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توسعه و ارزیابی فناوری اندازه‌گیری غلظت رسوبات معلق در محیط‌های آبی به روش اندازه-گیری نوری | ||
| نشریه مهندسی عمران امیرکبیر | ||
| دوره 54، شماره 5، مرداد 1401، صفحه 2051-2064 اصل مقاله (1.12 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22060/ceej.2021.20356.7402 | ||
| نویسندگان | ||
| فاطمه برزگری بنادکوکی* 1؛ َشروین فقیهی راد2؛ محمدتقی دستورانی3 | ||
| 1دانشکده فنی مهندسی، دانشگاه پیام نور، تهران، ایران | ||
| 2پژوهشکده مهندسی هیدرولیک و محیط های آبی، موسسه تحقیقات آب، وزارت نیرو، تهران، ایران | ||
| 3دانشکده منابع طبیعی و محیط زیست، دانشگاه فردوسی مشهد، مشهد، ایران | ||
| چکیده | ||
| پایش رسوبات معلق نقش مهمی در شناخت رفتار رودخانه، شناسایی کانون های فرسایش و رسوب و مدیریت بهتر اراضی آبخیزها ایفا می‌کند. در حال حاضر به دلیل هزینه های بالای پایش مستمر رسوبات معلق، برنامه ریزی و مدلسازی جهت مهار فرسایش با چالش‌های جدی روبروست. هدف از این پژوهش، توسعه فناوری و ارزیابی دستگاه بازتاب نوری اندازه گیر رسوب معلق با سامانه توام چند منبع نوری همراه با مدل سازی هوش مصنوعی می باشد. دستگاه مذکور پس از ساخت، طی مراحل مختلف آزمایشی در آزمایشگاه هیدرولیک موسسه تحقیقات آب وزارت نیرو مورد بررسی قرار گرفت. بررسی عملکرد دستگاه طی دو مرحله واسنجی و صحت سنجی انجام شد. در فرایند بررسی، تعداد 40 نمونه رسوب تولید و نمونه برداری شد که 70% آنها برای آموزش دستگاه و 30% باقی‌مانده داده ها جهت صحت سنجی استفاده شد. از کدورت سنج مرجع و نمونه برداری دستی جهت آزمون درستی عملکرد دستگاه استفاده گردید. منحنی های ترسیم شده، بیانگر همبستگی بسیار خوب بین عدد نوری ثبت شده توسط دستگاه و غلظت رسوب معلق می باشد. به منظور ارتقاء نتایج پیش بینی دستگاه، از روش هوشمند مبتنی بر آمار رگرسیون ماشین بردار (SVR) و همچنین شبکه عصبی پرسپترون چند لایه (MLP) استفاده گردید. در نهایت نتایج حاصله توسط شاخص های میانگین خطای مطلق (MAPE)، مجذور میانگین مربعات خطا (RMSE)، ضریب ناش ساتکلیف(NSE) ، ضریب همبستگی (R)و ضریب تبیین (R2)مورد ارزیابی قرار گرفتند. نتایج نشان داد استفاده از مدل MLP در مقایسه با نتایج حاصل از دستگاه بدون اعمال هوش مصنوعی و نیز در مقایسه با مدل SVR در بهبود نتایج پیش بینی رسوب معلق دارد. مقادیر شاخص های ارزیابی برای مدل MLP به ترتیب برابر با 0/023، 7/608، 0/997، 0/999 و 0/999 می باشد. | ||
| کلیدواژه‌ها | ||
| بازتاب نوری؛ شبکه عصبی پرسپترون چندلایه؛ کدورت‌سنج؛ رگرسیون بردار پشتیبان؛ هوش مصنوعی | ||
| موضوعات | ||
| کیفیت آب؛ هیدرولیک رسوب | ||
| عنوان مقاله [English] | ||
| Assessment and development of optical technology for continuous suspended sediment measurement in aquatic environments | ||
| نویسندگان [English] | ||
| Fatemeh Barzagari1؛ Shervin Faghihi Rad2؛ Mohammad Taghi Dastorani3 | ||
| 1Faculty of Engineering, Payame Noor University, Tehran, Iran | ||
| 2Department of Hydraulic Engineering and Hydro-Environment, Water Research Institute, Tehran, Iran | ||
| 3Faculty of Natural Resources and Environment, Ferdowsi University of Mashhad, Mashhad, Iran | ||
| چکیده [English] | ||
| The monitoring of fluvial suspended sediment transport plays an important role in the assessment of morphological processes, river behavior, identifying erosion and sediment loss zones and better watershed management. In order to eliminate information deficiencies and achieve a suitable database for suspended load, it is necessary to equip hydrometric stations with instruments for continuous and indirect monitoring of suspended sediment. The aim of this research is to construct and validate an optical sensor with a multi-beam ratio technology and artificial intelligence-based modeling (MLP & SVR) for suspended sediment measuring. After the implementation of the new technology, the performance of the device was evaluated in two stages, including calibration and validation. To attain this, various experimental tests were carried out in the hydraulic laboratory of the Water Research Institute of the Ministry of Energy. Reference turbidity meter and total suspended solids (TSS) were used to test the performance of the OBS technology. In the calibration stage, 70% of TSS data were used and the remaining 30% of data were used to validate optical technology. The plotted calibration curves show a very good correlation between the optical voltage recorded by the sensors and the suspended sediment concentration. Also, SVR & MLP models were employed to improve the results of suspended load prediction. The performance of the optical device and also optical device with intelligence models were evaluated through four statistical indices, namely, Mean Absolute Percentage Error (MAPE), Root mean square Error (RMSE), Nash–Sutcliffe coefficient (NSE), correlation coefficient (R) and coefficient of determination (R2). The results of this stage showed that the intelligence modeling could result in improvements in suspended load reported by optical technology. The best improvements were obtained by MLP-optical technology. The results showed that values of validation indicators for MLP model are equal to 0.023, 7.608, 0.99, 0.99 and 0.99, respectively, which indicates the proper performance of the technology. | ||
| کلیدواژه‌ها [English] | ||
| Artificial Intelligence, Multi-Layer Perceptron Neural Network, Optical Back Scatter, Turbidity Meter, Support Vector Regression | ||
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| مراجع | ||
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