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بررسی عدم قطعیت مدلهای هوش مصنوعی در برآورد جریان رودخانه (مطالعه موردی: رودخانه کارون) | ||
| نشریه مهندسی عمران امیرکبیر | ||
| مقاله 18، دوره 54، شماره 11، بهمن 1401، صفحه 4383-4396 اصل مقاله (1.41 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22060/ceej.2022.21208.7653 | ||
| نویسندگان | ||
| یاسر مهدی زاده زارع اناری1؛ محمد نجف زاده1؛ صدیقه انوری* 2 | ||
| 1دانشکده عمران و نقشه برداری، دانشگاه تحصیلات تکمیلی صنعتی و فناوری پیشرفته، کرمان، ایران | ||
| 2پژوهشکده علوم محیطی، دانشگاه تحصیلات تکمیلی صنعتی و فناوری پیشرفته، کرمان، ایران | ||
| چکیده | ||
| پیش‌بینی دقیق فرآیندهای هیدرولوژیکی و احتساب عدم قطعیت‌های آنها، از جمله چالش‌های اساسی در حوضه مدیریت منابع اب است. هدف مقاله حاضر، پیش‌بینی جریان ماهانه رودخانه کارون در محل ایستگاه هیدرومتری ارمند با استفاده از روش‌های هوش مصنوعی (AI) می‌باشد. بکارگیری رویکرد شبیه‌سازی مونت کارلو (MCS) جهت احتساب عدم قطعیت پیش‌بینی‌های نامبرده و نیز مقایسه عملکرد آنها از اهداف دیگر مقاله محسوب می‌شود. بدین منظور از مدلهای مبتنی بر AI شامل برنامه‌نویسی بیان ژن(GEP) ، اسپیلاین رگرسیون تطبیقی چند متغیره (MARS) و درخت مدل (MT) استفاده شده است. همچنین آمار 28 ساله جریان رودخانه کارون (سالهای 1387-1360) استفاده و برای تولید اعداد تصادفی، روش پارامتریک توماس–فیرینگ (TF) بکار گرفته شد. نتایج ارزیابی عملکرد مدلها با شاخص‌هایی همچون ضریب همبستگی (R)، میانگین قدر مطلق خطا (MAE) و ریشه میانگین مربعات خطا (RMSE)، نشان داد که مدل MT در هر دو مرحله آموزش و آزمون عملکرد بهتری نسبت به سایرین داشته است. شاخص‌های دقت مدل برای مرحله آموزش مدل MT برابر R=0.841 و RMSE=36.789 m^3/s بوده است در حالیکه این شاخص‌ها برای مرحله آزمون برابر با R=0.87 و RMSE=44.253 m^3/s می‌باشد. نتایج ارزیابی عدم قطعیت پیش‌بینی‌ها توسط مدلهای MARS، GEP و MT نشان داد که مدل MT با داشتن شاخص R-factor=1.67 و 95PPU=55.5% بهترین عملکرد را برای احتساب عدم قطعیت داشته است. | ||
| کلیدواژه‌ها | ||
| پیش‌بینی جریان؛ مدلهای هوش مصنوعی؛ تحلیل عدم قطعیت؛ رویکرد مونت کارلو | ||
| موضوعات | ||
| مدیریت منابع آب | ||
| عنوان مقاله [English] | ||
| Uncertainty Analysis of Artificial Intelligence Models in Forecasting River Flow (Case Study: Karun River) | ||
| نویسندگان [English] | ||
| Yaser Mehdizadeh Zare Anari1؛ Mohammad Najafzadeh1؛ Sedigheh Anvari2 | ||
| 1Graduate Student, Water Engineering Department, Faculty of Civil and Surveying Engineering, Graduate University of Advanced Technology, Kerman, Iran | ||
| 2Assistant Professor, Department of Ecology, Institute of Science and High Technology and Environmental Science, Graduate University of Advanced Technology, Kerman, Iran | ||
| چکیده [English] | ||
| An accurate estimation of the discharge flow of natural streams plays a key role in irrigation planning, the design of bridges embedded in waterways, the management of reservoirs and dams, and the design of flood-warning systems. In recent decades, several studies have been conducted using artificial intelligence (AI) to accurately estimate the flow. Despite the proven accuracy level of AI methods, in many cases, there are uncertainties that occur for a variety of reasons. Insufficient knowledge of these uncertainties in the flow modeling process can have irreversible effects. In this research, monthly flow data, measured from Armand hydrometric station, located in Karun River basin, during a-28 year period (from 1980 to 2008) were used. First, the Thomas and Fiering method was used to generate flow series data and consider them as input variables. Then, flow forecast modeling was performed by three AI methods, namely Model Tree (MT), Gene Expression Programming (GEP), and Multivariate Adaptive Regression Spline (MARS). Statistical indicators such as correlation coefficient (R) and Root Mean Square Error (RMSE) were used to evaluate the accuracy of the models. In terms of the training stage, the MARS model with R=0.839 and RMSE=28.624 m3/s performed better than the other two models. Additionally, in the testing stage, the MT model with R=0.784 and RMSE=34.441 m3/s showed a more appropriate performance than other models. The Monte Carlo simulation method was used to calculate the uncertainty of the models, so the results showed that the r-factor parameter, which was the average width of the confidence band in the MT model, was equal to 1.67, indicating a lower and more optimal number compared to MARS (1.92) and GEP (2.025) models. Moreover, the usability of the statistical criterion for quantifying uncertainty, (known as 95PPU) indicated that the GEP model with 95PPU of 64% was selected as a more appropriate percentage than MARS (61%) and MT-M5 (55%). | ||
| کلیدواژه‌ها [English] | ||
| Flow prediction, Artificial intelligence models, Uncertainty analysis, Monte-Carlo technique | ||
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| مراجع | ||
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