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تحلیل حساسیت کانونهای مخاطرهآمیز مستعد گردوغبار در دورههای مرطوب و خشک حوضه دجله و فرات: الگوریتم فرا ابتکاری و یادگیری ماشین | ||
مدیریت مخاطرات محیطی | ||
دوره 10، شماره 4، دی 1402، صفحه 355-370 اصل مقاله (1.17 M) | ||
نوع مقاله: پژوهشی کاربردی | ||
شناسه دیجیتال (DOI): 10.22059/jhsci.2024.373445.821 | ||
نویسندگان | ||
ازهر ابراهیم الطائی1؛ علی اصغر آل شیخ* 2؛ علی درویشی بلورانی3 | ||
1گروه مهندسی GIS، دانشگاه صنعتی خواجه نصیرالدین طوسی | ||
2گروه GIS ، دانشکده مهندسی نقشه برداری، دانشگاه صنعتی خواجه نصیرالدین طوسی، تهران، ایران | ||
3گروه سنجشازدور و GIS، دانشکده جغرافیا، دانشگاه تهران | ||
چکیده | ||
طوفانهای گردوغبار یکی از شدیدترین نوع آلودگی هوا هستند و تهدیدات جدی را برای سلامت، محیطزیست و انسان به همراه دارند. برای مقابله با این پدیده، درک مکانیسمهای تولید گردوغبار بسیار حیاتی است. این امر با استفاده از یادگیری ماشین در تحلیل حساسیت کانونهای گردوغبار و تعیین سطح مخاطرهآمیز بودن آنها به دست میآید. اگرچه فعالیتهای گردوغبار ارتباط بسیار بالایی با تغییر مداوم مکانی و زمانی پارامترهای جوی و محیطی دارد، بااینحال مطالعات معدودی به تحلیل حساسیت کانونهای گردوغبار با در نظر گرفتن نوسانات اقلیمی مانند دورههای مرطوب و خشک پرداختهاند. همچنین، درحالیکه بهینهسازی فرا ابتکاری پارامترها برای بهبود عملکرد یادگیری ماشین بسیار مهم است، بسیاری از مطالعات از آن صرفنظر کردهاند. برای پر کردن خلاءهای پژوهشی مرتبط با این موضوع، هدف از این مطالعه ارائه یک چارچوب برای تحلیل حساسیت کانونهای مخاطرهآمیز مستعد گردوغبار در دورههای خشک و مرطوب (بر اساس تغییرات بدنههای آبی) با استفاده از یک مدل جنگل تصادفی (RF) بهبود یافته با بهینهسازی مبتنی بر آموزش و یادگیری (TLBO) و بهینهسازی مبتنی بر روانشناسی دانشآموز (SPBO) میباشد. برای دستیابی به این هدف، این مطالعه 10392 کانون گردوغبار شناساییشده را همراه با عوامل مؤثر محیطی بین سالهای 2000 تا 2020 در حوضه مشترک فرامرزی دجله و فرات، که ازجمله مهمترین کانونهای گردوغبار در خاورمیانه و در سطح جهانی است، تحلیل کرد. نتایج نشان داد که RF-TLBO با متوسط خطای مطلق میانگین (MAE) 0.146، متوسط خطای جذر میانگین مربعات (RMSE) 0.194 و متوسط ضریب ویلموت (WI) 0.761 در مقایسه با متوسط MAE برابر 0.148، متوسط RMSE برابر 0.195 و متوسط WI برابر 0.757 کمی بهتر از RF-SPBO عمل کرد. TLBO تنظیم مدل RF را با تعداد درختان کمتر و نیز حداکثر عمق کمتر و بهصورت مدلی سادهتر انجام داد. بر همین اساس ما از RF-TLBO استفاده کردیم و نواحی کانونی مستعد گردوغبار را در طول دورههای خشک با سطح بالاتری از مخاطرهآمیز بودن نسبت به دورههای مرطوب شناسایی کردیم. این مشاهده ارتباط معنیداری بین دورههای مرطوب و خشک و مستعد بودن برای ایجاد طوفانهای مخاطرهآمیز را تأیید میکند. سطح بالای مخاطرهآمیز بودن کانونهای نزدیک منابع آبی و باتلاقها نشاندهنده تأثیر قابلتوجه تغییرات پهنههای آبی بر تولید منابع گردوغبار مخاطرهآمیز است. نتایج شاخص Gini همچنین نشان میدهد که پوشش گیاهی، ارتفاع، سرعت باد و بافت خاک تأثیر بیشتری بر مخاطرهآمیز بودن کانونهای مستعد تولید گردوغبار دارند. | ||
کلیدواژهها | ||
کانونهای گردوغبار؛ جنگل تصادفی؛ روشهای فرا ابتکاری؛ حوضه دجله و فرات؛ دورههای مرطوب و خشک | ||
مراجع | ||
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