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توسعه مدل هیبریدی غیرخطی برای پیش‌بینی جریان ماهانه ورودی مخازن سدها براساس پارامترهای هیدروکلیما و پوشش گیاهی حوضه (مطالعه موردی: سد دز) | ||
| مجله اکوهیدرولوژی | ||
| مقاله 3، دوره 11، شماره 1، فروردین 1403، صفحه 26-45 اصل مقاله (1.12 M) | ||
| نوع مقاله: پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ije.2024.373165.1797 | ||
| نویسندگان | ||
| پوریا ظهیرپور1؛ سامان جوادی* 2؛ فریماه سادات جمالی3؛ علی محمدی2 | ||
| 1گروه مهندسی آب، دانشکده فناوری کشاورزی (ابوریحان)، دانشکدگان کشاورزی و منابع طبیعی، دانشگاه تهران، تهران، ایران | ||
| 2گروه مهندسی آب، دانشکده فناوری کشاورزی (ابوریحان)، دانشکدگان کشاورزی و منابع طبیعی، دانشگاه تهران، تهران، ایران. | ||
| 3گروه جغرافیای طبیعی، دانشکده علوم زمین، دانشگاه شهید بهشتی ، تهران، ایران | ||
| چکیده | ||
| سد دز نقش اساسی در کنترل سیلاب‌های رودخانه دز ایفا کرده و از طرفی تأمین کننده آب مورد نیاز کشاورزی دشت‌های خوزستان می‌باشد. بنابراین پیش‌بینی دقیق و قابل اعتماد ورودی جریان به مخزن، یک مرجع حیاتی برای تصمیم‌گیری در مورد بهره‌برداری و مدیریت این مخزن است. در این تحقیق مدل WAVELET-ARIMA-NARX برای پیش‌بینی جریان ماهانه ورودی مخزن سد دز توسعه یافت. از روش WAVELET جهت تجزیه سری زمانی به زیر سری ها و تحلیل بهتر سری زمانی، از مدل ARIMA به منظور مدل سازی مولفه خطی سری های تجزیه شده و از مدل NARX جهت مدل سازی خطای حاصل از مدل WAVELET-ARIMA استفاده گردید. از شاخص NDVI حوضه نیز جهت سنجش تغییر دقت مدل هیبریدی WAVELET-ARIMA-NARX بهره برده شد. نتایج نشان داد عملکرد پیش بینی مدل هیبریدی WAVELET-ARIMA-NARX نسبت به مدل ARIMA بهبود قابل توجهی یافته است. به نحوی که با توجه به معیار RMSE، دقت پیش‌بینی در مراحل صحت‌سنجی و آموزش، نسبت به مدل ARIMA و WAVELET-ARIMA به ترتیب 74 و 82 درصد کاهش یافته است. همچنین پارامتر NDVI به همراه دما و بارش متوسط حوضه به عنوان ورودی مدل NARX دقت مدل را افزایش داده بدین صورت که در این مدل با 10 نرون در لایه پنهان، در مقایسه با مدل دو پارامتری بارش و NDVI با تعداد 15 نورون، در بخش ارزیابی مدل مقادیر MAE از 2/27 به 6/18 و RMSE از 45/0 به 26/0 رسیده است که این مقادیر نشانگر اهمیت و تاثیر در نظرگرفتن همزمان سه پارامتر بر دقت پیش بینی می باشد. | ||
| کلیدواژه‌ها | ||
| تئوری موجک؛ شاخص فصلی؛ شبکه عصبی مصنوعی؛ مدل هیبریدی؛ سد دز | ||
| عنوان مقاله [English] | ||
| Development of a nonlinear hybrid model for forecasting monthly reservoirs inflow Based on hydro-climate parameters and basin vegetation cover (Case study: Dez dam) | ||
| نویسندگان [English] | ||
| Pouria Zahirpour1؛ Saman Javadi2؛ Farimah Sadat Jamali3؛ Ali Mohammadi2 | ||
| 1Department of Water Engineering, Faculty of Agriculture Technology (Aburaihan), College of Agriculture and Natural Resources, University of Tehran, Tehran, Iran | ||
| 2Department of Water Engineering, Faculty of Agriculture Technology (Aburaihan), College of Agriculture and Natural Resources, University of Tehran, Tehran, Iran | ||
| 3Department of Physical Geography, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran. | ||
| چکیده [English] | ||
| Dez dam has an essential role in controlling the floods and water supply in Khouzestan. Accurate and reliable prediction of the flow input to the reservoir is crucial to make decisions about the exploitation and management of this reservoir. In this research, a novel WAVELET-ARIMA-NARX model was developed to predict the monthly inflow to Dez dam reservoir. The WAVELET method was used to break down the time series into sub-series and better analyzing, the ARIMA model was used to model the linear component of the analyzed series, and the NARX model used to model the error resulting from WAVELET-ARIMA model. The NDVI index was also used to measure the accuracy change of the WAVELET-ARIMA-NARX hybrid model. The results showed that the prediction performance of the WAVELET-ARIMA-NARX hybrid model has improved significantly compared to the ARIMA model. In such a way according to the RMSE criterion, the prediction accuracy in the verification and training stages has decreased by 74% and 82%, compared to the ARIMA and WAVELET-ARIMA model, respectively. The NDVI parameter with average temperature and rainfall of the basin as an input of the NARX model has increased the accuracy of the model. In the model with 10 neurons in the hidden layer, compared to the two-parameter model of rainfall and NDVI with 15 neurons, in evaluation section, the MAE values have decreased from 27.2 to 18.5 and the RMSE from 0.45 to 0.26. These values indicate the importance and impact of simultaneous consideration of three parameters on forecasting accuracy. | ||
| کلیدواژه‌ها [English] | ||
| WAVELET theory, Seasonal Index, Artificial Neural Network, Hybrid Model, Dez dam | ||
| مراجع | ||
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