بهبود عملکرد مدل شبکۀ عصبی مصنوعی با کمک تبدیل موجک و روش PCA برای مدل‌سازی و پیش‌بینی اکسیژن مورد نیاز بیولوژیکی (BOD)

نوع مقاله : پژوهشی

نویسندگان

1 دانشیار گروه هیدرولوژی و منابع آب، دانشکدۀ مهندسی علوم آب، دانشگاه شهید چمران اهواز‌

2 دانشجوی دکتری مهندسی منابع آب، دانشکدۀ مهندسی علوم آب، دانشگاه شهید چمران اهواز‌

چکیده

در دهه‏های اخیر، توسعۀ مدل‏های هوش مصنوعی برای پیش‌بینی پدیده‏های هیدرولوژیکی کاربرد زیادی پیدا کرده است. در این مطالعه، توانایی مدل‏های شبکۀ عصبی مصنوعی به‌منظور مدل‏سازی و پیش‏بینی اکسیژن مورد نیاز بیولوژیکی (BOD) در رودخانۀ کارون واقع در غرب کشور ایران ارزیابی شد. به‌منظور بهبود نتایج شبیه‏سازی از آنالیز موجک به‌عنوان مدل ترکیبی استفاده شد. سری زمانی ماهانۀ شاخص BOD رودخانۀ کارون در ایستگاه ملاثانی به‌مدت 13سال‌ (1381‌ـ 1393) و با استفاده از متغیرهای کمکی اکسیژن محلول (DO)، جریان رودخانه و دمای ماهانه شبیه‏سازی شد. بهترین ورودی مدل‏های به‌کار گرفته‌شده با استفاده از روش تجزیه و تحلیل مؤلفه‏های اصلی (PCA) انتخاب شد. برای ارزیابی و مقایسۀ عملکرد مدل‏ها از جذر میانگین مربعات خطا (RMSE)، ضریب تعیین (R2) و معیار اطلاعاتی آکائیک (AIC) استفاده شد. نتایج به‌دست‌آمده بیانگر آن بود که شبکۀ عصبی مصنوعی میزان خطای 0412/0 و ضریب تعیین 76/0 دارد و اعمال تبدیل موجک روی داده‏های ورودی مدل، سبب بهبود نتایج تا ضریب تعیین 89/0 و میزان خطای 0273/0 شد.
 
 

کلیدواژه‌ها

موضوعات


 
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دوره 3، شماره 4
دی 1395
صفحه 569-585
  • تاریخ دریافت: 20 آبان 1395
  • تاریخ بازنگری: 19 آذر 1395
  • تاریخ پذیرش: 01 دی 1395
  • تاریخ اولین انتشار: 01 دی 1395
  • تاریخ انتشار: 01 دی 1395