توسعه مدل شبکه عصبی بر مبنای توابع آموزش گرادیان مزدوج و پس‌انتشار ارتجاعی برای پیشبینی ضریب انتشار طولی رودخانه‌ها
محورهای موضوعی : برگرفته از پایان نامهروح اله نوری 1 , بهزاد قیاسی 2 , عبدالرضا کرباسی 3 , امین سارنگ 4
1 - دانشگاه تهران
2 - دانشگاه تهران
3 - دانشگاه تهران
4 - دانشگاه تهران
کلید واژه: ضریب پخش طولی, آلودگی آب, مدلهای هوشمند, الگوریتم آموزش,
چکیده مقاله :
گام اساسی در مدل‌سازی کیفی محیط‌های آبی یک بعدی مانند رودخانه‌ها، تعیین ضریب انتشار طولی (LDC) برای معادله‌ی انتقال-پخش آلاینده‌ها است. در این مقاله برای پیشبینی LDC، مدل شبکه‌ی عصبی مصنوعی (ANN) بر مبنای الگوریتم‌های آموزشی با رویکرد عددی و همچنین رویکرد اکتشافی توسعه داده شده است. برای این منظور توابع آموزشی گرادیان مزدوج شامل توابع فلچر-ریوس، پولاک-ریبره، پاول-بیل و گرادیان مزدوج مقیاسدار از دسته الگوریتم‌های عددی و همچنین تابع پس‌انتشار ارتجاعی از دسته الگوریتم‌های اکتشافی برای بهینه‌سازی پارامترهای مدل ANN استفاده شدند. در مرحله‌ی بعد با استفاده از آمارههای بررسی شده برای ارزیابی نتایج، بهترین مدل با ساختار شامل هر یک از توابع نامبرده انتخاب شدند و در ادامه از بین مدلهای منتخب، مدلی که بهترین عملکرد را داشت، یعنی مدل با تابع آموزش پس‌انتشار ارتجاعی، با توجه به آماره‌ی نسبت تفاوت توسعه یافته (DDR)، به عنوان نتیجه نهایی این مقاله برگزیده شد. در پایان نیز برای ارزیابی بهتر نتایج تحقیق، رویکردی مقایسه‌ای بین نتیجه بهترین مدل توسعه داده شده با دیگر مطالعات انجام گرفته به وسیله مدلهای هوشمند انجام شد که یافته‌ها حاکی از عملکرد برتر مدل پس‌انتشار ارتجاعی بود.
Determining the longitudinal dispersion coefficient (LDC) for Advection-Diffusion equation is the first step in water quality modeling for one-dimensional water bodies such as rivers. In this research, an artificial neural network (ANN) model has been developed based on the standard numerical optimization algorithms and heuristic techniques to determine the LDC. In this regard, conjugate gradient (CG) training functions including Fletcher-Reeves, Polak-Ribiére, Powell-Beale and scaled conjugate gradient functions from the standard numerical optimization algorithms category and resilient back-propagation (Trainrp) training function from the heuristic algorithms, have been applied to optimizing ANN parameters. Then, the best model has been selected for each of the training functions according to indices that are used to evaluate results. Among the selected models, the ANN model with the Trainrp training function has been selected as the best model to predict the LDC due to DDR statistic. Finally, a comparison has been undertaken between the selected model and other suggested artificial intelligent methods by the researchers. According to the implemented comparisons, the Trainrp function acquired the best performance.
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