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استفاده از شبکه عصبی NARX به عنوان مدل جایگزین برای شبیه‌سازی بلند مدت شوری خروجی از مخازن دارای لایه‌بندی کیفی | ||
| تحقیقات آب و خاک ایران | ||
| دوره 51، شماره 8، آبان 1399، صفحه 1971-1982 اصل مقاله (1.32 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijswr.2020.299151.668532 | ||
| نویسندگان | ||
| مراد اسدی1؛ جمال محمدولی سامانی* 2؛ حسین محمدولی‌سامانی3 | ||
| 1گروه سازه‌های آبی، دانشکده کشاورزی، دانشگاه تربیت مدرس، تهران، ایران | ||
| 2استاد بخش مهندسی عمران، دانشگاه آزاد اسلامی واحد شهر قدس، تهران، ایران | ||
| 3گروه مهندسی عمران، دانشکده فنی دانشگاه شهید چمران، اهواز، ایران | ||
| چکیده | ||
| برنامه CE-QUAL-W2 یک مدل فیزیکی با اطمینان‌پذیری بالا جهت شبیه‌سازی هیدرودینامیکی-کیفی مخازن بوده که هزینه محاسباتی زیادی دارد. بنابراین یافتن مدل‌های جایگزین که نتایج این مدل را با دقت مطلوب و در زمان اندکی برآورد کنند از اهمیت کاربردی بالایی برخوردار است. در این تحقیق قابلیت مدل شبکه عصبی NARX به عنوان مدل جایگزین CE-QUAL-W2 جهت پیش‌بینی نتایج بلند مدت شوری خروجی از مخزن بررسی شده است. برای این منظور مدل CE-QUAL-W2 مخزن سد گتوند علیا تهیه و پس از واسنجی، برای شبیه‌سازی شوری خروجی از مخزن در یک دوره زمانی 10 ساله استفاده گردید. با توجه به امکان تخلیه از دریچه‌های مختلف مخزن، با تغییر ماهیانه نسبت تخلیه دریچه‌ها مسائل متعددی تعریف و کتابخانه‌ای از نتایج مدل فیزیکی تشکیل شد. سپس با معرفی سناریوهای مختلف معماری شبکه عصبی NARX، آموزش آن‌ها با استفاده از کتابخانه نتایج انجام شد. نتایج حاصل از سناریوهای مختلف بیانگر توانایی بالای شبکه عصبی NARX در برآورد روند شوری خروجی از مخزن بوده و ضریب تعیین همواره بیش از 91/0 است. در سناریوی منتخب ضریب تعیین 95/0، میانگین درصد خطای مطلق و ضریب نش-ساتکلیف به ترتیب 7/8 درصد و 79/0 بوده و انطباق خوبی بین نتایج دو مدل مشاهده می‌شود. مدت زمان شبیه‌سازی بلند مدت مخزن گتوند با استفاده از مدل شبکه عصبی کم‌تر از 0.06 درصد زمان لازم برای اجرای مدل فیزیکی است. نتایج این تحقیق نشان می‌دهد که مدل NARX را می‌توان جهت پیش‌بینی بلند مدت شوری خروجی از مخازن به عنوان مدل جایگزین برای CE-QUAL-W2 بکار برده و هم‌زمان هزینه‌ی محاسبات را به طور چشمگیری کاهش داد. | ||
| کلیدواژه‌ها | ||
| شبکه عصبی NARX؛ مدل CE-QUAL-W2؛ مدل جایگزین؛ شبیه‌سازی بلند مدت؛ شوری خروجی از مخزن | ||
| عنوان مقاله [English] | ||
| Application of NARX Neural Network as Surrogate Model to Long-term Simulation of the Outlet Salinity from Strong Stratified Reservoirs | ||
| نویسندگان [English] | ||
| Morad Asadi1؛ Jamal Mohamad Vali Samani2؛ Hossein Mohamad Vali Samani3 | ||
| 1Water Structures Engineering Department, Tarbiat Modares University, Tehran, Iran | ||
| 2Department of Civil Engineering, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran | ||
| 3Department of Civil Engineering, Faculty of Engineering, Shahid Chamran University, Ahwaz, Iran | ||
| چکیده [English] | ||
| The CE-QUAL-W2 program as a physical model for quality and hydrodynamic simulation of water reservoirs has a high computational cost. Therefore, finding surrogate models to give optimal results in short term would have a great practical importance especially in simulation-optimization problems. In this study, the capability of the NARX model as a surrogate model was investigated to simulate the outlet salinity from strongly stratified reservoirs. For this purpose, the CE-QUAL-W2 model was used and calibrated to simulate the outlet salinity of the Upper Gotvand Reservoir over 10 years. Regarding the possibility of release from different reservoir intakes, by monthly change of release ratios, several problems were defined and a library of the physical model results was formed. Then different NARX architecture scenarios were introduced and trained using the library results. The results obtained from different scenarios indicate that the NARX neural network model has a high capability to simulate the CE-QUAL-W2 model results of outflow salinity, so that the correlation coefficient is always above 0.91. In the selected scenario, a very good agreement is observed between the results of the two models, with a correlation coefficient of 0.95, mean absolute percentage error of 8.7% and Nash-Sutcliffe coefficient of 0.79. The simulation time required for the NARX neural network model is less than 0.06% of the time required to run the physical model for the same problem. The results show that the NARX model can be used as a suitable surrogate model for CE-QUAL-W2 to predict the long-term reservoir outlet salinity and reduces the cost of computing while maintains accuracy. | ||
| کلیدواژه‌ها [English] | ||
| NARX neural network model, CE-QUAL-W2 physical model, Surrogate Model, Long-term simulation, Reservoir outlet salinity | ||
| مراجع | ||
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