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ارزیابی کارکرد مدل های برنامه ریزی ژنتیک و خودهمبسته میانگین متحرک در پیش بینی آبدهی روزانه در آبخیز امامه | ||
| پژوهش های آبخیزداری | ||
| مقاله 2، دوره 32، شماره 2 - شماره پیاپی 123، تیر 1398، صفحه 2-18 اصل مقاله (978.34 K) | ||
| نوع مقاله: پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/wmej.2018.123630.1157 | ||
| نویسندگان | ||
| محبوبه معتمدنیا* 1؛ کمال کریمی زارچی2؛ احمد نوحه گر3؛ مریم صابری اناری4؛ آرش ملکیان5 | ||
| 1دکترای علوم و مهندسی آبخیزداری | ||
| 2دانشجوی کارشناسی ارشد کنترل و مدیریت بیابان، دانشکده منابع طبیعی، دانشگاه یزد | ||
| 3استاد گروه آموزش،برنامه ریزی و مدیریت محیط زیست، دانشکده محیط زیست، دانشگاه تهران، کرج | ||
| 4مدرس دانشگاه فنی و حرفه ای، یزد | ||
| 5دانشیار 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 | ||
| برنامهریزی ژنتیک؛ آبخیز امامه؛ مدل سازی جریان رود؛ مدل های خودهمبستهی میانگین متحرک | ||
| 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 Motamednia1؛ Kamal Karimi Zarchi2؛ Aahmad Nohegar3؛ Maryam Saberi Anari4؛ Arash Malekian5 | ||
| 1Ph.D. of Watershed Management Science and Engineering | ||
| 2M.Sc., student of management and control of desert, natural resources and desert studies, The Head of the Department of Natural Resources, Bafgh, Yazd, Iran | ||
| 3Professor of Learning, Programming and Environment Management Department, Environment Faculty, Tehran University, Karaj | ||
| 4Instructor of Technical and Vocational University, Yazd, Iran | ||
| 5Associate 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 | ||
| مراجع | ||
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