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استفاده از مدل درختی M5 برای تعیین ضریب دبی سرریز لبه پهن مستطیلی | ||
| نشریه مهندسی عمران امیرکبیر | ||
| مقاله 1، دوره 54، شماره 12، اسفند 1401، صفحه 4435-4458 اصل مقاله (3.11 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22060/ceej.2022.18990.7021 | ||
| نویسندگان | ||
| فرزین سلماسی* ؛ فرناز نهرین؛ علی طاهری اقدم | ||
| دانشکده کشاورزی، دانشگاه تبریز، تبریز، ایران | ||
| چکیده | ||
| سرریز لبه پهن سازه‌ای ساده برای اندازه‌گیری دبی جریان در کانال‌های انتقال آب است. ویژگی شکل آن و وزن زیاد آن باعث می‌شود تا اغلب به عنوان سرریز سدها نیز بکار رود. گاهی نیز این نوع سازه برای بدنه سد مد نظر قرار می‌گیرد. در این تحقیق توانایی شبکه عصبی مصنوعی و مدل درختی M5 در تخمین ضریب دبی (Cd) سرریز لبه پهن بررسی شده و نتایج این دو مدل با روش رگرسیون غیرخطی چند متغیره لجستیک قابل اعمال روی داده‌های گسسته مقایسه شده است. برای این کار چهار سری داده حاصل از تحقیقات متفاوت روی سرریزهای لبه پهن مستطیلی استفاده شده و پارامترهای بی‌بعد H1/L و H1/P به عنوان ورودی مدل‌ها در نظر گرفته شده و پارامتر هدف Cd به عنوان خروجی از مدل‌ها استخراج شده است. نتایج حاصله نشان داد که هر سه روش مذکور نتایج نسبتا دقیقی را جهت تخمین ضریب دبی سرریز لبه پهن ارائه می‌دهند (ANN: R=0.966 ، M5Rule: R=0.935 و Regression: R=0.84) ولی به دلیل ارائه روابط خطی ساده و قابل فهم توسط مدل درختی M5، این روش می-تواند به عنوان روشی کاربردی و جایگزین برای محاسبه ضریب دبی مد نظر قرار گیرد. همچنین H1/L مهمترین پارامتر دخیل در محاسبه ضریب دبی سرریز لبه پهن مستطیلی می‌باشد. تحلیل مدل درختی M5 نشان داد که 4 قانون با معادلات خطی متفاوت، الگوی تغییرات Cd را مدل می‌کند. تحلیل رگرسیون غیرخطی نشان داد که نقطه H1/L=0.22 محل تلاقی کلیه منحنی‌های تغییرات Cd به شمار می‌آید. | ||
| کلیدواژه‌ها | ||
| ضریب دبی؛ سرریز لبه پهن؛ شبکه‌های عصبی مصنوعی؛ رگرسیون غیرخطی؛ مدل درختی M5 | ||
| موضوعات | ||
| سازه های هیدرولیکی | ||
| عنوان مقاله [English] | ||
| Prediction of discharge coefficients for broad-crested weirs using expert systems | ||
| نویسندگان [English] | ||
| Farzin Salmasi؛ Farnaz Nahrain؛ Ali Taheri aghdam | ||
| Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran | ||
| چکیده [English] | ||
| Broad-crested weirs can be used to make discharge measurements in irrigation canals; the entrance of stepped weirs or chutes is sometimes designed as a broad-crested weir structure. These structures are also sometimes used for the dam body. In this study, the Artificial Neural Network (ANN) and M5 model tree methods are used to predict discharge coefficients (Cd) for broad-crested weirs. The results from these two models are compared with nonlinear regression equations. Four series of data obtained from different rectangular broad-crested weirs have been used and important dimensionless parameters have been defined. Results show that the ANN procedure is superior to the M5 model and regression approaches. The accuracy for ANN is quantified by R=0.966 and RMSE=0.038. All three methods are able to provide a reasonable prediction for Cd; the M5 model tree provides four linear equations that can be used to estimate Cd. The shape of the Cd contours shows that the effect of weir height (P) exceeds that of the weir length (L). | ||
| کلیدواژه‌ها [English] | ||
| Discharge coefficient, broad-crested weir, artificial neural network, nonlinear regression, M5 model tree | ||
| مراجع | ||
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