برآورد میزان تبخیر روزانه با استفاده از شبکه عصبی مصنوعی در شهرستان‌های شیراز و زرقان
محورهای موضوعی : فصلنامه علمی برنامه ریزی منطقه ای
محمد شعبانی
1
(استادیار گروه مهندسی آب، واحد مرودشت، دانشگاه آزاد اسلامی، مرودشت، ایران)
کلید واژه: شبکه عصبی مصنوعی, تبخیر, تعمیم‌پذیری مدل, شهرستان شیراز, شهرستان زرقان,
چکیده مقاله :
تبخیر یکی از مهمترین اجزای چرخه هیدرولوژی می‌باشد که نقش بسیار مهمی در مدیریت منابع آب و محیط زیست دارد. اطلاع از میزان هدر رفت آب در اثر فرآیند تبخیر در یک منطقه بالاخص در مناطق خشک و نیمه خشک که با کمبود منابع آب مواجه هستند، یکی از مهمترین اصول مدیریتی در برنامه‌ریزی منطقه‌ای است. هدف از انجام این تحقیق ارزیابی دقت روش شبکه عصبی مصنوعی در برآورد تبخیر روزانه در ایستگاه هواشناسی شهرستان شیراز و قابلیت تعمیم آن در ایستگاه هواشناسی شهرستان زرقان واقع در استان فارس می‌باشد. برای این منظور تعداد 1775 داده هواشناسی روزانه شامل دما، رطوبت نسبی، سرعت باد، ساعت آفتابی جمع‌آوری و مقدار تبخیر روزانه با استفاده از چهار مدل شبکه عصبی مصنوعی برآورد گردید. جهت مدل‌سازی در این تحقیق از شبکه عصبی پرسپترون چند لایه و تابع سیگموئیدی استفاده گردید. نتایج بدست آمده از چهار مدل شبکه عصبی مصنوعی بر اساس معیارهای ضریب تعیین (R2)، ضریب ناش-ساتکلیف (NSC) و مجذور میانگین مربعات خطا (RMSe) مورد ارزیابی قرار گرفتند. نتایج نشان داد که در ایستگاه هواشناسی شیراز مدل 4 با ساختار 1-6-5 نرون، دارای RMSe کمتر و R2 و NSC بالاتر در هر دو مرحله آموزش و آزمون نسبت به دیگر مدل‌ها می باشد و به عنوان مدل برتر جهت پیش‌بینی میزان تبخیر روزانه در شهرستان شیراز انتخاب گردید. نتایج حاصل از تعمیم‌پذیری مدل 4 با ساختار 1-6-5 در ایستگاه هواشناسی زرقان نیز نشان از دقت بالای این مدل در پیش‌بینی تبخیر روزانه در این ایستگاه دارد. بنابراین می توان از مدل 4 به عنوان مدل مناسب جهت پیش‌بینی مقادیر تبخیر روزانه در شهرستان زرقان برای دوره‌هایی که اندازه‌گیری تبخیر انجام نشده است، استفاده نمود.
Evaporation is one of the most important components of the hydrological cycle that plays a very important role in the management of water resources and the environment. Knowing the amount of water lost due to the evaporation process in an area, especially in arid and semi-arid areas that face shortages of water resources, is one of the most important management principles in regional planning. The aim of this study was to evaluate the accuracy of artificial neural network method in estimating daily evaporation in Shiraz meteorological station and its generalizability in Zarghan meteorological station located in Fars province. For this purpose, 1775 data on a daily scale from meteorological factors including temperature, relative humidity, wind speed, sunshine were collected and then the amount of daily evaporation was estimated using 4 models of artificial neural network. For modeling in this study, multilayer perceptron neural network and sigmoid function were used. The results obtained from four models of artificial neural network were evaluated based on the criteria of coefficient of determination (R2), Nash-Sutcliffe coefficient (NSC) and Root Mean Square Error (RMSe). The results showed that in Shiraz meteorological station, model 4 with a structure of 5-6-1 neurons has less RMSe and higher R2 and NSC in both training and testing stages than other models, so as a superior model to predict the rate of evaporation Was selected daily at Shiraz meteorological station. The results of the generalizability of Model 4 with 5-6-1 structure in Zarghan meteorological station also show the high accuracy of this model in predicting daily evaporation in this station, so it can be used as a suitable model to predict daily evaporation values in This station was used during periods when evaporation was not measured.
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