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بازیابی شدت شوری منابع گسترده در رودخانه با استفاده از رویکرد معکوس شبیهسازی- بهینه سازی | ||
| نشریه مهندسی عمران امیرکبیر | ||
| مقاله 8، دوره 55، شماره 12، 1402، صفحه 2503-2526 اصل مقاله (1.96 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22060/ceej.2023.21675.7791 | ||
| نویسندگان | ||
| فاطمه یوسفوند1؛ جمال محمدولی سامانی* 1؛ حسین محمدولی سامانی2 | ||
| 1گروه سازههای آبی، دانشگاه تربیت مدرس، تهران، ایران | ||
| 2گروه مهندسی عمران، دانشگاه شهید چمران، اهواز، ایران | ||
| چکیده | ||
| در سالهای اخیر مسئله شناسایی منابع آلاینده در رودخانهها یکی از پرتوجهترین موضوعات تحقیقات علمی در حوزهی آب بوده است. در عمدهی پژوهشهای انجامشده، منابع آلاینده، نقطهای در نظر گرفته شدهاند و برای بازیابی شدت آلاینده نیاز است، یک نقطهی شاهد در پاییندست هر منبع در نظر گرفته شود. در این پژوهش محلهای ورود آب زیرزمینی به رودخانه، به عنوان منابعی گسترده با مکان و طول معلوم درنظر گرفته میشوند و هدف بازیابی شدت منابع، تنها با استفاده از یک نقطه شاهد در پایاب رودخانه است. منابع موردنظر، منابعی گسترده با بارگذاری ثابت و در فاصله قابل توجهی از هم هستند. وجود فاصله، مانع از اختلاط کامل غلظت در نقطه شاهد میشود. این امر و نیز ثابت بودن شدت بارگذاری باعث میشود بتوان تنها با یک نقطه شاهد، چند منبع گسترده را بازیابی کرد. بدین منظور حل معکوس معادله انتقال با استفاده از رویکرد شبیهسازی- بهینهسازی انجام میشود. در تهیه مدل معکوس از پیوند MIKE11 به عنوان شبیهساز و الگوریتم ژنتیک در MATLAB استفاده شده است. بازیابی چندین منبع گسترده با یک نقطه شاهد، مهمترین نقطه قوت پژوهش حاضر میباشد. صحتسنجی مدل توسط مثالهای فرضی، 40 کیلومتر از رودخانه کارون و نیز اعمال 5 و 15 درصد خطا به دادههای مشاهداتی انجام شد. نتایج نشان میدهد مدل قادر است، نه تنها با یک نقطه شاهد بلکه فقط با یک داده از نمودار غلظت-زمان در نقطه شاهد که تحت تاثیر منبع موردنظر باشد، شدت را بازیابی نماید. دقت مدل در بازیابی شدت منابع، براساس شاخصهای آماری بیش از 99 درصد میباشد. | ||
| کلیدواژهها | ||
| مدل معکوس؛ رویکرد شبیهسازی - بهینهسازی؛ مدل عددی MIKE11؛ منابع گسترده شوری؛ الگوریتم ژنتیک | ||
| موضوعات | ||
| کیفیت آب؛ مدیریت منابع آب؛ مهندسی رودخانه | ||
| عنوان مقاله [English] | ||
| Recovering the salinity intensity of distributed sources in the river using inverse simulation-optimization approach | ||
| نویسندگان [English] | ||
| fatemeh yousofvand1؛ Jamal Mohammad Vali Samani1؛ hossein Mohammad Vali Samani2 | ||
| 1Department of Water Structures Engineering/Faculty of Agriculture,/Tarbiat Modares University/ tehran/iran | ||
| 2Department of Water Engineering/ Faculty of Civil Engineering/Shahid Chamran University/ahvaz/iran | ||
| چکیده [English] | ||
| In recent years, the issue of identifying the polluting sources in the rivers has been one of the most important topics in scientific research in the field of water. In the main research, the pollutant sources have been considered as the point sources, and in order to recover pollutant concentration, it is necessary to have an observation point for each source. In this study, the places where groundwater enters to river are considered as distributed sources with known locations and length and the goal is to recover the intensity of sources, using only one observation point. The sources which considered are distributed sources with constant loading and significant distance from each other. The existence of distance among sources prevents the complete mixing of concentration at the observation point. This matter and also the constant intensity of loading, makes it possible to recover several distributed sources using only one observation point. For this purpose, the inverse solution of the advection-dispersion equation is done using the simulation-optimization approach. To design the backward model, MIKE11, linked with a genetic algorithm in MATLAB. Considering one observation point for recovering the intensity of several distributed sources is the advantage of the present study. The model was verified by using hypothetical examples, 40km section of Karun River, and by applying 5 and 15 percent noise to the observation data. The results demonstrate that the backward model can recover the intensity of several sources not only with one observation point but also with data from the concentration versus time curve at the observation point. The accuracy of the model in recovering resource intensity, according to statistical indicators, is more than 99%. | ||
| کلیدواژهها [English] | ||
| Backward Model, simulation-optimization approach, MIKE11 numerical model, the salinity distributed sources, genetic algorithm | ||
| مراجع | ||
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