پیش بینی جریان ورودی به مخزن سد با استفاده مدل شبکه عصبی مصنوعی بر مبنای داده‌های ماهواره‌محورPERSIANN-CDR و CMC(مطالعه موردی: سد زاینده رود)

محورهای موضوعی : برگرفته از پایان نامه

رامتین معینی ^{1 *} , محمدعلی علیجانیان ² , مینا مرادی زاده ³

1 - گروه عمران، دانشکده عمران حمل و نقل، دانشگاه اصفهان، اصفهان، ایران
2 - گروه عمران، دانشکده عمران حمل و نقل، دانشگاه اصفهان
3 - گروه نقشه برداری، دانشکده عمران حمل و نقل، دانشگاه اصفهان

تاریخ دریافت : 1402/09/26 تاریخ پذیرش : 1402/11/25 تاریخ انتشار : 1402/11/25

کلید واژه: شبکه عصبی مصنوعی, بارندگی, آب معادل برف, داده های ماهواره محور, سد زاینده رود,

چکیده مقاله :

در این تحقیق، عملکرد داده‌‌های ماهواره محور PERSIANN- CDR و CMC در تخمین بارش و تعیین جریان ورودی به مخزن سد بررسی شده است. لذا، با ترکیب مختلف داده‌های ورودی، مدلی هایی معرفی و با استفاده از مدل شبکه عصبی مصنوعی جریان ورودی به مخزن سد پیش بینی شده و با نتایج داده‌های زمینی مقایسه شده است. در این تحقیق، مخزن سد زاینده رود از حوضه آبریز گاوخونی به عنوان مطالعه موردی انتخاب شده است. بررسی نتایج نشان دهنده آنست که بهترین نتایج شاخص R2 و RMSE برای داده‌های تخمین بارندگی (برف) ماهواره‌محور PERSIANN-CDR (CMC) 49/0 (34/0) و 90/60 (56/41) میلیمتر می باشد. به عبارت دیگر، نتایج نشان دهنده عملکرد مناسب داده های ماهواره محور در تخمین بارنگی و برف می باشد. بنابراین از این داده ها در ساخت شبکه عصبی مصنوعی به منظور تعیین جریان ورودی به مخزن سد زاینده رود استفاده شده است. بررسی نتایج مدل شبکه عصبی مصنوعی نشان داد که مقادیر شاخص R2 ، RMSE و NSE برای داده های آموزش (صحت سنجی و آزمایش) به ترتیب برابر با 72/0 (74/0)، 08/56 (178/75) میلیون متر مکعب (MCM) و 85/0 (86/0) می باشد که نشان دهنده عملکرد مناسب این مدل در تعیین و پیش بینی جریان ووردی به مخزن سد زاینده رود می باشد.

چکیده انگلیسی:

However, the scale of satellite-based data and the need for their exponential scaling are the uncertainties of these data. In this research, the performance of PERSIANN-CDR and CMC satellite data for rainfall and snow estimation and determining the inflow values into the dam reservoir is investigated. Therefore, by considering different combinations of input data, different models are proposed and the input flow to the dam reservoir is predicted using the artificial neural network (ANN) model. Here, the the ZayandehRoud dam reservoir of the Gavkhoni drainage basin is selected as a case study. The results shows that the best R2 and RMSE values for rainfall (snow) estimation data based on the PERSIANN-CDR satellite (CMC) are 0.49 (0.34) and 60.90 (41.56) mm. In other words, the results show the proper performance of satellite-based data for rainfall and snow estimation. Therefore, these data are used for creating the ANN model to determine the inflow values into the reservoir of ZayandehRoud dam reservoir. The results show that the values of R2, RMSE and NES for training data (validation and testing) of ANN model are equal to 0.72 (0.74), 56.08 (75.178) MCM, and 0.85 (0.86) respectively. In other words, the results show the proper performance of satellite-based data for estimating and determining the inflow into the ZayandehRoud dam reservoir using ANN model.

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