دوره 17، شماره 47 - ( 12-1396 )                   جلد 17 شماره 47 صفحات 39دوره59فصل__Se | برگشت به فهرست نسخه ها

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PM10 AIR POLLUTION IN MASHAD CITY USING ARTIFICIAL NEURAL NETWORK AND MAKOV CHAIN MODEL. researches in Geographical Sciences. 2018; 17 (47) :39-59
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

URL: http://jgs.khu.ac.ir/article-1-2859-fa.html


1- استاد دانشگاه خوارزمی تهران ، asadollahfardi@yahoo.com
2- دانشجوی دانشگاه آزاد اسلامی واحد جنوب تهران
چکیده:   (523 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.
 
 
 
 
 
 
 
 
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نوع مطالعه: پژوهشي | موضوع مقاله: اب و هواشناسی
دریافت: ۱۳۹۵/۶/۲۴ | پذیرش: ۱۳۹۶/۸/۲۱ | انتشار: ۱۳۹۶/۱۱/۱۸

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