پیش‌بینی هوشمند جریان رودخانه با ترکیب الگوریتم‌های فراکاوشی شیطان تاسمانی و شاهین دم‌قرمز در حوضه‌ی دهگلان کردستان

معروفی نیا, ادریس; شرافتی, احمد; عبقری, هیراد; حسن زاده, یوسف

doi:10.22034/hyd.2025.68113.1799

پیش‌بینی هوشمند جریان رودخانه با ترکیب الگوریتم‌های فراکاوشی شیطان تاسمانی و شاهین دم‌قرمز در حوضه‌ی دهگلان کردستان

نوع مقاله : پژوهشی

نویسندگان

¹ دانشجوی دکتری، گروه مدیریت ساخت و آب، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران

² دانشیار، گروه مدیریت ساخت وآب، واحد علوم وتحقیقات، دانشگاه آزاد اسلامی، تهران، ایران

³ دانشیار، گروه مرتع و آبخیزداری، دانشکده منابع طبیعی، دانشگاه ارومیه، ارومیه، ایران

⁴ 4- استاد، گروه مهندسی آب، قطب علمی هیدروانفورماتیک، دانشکده مهندسی عمران، دانشگاه تبریز و شرکت فرازآب( مهندسین مشاور)، امورارتقاع توانمندی تحقیقات وتا لیفات، تبریز، ایران

10.22034/hyd.2025.68113.1799

چکیده

پیش‌بینی دقیق جریان رودخانه نقش مهمی در مدیریت منابع آب، به ویژه برای کاهش مخاطرات ناشی از سیل، هشدار خشکسالی و بهره‌برداری از مخازن سدها ایفا می‌کند. در این پژوهش از آمار 20 ساله ( از سال 1380 لغایت 1400) بارش، دبی رودخانه و دمای میانگین در مقیاس روزانه در حوضه آبریز دهگلان استان کردستان استفاده گردید. برای انتخاب ترکیب بهینه و سناریوهای مدل از مقادیر بارش (Pt)، دمای میانگین (Tt) و دبی از یک تا سه روز تاخیر (Qt-1 تا Qt-3) با استفاده از ضریب همبستگی پیرسون استفاده شد. جهت انجام مدل سازی و پیش بینی جریان رودخانه نیز از مدل‌های هیبریدی شبکه عصبی مصنوعی-شیطان تاسمانی (ANN-TDO)، ماشین بردار پشتیبان رگرسیونی- شاهین دم قرمز (SVR-RTH) و مدل یادگیری عمیق حافظه طولانی کوتاه‌مدت - شکارچیان دریایی (LSTM-MPA) استفاده گردد. سپس نتایج مدل سازی بر اساس معیارهای ارزیابی (R2-MAE-RMSE-KGE) مورد سنجش قرار گرفت. یافته‌های این پژوهش نشان داد که مدل‌های هیبردی تا حد بسیار خوبی دقت مدل‌های منفرد را بهبود بخشیدند. همچنین نتایج نشان داد که عملکرد مدل‌ها بسیاز نزدیک است اما مدل ANN-TDO نسب به سایر مدل‌ها عملکرد بهتری داشته است. همچنین این مدل در فاز آموزش توانست در سناریوهای یک تا پنج (M1 تا M5) به ترتیب 10.66، 40.25، 39.19، 79.45 و 82.44 درصد عملکرد مدل منفرد ANN را بهبود بخشد.

کلیدواژه‌ها

موضوعات

هیدرولوژی

عنوان مقاله [English]

Intelligent Streamflow Prediction Using a Hybrid Metaheuristic Approach: Tasmanian Devil and Red-Tailed Hawk Optimization Algorithms in the Dehgolan Kurdistan Basin

نویسندگان [English]

Edris Merufinia ¹
Ahmad Sharafati ²
Hirad Abghari ³
Yousef Hassanzadeh ⁴

¹ Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

² ASD. Associate Professor, Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.

³ Associate Professor, Department of Range and Watershed Management, Urmia University, Urmia, Iran

⁴ Professor, Department of Water Engineering, Center of Excellence in Hydroinformatics, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran Farazab Co. (Consulting Engineers), Research and Writing Capacity Enhancement Affairs, Tabriz, Iran.

چکیده [English]

With the increasing complexity and dynamics of hydrological systems, accurate and reliable river stream flow prediction is necessary for sustainable water resource management. This research utilized 20 years (from 2001 to 2021) of daily precipitation, river discharge, and mean air temperature data from the Dehgolan basin in Kurdistan Province. To select the optimal combination and model scenarios, Pearson's correlation coefficient was employed using precipitation (Pt), mean temperature (Tt), and river discharge with one to three days of lag (Qt-1 to Qt-3). The Pearson correlation coefficient (PCC) was used to select optimal scenarios and model combinations, establish the relationship between input and output variables, and subsequently choose the model and scenario combinations. For streamflow prediction, we utilized hybrid models including the Artificial Neural Network-Tasmanian Devil Optimizer (ANN-TDO), Support Vector Regression-Red Tailed Hawk (SVR-RTH), and the deep learning model Long Short-Term Memory-Marine Predators Algorithm (LSTM-MPA). Model evaluation involved the following metrics: Root Mean Square Error (RMSE), Coefficient of Determination (R2), and Kling-Gupta Efficiency (KGE). Among the hybrid models, ANN TDO consistently demonstrated the best performance. Its R2 values for the testing phase were frequently above 0. 8, and KGE values reached up to 0. 915 in some scenarios, indicating a very high correlation with observed data. The LSTM-MPA model also delivered very good performance. Although it performed slightly below ANN-TDO in some scenarios, its R2 and KGE values (often above 0. 7 and 0. 8), along with low MAE and RMSE values, demonstrated this model's high capability in time series modeling.

کلیدواژه‌ها [English]

Dehgolan Basin
Pearson correlation coefficient
Streamflow prediction
Tasmanian devil optimization
LSTM

مراجع

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