پیش‌بینی ماهانه جریان با استفاده از ماشین بردار پشتیبان بر مبنای آنالیز مؤلفه اصلی

نوع مقاله : یاداشت‌ فنی

نویسندگان

1 کارشناس مؤسسه تحقیقات آب وزارت نیرو، دانشجوی دکترای مهندسی محیط زیست، دانشکده تحصیلات تکمیلی محیط زیست، دانشگاه تهران

2 مدیر عامل شرکت عمران زیست آزما (CELCO)، دانشجوی دکترای مهندسی محیط زیست، دانشکده تحصیلات تکمیلی محیط زیست، دانشگاه تهران

3 دانشجوی دکترای مهندسی عمران آب، دانشکده فنی و مهندسی، دانشگاه آزاد اسلامی، واحد علوم و تحقیقات، تهران

4 کارشناس مؤسسه تحقیقات آب وزارت نیرو، دانشجوی دکترای سازه‌های آبی، دانشکده کشاورزی، دانشگاه تربیت مدرس

چکیده

هدف اصلی این تحقیق بررسی تأثیر انتخاب متغیرهای ورودی با استفاده از آنالیز مؤلفه اصلی (PCA) بر عملکرد مدل ماشین بردار پشتیبان (SVM) برای پیش‌بینی ماهانه دبی رودخانه بود. به این منظور ابتدا با استفاده از 18 متغیر ورودی به مدل SVM، دبی جریان ماهانه پیش‌بینی شد. سپس با استفاده از PCA تعداد متغیرهای ورودی به مدل SVM از 18 متغیر به 5 مؤلفه کاهش یافت. در نهایت با استفاده از آماره توسعه یافته توسط نویسندگان مقاله، عملکرد مدل‌های ارائه شده (SVM و PCA-SVM) مورد ارزیابی قرار گرفت. یافته‌های این تحقیق نشان داد که پیش‌پردازش متغیرهای ورودی به مدل SVM با استفاده از PCA، بهبود عملکرد مدل SVM را به همراه داشته است.
 
 

کلیدواژه‌ها


عنوان مقاله [English]

Monthly Stream Flow Prediction Using Support Vector Machine Based on Principal Component Analysis

نویسندگان [English]

  • Roohollah Noori 1
  • Amir Khakpour 2
  • Majid Dehghani 3
  • Ashkan Farokhnia 4
1 Member of Water Research Institute, Ministry of Energy, Ph.D Student of Environmental Eng., Graduate Faculty of Environment, University of Tehran
2 Management Director of CELCO , and Ph.D Student of Environmental Eng., Graduate Faculty of Environment, University of Tehran
3 Ph.D Student of Hydraulic Eng., Dept. of Eng., Islamic Azad University, Science and Research Branch, Tehran
4 Member of Water Research Institute, Ministry of Energy, Ph.D. Student of Hydraulic Structure, College of Agriculture, Tarbiyat Modarres University, Tehran
چکیده [English]

The main goal of this research is to evaluate the role of input selection by Principal Component Analysis (PCA) on Support Vector Machine (SVM) performance for monthly stream flow prediction. For this purpose, SVM is used to predict monthly flow as a function of 18 input variables. PCA is subsequently employed to reduce the number of input variables from 18 to 5 PCs which are finally fed into the SVM model. SVM and PCA-SVM models are evaluated in terms of their performance using a developed statistic by the authors. Findings show that preprocessing of input variables by PCA improved SVM performance.
 
 

کلیدواژه‌ها [English]

  • Support Vector Machine
  • Principal component analysis
  • Monthly Stream Flow
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