توسعه مدل ترکیبی IHACRES–XGBoost برای شبیه‌سازی رواناب روزانه در حوضه قره‌سو

کاکاپور, وحید; بیاتی خطیبی, مریم; صمدیان فرد, سعید

doi:10.22034/hyd.2025.68164.1800

توسعه مدل ترکیبی IHACRES–XGBoost برای شبیه‌سازی رواناب روزانه در حوضه قره‌سو

نوع مقاله : پژوهشی

نویسندگان

¹ دانشجوی دکتری سنجش از دور، گروه سنجش از دور، دانشکده برنامه‌ریزی و علوم محیطی، دانشگاه تبریز، ایران

² استاد گروه سنجش از دور، دانشگاه تبریز

³ دانشیار، گروه مهندسی آب، دانشگاه تبریز، تبریز، ایران

10.22034/hyd.2025.68164.1800

چکیده

در این پژوهش، با هدف بهبود دقت شبیه‌سازی رواناب روزانه در حوضه‌ی قره‌سو واقع در استان کرمانشاه، یک چارچوب مدل‌سازی ترکیبی مبتنی بر تلفیق مدل مفهومی IHACRES و الگوریتم‌های پیشرفته یادگیری ماشین توسعه داده شد. داده‌های مورد استفاده شامل بارش، دمای حداقل، حداکثر و میانگین و دبی رودخانه از ایستگاه‌های هیدرومتری پل‌کهنه و قورباغستان در بازه‌ی زمانی ۱۹۹۵ تا ۲۰۲۳ می‌باشند. در مرحله نخست، مدل نیمه‌توزیعی IHACRES جهت شبیه‌سازی فرآیند بارش–رواناب پیاده‌سازی گردید و با بهره‌گیری از الگوریتم ژنتیک،پارامترهای آن بهینه‌سازی شد. به‌منظور حذف نوسانات کوتاه‌مدت، یک فیلتر میانگین متحرک سه‌روزه بر خروجی مدل اعمال شد. سپس،با استفاده از خروجی IHACRES و مجموعه‌ای از متغیرهای مشتق‌شده شامل ویژگی‌های تأخیری، آماره‌های بارش و دما، شاخص‌های زمانی و خشکسالی، یک مدل یادگیری ماشین نوع XGBoost طراحی گردید. عملکرد مدل‌ها با شاخص‌های آماری RMSE و NSE در دو دوره آموزش و آزمون مورد ارزیابی قرار گرفت. نتایج نشان داد که مدل پایه‌ی IHACRES عملکردی در حد قابل قبول داشت (NSE≈0.44)، اما با اعمال فیلتر میانگین متحرک و بهینه‌سازی پارامترها، دقت آن به‌طور متوسط تا ۳۰٪ افزایش یافت. در نهایت، مدل ترکیبی IHACRES–XGBoost با دستیابی به مقدار NSE بیش از ۰.۹۷ و RMSE کمتر از ۱۰، بالاترین دقت را ارائه کرد. این یافته‌ها نشان‌دهنده‌ی کارایی بالا و پتانسیل بالای مدل‌های ترکیبی در ارتقاء پیش‌بینی رواناب روزانه می‌باشد.

کلیدواژه‌ها

موضوعات

هیدروژئومورفولوژی

عنوان مقاله [English]

Improving the hybrid IHACRES–XGBoost model for simulating daily runoff in the Gharesu Basin

نویسندگان [English]

vahid kakapor ¹
maryam bayatikhatibi ²
Saeed Saeed Samadianfard ³

¹ . Ph.D. Student in Remote Sensing, Department of Remote Sensing, Faculty of Environmental Planning and Sciences, University of TabrizIran

² Professor in Remote Sensing, Department of Remote Sensing, Faculty of Environmental Planning and Sciences, University of TabrizIran

³ Associate Professor, Department of Water Engineering, Tabriz University, Tabriz, Iran

چکیده [English]

In this study, with the aim of improving the accuracy of daily runoff simulation in the Ghareso basin located in Kermanshah province, a hybrid modeling framework was developed based on the integration of the IHACRES conceptual model and advanced machine learning algorithms. The data used included precipitation, minimum, maximum, and average temperatures, and river discharge from the Pol-e-Kohne and Qorbaghestan hydrometric stations during the period 1995 to 2023. In the first stage, the IHACRES semi-distributed model was implemented to simulate the precipitation-runoff process and its parameters were optimized using a genetic algorithm. In order to remove short-term fluctuations, a three-day moving average filter was applied to the model output. Then, using the IHACRES output and a set of derived variables including lag features, precipitation and temperature statistics, time and drought indices, an XGBoost type machine learning model was designed. The performance of the models was evaluated with the statistical indices RMSE and NSE in two training and testing periods. The results showed that the basic IHACRES model had an acceptable performance (NSE≈0.44), but by applying the moving average filter and optimizing the parameters, its accuracy increased by an average of 30%. Finally, the combined IHACRES–XGBoost model provided the highest accuracy by achieving an NSE value of more than 0.97 and an RMSE of less than 10. These findings indicate the high efficiency and high potential of the combined models in improving daily runoff forecasting.

کلیدواژه‌ها [English]

IHACRES
Genetic Algorithm
XGBoost
Runoff Simulation
Moving Average
Machine Learning
Nash–Sutcliffe Index Gharesu Basin

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