش از دور و سیستم اطلاعات جغرافیایی، دانشکده جغرافیا و علوم محیطی، دانشگاه حکیم سبزواری، سبزوار، ایران | ||
چکیده | ||
امروزه استفاده از دادههای شبکهای پایگاههای اقلیمی مانند WorldClim یکی از منابع معتبر داده است که جایگزین دادههای نقطهای ایستگاههای هواشناسی شده است؛ اما استفاده از این پایگاههای اقلیمی باقدرت تفکیک مکانی پایین موجب ایجاد محدودیت برای بسیاری از مطالعات مرتبط با علوم زیستشناسی و بومشناسی شده است. هدف از این پژوهش بررسی ارتباط دمای هوا و دمای سطح زمین و سپس بازتولید دمای سطح زمین باقدرت تفکیک مکانی بالا جهت ریزمقیاس نمایی میانگین سالانة دمای هوا با استفاده از دو محصول پرکاربرد میانگین سالانة دمای هوا از پایگاه داده WorldClim و میانگین سالانة دمای روز و شب سطح زمین MOD11A2 v061 سنجندة مادیس است. در این پژوهش، ابتدا عملکرد مدلهای یادگیری ماشین شامل جنگل تصادفی،شبکة عصبی مصنوعی، رگرسیون شبکه الاستیک و ماشین بردار پشتیبان جهت ریزمقیاس نمایی محصول MOD11A2 v061 از 1 کیلومتر به 250 متر بررسی شد. برای این منظور از متغیرهای پیوسته و گسسته شامل ارتفاع از سطح دریا، عرض جغرافیایی، پوشش گیاهی، بافت خاک، جهت شیب و پوشش سطح زمین استفاده گردید. سپس میانگین سالانة دمای هوا WorldClim با استفاده از دمای سطح زمین با مدل پولی نومیال درجة 3 از 1 کیلومتر به 250 متر ریزمقیاس شد. همچنین از داده های 7 ایستگاه سینوپتیک جهت بررسی اعتبار محصول ریزمقیاس شده استفاده شد. نتایج نمودار تیلور نشان داد مدل جنگل تصادفی، بهترین عملکرد در ریزمقیاس نمایی میانگین سالانة دمای سطح زمین با ریشة میانگین مربعات خطا 0/54 درجه سلسیوس دارد. همچنین مدل پولی نومیال درجة 3 میزان خطای نسبی کمتر در تولید داده ریزمقیاس دمای هوا دارد. مقدار ریشة میانگین مربعات خطا نتایج برای مدل تصحیح نشده و تصحیح شده ریزمقیاس به ترتیب 1/32 و 1/21 درجه سلسیوس بهدست آمد که با توجه به آزمون t جفتی اختلاف معنیداری در سطح 0/05 نشان نداد. یافته های این پژوهش نشان میدهد که ریزمقیاس نمایی میانگین سالانه دمای هوا WorldClim از اعتبار لازم برخوردار است. | ||
کلیدواژهها | ||
پایگاه اقلیمی WorldClim؛ داده مادیس؛ ریزمقیاس نمایی مکانی؛ یادگیری ماشین | ||
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