واسنجی مدل هیدرولوژیکی WetSpa با استفاده از بهینه‌سازی چندهدفه NSGAII و PSO
محورهای موضوعی : مقالات برگرفته از پایان نامه
حسین قلخانی
1
(دفتر مطالعات پایه منابع آب، شرکت مدیریت منابع آب ایران، وزارت نیرو، ایران)
فرهاد هوشیاری پور
2
(گروه مهندسی عمران، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران)
فرشاد کوهیان افضل
3
(پژوهشکده مطالعات و تحقیقات منابع آب، موسسه تحقیقات آب، وزارت نیرو، ایران)
مهیار شفیعی حسن آبادی
4
(گروه علوم زمین و محیط زیست، دانشگاه واترلو، کانادا)
کلید واژه: الگوریتم ژنتیک, WetSpa, مدل بارش رواناب, واسنجی, جامعه ذرات,
چکیده مقاله :
مدل‌های بارش-رواناب مفهومی از جمله ابزارهای ساده و در عین حال کارآمد در مدل‌سازی‌های هیدرولوژیکی هستند. این مدل‌ها با در نظر گرفتن اطلاعات ورودی از قبیل بارش، تبخیر و تعرق و دمای اندازه گیری شده و اطلاعات توپوگرافی حوضه، رژیم جریان رودخانه‌ها را با استفاده از روابط ریاضی شبیه‌سازی می‌کنند. مدل بارش-رواناب WetSpa از جمله مدل‌های توزیعی است که در کشور بلژیک توسعه داده شده است. این مقاله قابلیت الگوریتم‌های بهینه‌سازی ژنتیک و جامعه ذرات را در واسنجی مدل هیدرولوژیکی WetSpa به منظور شبیه سازی بارش – رواناب حوضه کارون بزرگ ارائه می‌نماید. الگوریتم‌های بهینه سازی فوق به صورت چند هدفه برای واسنجی 11 پارامتر سراسری مدل WetSpa استفاده شده‌اند. توابع هدف در نظر گرفته شده در این مقاله شامل دو شاخص نش-سوتکلیف و نش-سوتکلیف لگاریتمی است تا بوسیله آنها عملکرد مدل در پیش بینی دبی‌های حداکثری و حداقلی بهبود یابد. نتایج نشان داده است که هر دو الگوریتم NSGA-II و PSO به ترتیب با ضریب رگرسیون 69/0 و 71/0 عملکرد مناسبی در کالیبراسیون مدل داشته‌اند. مقدار شاخص RMSE در دوره واسنجی نیز به طور متوسط برابر 8/119 و 3/152 اندازه گیری شده است. پس از واسنجی و صحت سنجی مدل، از آن برای شبیه‌سازی سیلاب در یک دوره یکساله در حوضه مذکور استفاده گردیده و قابلیت مدل ارزیابی شده است. همچنین آنالیز حساسیت روی پارامترهای موثر نشان داد که ضریب رواناب سطحی با 40% تاثیر روی مقدار دبی جریان، حساس‌ترین پارامتر سراسری مدل WetSpa بوده است.
Conceptual rainfall-runoff (RR) models, aiming at predicting stream flow from the knowledge of precipitation over a catchment, evapotranspiration, tempreture, and topography of the basin, have become basic and effective tools for flow regime simulation. Calibration of RR models, e.g. WetSpa which has been developed in Belgium, is a process in which parameter adjustment are made so as to match the dynamic behaviour of the RR model to the observed behaviour of the catchment. This research presents an application of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Particle Swarm Optimization (PSO) for multi-objective calibration of WetSpa in Karoon river basin, Iran to optimize 11 global parameters of the WetSpa model. The objective functions are Nash–Sutcliffe and logarithmic Nash–Sutcliffe efficiencies in order to improve the model's performance. Results showed that the evolutionary NSGA-II and PSO algorithms are capable of locating optimal parameter sets in the search space. The measured correlation coefficient in the calibration process was 0.69 and 0.71 for the NSGA-II and PSO algorithms, respectively. Moreover RMSE values were calculated as 119.8 and 152.3 m3/s for the algorithms. The WetSpa model then was applied for a period of 1-year flood simulation in the basin and the results were analysed. Finally a sensitivity analysis was conducted on the global parameters in which the surface runoff coefficient was the most sensitive parameter with more than 40% influence on the results.
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