ارزیابی کارکرد مدل های برنامه ریزی ژنتیک و خودهمبسته میانگین متحرک در پیش بینی آب‌دهی روزانه در آبخیز امامه

نوع مقاله : پژوهشی

نویسندگان

1 دکترای علوم و مهندسی آبخیزداری

2 دانشجوی کارشناسی ارشد کنترل و مدیریت بیابان، دانشکده منابع طبیعی، دانشگاه یزد

3 استاد گروه آموزش،برنامه ریزی و مدیریت محیط زیست، دانشکده محیط زیست، دانشگاه تهران، کرج

4 مدرس دانشگاه فنی و حرفه ای، یزد

5 دانشیار گروه احیای مناطق خشک و کوهستانی، دانشکده منابع طبیعی، دانشگاه تهران، کرج

چکیده

کم‌بودمنابعآبو توجه به توسعه­ی پایدار،تأمینآبرا برای همه‌ی نیازهایموجودناممکنکردهاست. از آن­جا که پیش­بینیدقیقجریان رود­هادر مدیریتمنابعآب اهمیتبسزایی دارد، آب‌دهی رود با کاربرد مدل­های برنامه‏ریزی ژنتیک و خودهمبسته‌ی میانگین متحرک در آبخیز امامه، استان تهران مدل­سازی و پیش‌بینی شد. از داده‏های درازمدت باران، دما، آب‌دهی، رطوبت نسبی و تبخیر استفاده شد. نتایج نشان داد که برنامه‏ریزی ژنتیک خطای کم­تری دارد و توانسته‌است به‌خوبی آب‌دهی مشاهده‌یی را تخمین بزند. مدل 54 با ورودی­های دما، باران، و تأخیرهای باران تا دو روز، و رطوبت‏نسبی و تبخیر و تأخیر جریان تا دو روز،بهترین مدل با خطای 0/001، 0/031، و 0/009 در مرحله­ی آموزش، و 0/001، 0/032، و 0/009 در مرحله­ی آزمایشبود. علاوه بر این، خطای مدل‌های خطی خودهمبسته‌ی میانگین متحرک بسیار بیش‌تر است، و نه‌تنها در آب‌دهی­های بیش‌تر، بل‌که در آب‌دهی­های کم همکارکرد مناسبی ندارد، و نتوانسته­است نتیجه‌ی رضایت­بخشی به‌دست دهد. استفاده از مدل برنامه­ریزی ژنتیک به‌دلیل دقت بسیار زیاد با عمل‌گرهای اصلی و داده­های به‌معیارشده توصیه می­شود. 

کلیدواژه‌ها

عنوان مقاله [English]

An Assessment the Performance of Genetic Programming and Auto Regresive Moving Average on the Daily Discharge Prediction (Case study: the Amameh Watershed)

نویسندگان [English]

  • Mahboobeh Motamednia 1
  • Kamal Karimi Zarchi 2
  • Aahmad Nohegar 3
  • Maryam Saberi Anari 4
  • Arash Malekian 5
1 Ph.D. of Watershed Management Science and Engineering
2 M.Sc., student of management and control of desert, natural resources and desert studies, The Head of the Department of Natural Resources, Bafgh, Yazd, Iran
3 Professor of Learning, Programming and Environment Management Department, Environment Faculty, Tehran University, Karaj
4 Instructor of Technical and Vocational University, Yazd, Iran
5 Associate Professor, Faculty of Natural Resources, University of Tehran, Karaj
چکیده [English]

Shortage of water resources and the growing concern about the sustainable development have made the water supply for all of the potential needs nearly impossible.As an accurate prediction of river discharge is very important in water resources management, the development of a model to predict discharge has been carried out using the genetic programming and auto regression moving average on the Amameh Watershed located in the Province of Tehran. The long-term rainfall, temperature, discharge, relative humidity, and evaporation data have been used. Satisfactorily, the results showed that genetic programming had a lower error and could estimate the observed discharge. Furthermore, the number 54 model with inputs of temperature, rain, the delay in rainfall of up to two days, relative humidity, evaporation, and the delay in discharge of up to two days were considered as the best fit model with the errors of 0.001, 0.031 and 0.009 in the training stage and 0.002 , 0.032, and 0.009 at the testing stage respectively. On the other hand, the linear auto regression moving average models showed a much higher error; they could neither predict the high discharge, nor low flow and have not been able to provide satisfactory results. Therefore, the application of a genetic programming model is recommended due toits high precision with the main operators and the standardized data.

کلیدواژه‌ها [English]

  • Amameh Watershed
  • auto regressive moving average
  • genetic programming
  • river flow modeling

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سابقه مقاله

  • تاریخ دریافت: 16 مهر 1397
  • تاریخ بازنگری: 19 آبان 1397
  • تاریخ پذیرش: 06 آذر 1397

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