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PM10 AIR POLLUTION IN MASHAD CITY USING ARTIFICIAL NEURAL NETWORK AND MAKOV CHAIN MODEL. jgs 2017; 17 (47) :39-59
URL: http://jgs.khu.ac.ir/article-1-2859-fa.html
URL: http://jgs.khu.ac.ir/article-1-2859-fa.html
اسدالله فردی غلامرضا، زنگوئی حسین. پیش بینی آلودگی PM10 هوای شهر مشهد با استفاده از شبکه های عصبی مصنوعی MLP و مدل زنجیره مارکف. نشریه تحقیقات کاربردی علوم جغرافیایی. 1396; 17 (47) :39-59
1- دانشگاه خوارزمی تهران ، asadollahfardi@yahoo.com
2- دانشگاه آزاد اسلامی واحد جنوب تهران
2- دانشگاه آزاد اسلامی واحد جنوب تهران
چکیده: (6706 مشاهده)
مدیریت ذرات معلق یکی از موارد مهم در کنترل آلودگی شهرها محسوب میشود. این ذرات باعث ایجاد و توسعه بیماری های قلبی و تنفسی مختلف در افراد میگردد. شهر مشهد به عنوان یکی از شهرهای اصلی و پرجمعیت ایران با توجه به شرایط اقلیمی و همچنین توریستی بودن، بیش از هر چیزی در معرض خطر این نوع آلودگی قرار دارد. در این تحقیق سعی شده با استفاده از مدلهای پرسپترون شبکه های عصبی مصنوعی و مدل زنجیره مارکوف غلظت PM10 پیشبینی و تحلیل گردد. برای این منظور از دادههای ساعتی CO، SO2، PM2.5 و دما برای دو ماه فروردین و اردیبهشت در سال 1394 استفاده شد. از مجموع 1488 سری داده، 1300 داده برای آموزش شبکه و 188 داده جهت صحتسنجی استفاده گردید. نتایج نشاندهنده عملکرد مطلوب این روشها در پیشبینی میزان آلاینده و همچنین احتمال وقوع ساعات با کیفیتهای مختلف آلودگی بود. بهترین مدل پرسپترون میزان آلاینده ذرات معلق را با ضریب همبستگی 88/0، شاخص تطابق 91/0، میانگین بایاس خطای 0874/0 و جذر میانگین مربعات خطای 26/2 پیشبینی نمود، همچنین مدل مارکوف با خطای مطلق متوسط حدود 1/0 درصد احتمالات انتقال وضعیت و تداوم وضعیتهای مختلف آلودگی هوای ناشی از ذرات معلق را پیشبینی نمود.
نوع مطالعه: پژوهشي |
موضوع مقاله:
اب و هواشناسی
فهرست منابع
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