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پیش‌بینی میزان دبی متوسط ماهیانۀ رودخانۀ کارون با استفاده از روش ترکیبی GRU-LSTM | ||
| اکوهیدرولوژی | ||
| مقاله 6، دوره 7، شماره 3، مهر 1399، صفحه 619-633 اصل مقاله (1.48 M) | ||
| نوع مقاله: پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ije.2020.301608.1322 | ||
| نویسندگان | ||
| پویا احمدی1؛ حسین عارفی* 2؛ ناریلا کاردان3 | ||
| 1دانشجوی کارشناسی ارشد گروه مهندسی نقشه برداری و اطلاعات مکانی، پردیس دانشکده های فنی، دانشگاه تهران، تهران | ||
| 2دانشیار، گروه مهندسی نقشه برداری و اطلاعات مکانی، پردیس دانشکده های فنی، دانشگاه تهران، تهران | ||
| 3استادیار، گروه مهندسی عمران، دانشکدۀ فنی و مهندسی، دانشگاه شهید مدنی آذربایجان، تبریز، ایران | ||
| چکیده | ||
| مدل سازی دبی رودخانه در مدیریت منابع آب و مدیریت ریسک از اهمیت بالایی برخوردار است. این امر در مناطق کوهستانی اهمیت بیشتری پیدا می‌کند زیرا بیشتر جمعیت‌های پایین‌دست منطقه، وابستگی زیادی به کشاورزی و فعالیت‌های تجاری مانند تولید برق دارند. در این زمینه، در سال‌های اخیر، مدل‌های یادگیری ماشینی به دلیل دقت بالا در پیش‌بینی از طریق یادگیری به-صورت جعبه سیاه مورد توجه زیادی قرار گرفته‌اند. از این رو در مطالعه حاضر، یک رویکرد ترکیبی برای پیش‌بینی دبی متوسط ماهیانه رودخانه کارون پیشنهاد شده است. این روش از ترکیب شبکه‌های عصبیLSTM و GRU استفاده می‌نماید. شبکه LSTM یک شبکه عصبی یادگیری عمیق می‌باشد که توانایی اضافه کردن مفهموم زمان به مدل‌سازی را دارد؛ از این رو در پژوهش حاضر به دلیل ماهیت سری زمانی داده‌ها این روش مورد توجه قرار گرفته است. این شبکه به دلیل داشتن دروازه‌های زیاد، بسیار کند عمل می کند که برای جبران سرعت این روش از لایه‌های GRU که نمونه‌ای دیگر از شبکه‌های یادگیری عمیق می‌باشند استفاده می-شود. برای پیش‌بینی دبی متوسط ماهیانه رودخانه کارون از داده‌های آماری ایستگاه ملاثانی برای دوره 21 ساله از 1 فروردین 1374 تا 29 اسفند 1394 استفاده شده و مدل‌سازی براساس پنج ترکیب ورودی با مقادیر دبی رودخانه با تأخیر یک ماهه انجام شده است. رویکرد پیشنهادی با سایر روش‌های موجود نظیر ماشین بردار پشتیبان، سیستم استنتاج فازی-عصبی تطبیقی و مدل رگرسیون خطی چندگانه مورد مقایسه قرار گرفت که نتایج نشان دهنده‌ی بالا بودن دقت رویکرد پیشنهادی نسبت به سایر روش‌های مورد مقایسه می‌باشد. | ||
| کلیدواژه‌ها | ||
| پیش بینی؛ دبی ماهیانه؛ روش ماشین بردار پشتیبان؛ روش GRU-LSTM؛ رودخانۀ کارون | ||
| عنوان مقاله [English] | ||
| Modeling the discharge of Karun River Using a New Method Based on the Joint LSTM and GRU Neural Networks | ||
| نویسندگان [English] | ||
| Pouya Ahmadi1؛ Hossein Arefi2؛ Nazila Kardan3 | ||
| 1M.Sc. Student, School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran North Kargar Ave., Jalal Al. Ahmad Crossing | ||
| 2Associate Professor, School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran North Kargar Ave., Jalal Al. Ahmad Crossing | ||
| 3Assistant Professor, Department of Civil Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran | ||
| چکیده [English] | ||
| Modeling the river discharge is of great importance in water resources and risk management. This is especially important in mountainous areas since most of the low-income people in such areas are heavily dependent on agriculture and commercial activities such as electricity. In this regard, in recent years, machine learning models have received more attention due to their high accuracy in predicting through black box learning. Therefore, in this study, a combined approach has been proposed to predict the average monthly discharge of the Karun River. This method uses a combination of LSTM and GRU neural networks. The LSTM network is a deep learning neural network that has the ability to add the concept of time to modeling; therefore, this method has been considered in this study due to the nature of time series of the data. However, the utilized network in this method is considerably slow due to its large number of gates. Accordingly, to compensate the speed issue, the GRU layer method, as another example of deep learning networks, is used. To predict the average monthly flow of the Karun River in Dubai, the statistical data of Molasani station from April 1st, 1995 to March 20th, 2016, with five combination of river discharge inputs on monthly basis, has been used. The proposed approach is compared with other available methods such as support vector machine, adaptive fuzzy-neural inference system, and multiple linear regression model. The results show the high accuracy of the proposed approach compared to other methods. | ||
| کلیدواژه‌ها [English] | ||
| Monthly discharge, Forecast, SVM, GRU-LSTM, Karoun River | ||
| مراجع | ||
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[1]. Oliveira N, Cortez P, Areal N. The impact of microblogging data for stock market prediction: Using Twitter to predict returns, volatility, trading volume and survey sentiment indices. Expert Systems with Applications, 2017; 73: 125-144. [2]. Borujeni SC. Modeling flood occurrences using soft computing technique in southern strip of Caspian Sea watershed, 2012. [3]. Kisi Ö, Çobaner M. Modeling river stage‐discharge relationships using different neural network computing techniques. CLEAN–Soil, Air, Water, 2009; 37(2): 160-169. [4]. Liong SH, Chandrasekaran S. Flood stage forecasting with support vector machines. JAWRA Journal of the American Water Resources Association, 2007; 38(1):173 - 186 [5]. Yu PS, Chen ST, Chang IF. Support vector regression for real-time flood stage forecasting. Journal of Hydrology, 2006; 328(3-4): 704-716. [6]. Wang WC, Chau KW, Cheng CT, Qiu L. A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. Journal of Hydrology, 2009; 374: 294-306. [7]. Ghorbani MA, Kisi O, Aalinezhad M. 2010. A probe into the chaotic nature of daily streamflow time series by correlation dimension and largest Lyapunov methods. Applied [8]. Zahiri A, Azamathulla HM. Comparison between linear genetic programming and M5 tree models to predict flow discharge in compound channels. Neural Computing and Applications, 2014; 24(2): 413–420 [9]. He Z, Wen X, Liu H, Du J. A comparative study of artificial neural network, adaptive neuro fuzzy inference system and support vector machine for forecasting river flow in the semiarid mountain region. Journal of Hydrology, 2014; 509: 379–386. [10]. Hasanpour Kashani M, Ghorbani MA, Dinpazhouh Y, Shahmorad S. Rainfall-Runoff simulation in the Navrood river basin using truncated volterra model and artificial neural networks. Journal of Watershed Management Research, 201; 6(12): 1-10 (In Persian). [11]. Darbandi S, Pourhosseini FA. River flow simulation using a multilayer perceptron-firefly algorithm model. Applied Water Science, 2018; 8(3):1–9. [12]. Ghose DK. Measuring Discharge Using Back-Propagation Neural Network: A Case Study on Brahmani River Basin. In: Bhateja V., Coello Coello C., Satapathy S., Pattnaik P. (eds) Intelligent Engineering Informatics. Advances in Intelligent Systems and Computing, vol 695. Springer, Singapore, 2018. [13]. Petty T, Dhingra P. Streamflow hydrology estimate using machine learning (SHEM). JAWRA Journal of the American Water Resources Association, 2018; 54(1): 55-68. [14]. Muhammad AU, Li X, Feng J. Using LSTM GRU and Hybrid Models for Streamflow Forecasting. In: Zhai X., Chen B., Zhu K. (eds) Machine Learning and Intelligent Communications. MLICOM 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 294. Springer, Cham. [15]. Dou M, Qin C, Li G, Wang C. Research on Calculation Method of Free flow Discharge Based on Artificial Neural Network and Regression Analysis. Flow Measurement and Instrumentation, 2020; 72: 102-123. [16]. Hussain D, Khan AA. Machine learning techniques for monthly river flow forecasting of Hunza River, Pakistan. Earth Science Informatics, 2020; DOI: 10.1007/s12145-020-00450-z. [17]. Soltani A, Gorbani M, Fakheri Fard A, Darbandi S, Farsadizadeh D. Genetic Programming and Its Application in Rainfall-Runoff Modeling. Water and Soil Science, 2011; 20(4), 62-71 (In Persian). [18]. Khosravi M, Salajegheh A, Mahdavi M, Mohseni Saravi M. Determination of the Best Output Layer Activation Function in Neural Network for Forecasting Peak Discharge. Iranian Journal of Watershed Management Science and Engineering, 2010; 4(12): 61-64 (In Persian). [19]. Noori R, Karbassi A, Farokhnia A, Dehghani M. Predicting the longitudinal dispersion coefficient using support vector machine and adaptive neuro-fuzzy inference system techniques. Environmental Engineering Science, 2009; 26(10): 1503-1510. [20]. Pourhaghi A, Solgi A, Radmanesh F, Shehni darabi M. Hybrid Usage of The Wavelet transform and Intelligent to Simulation River Flow (Case Study: KaKa Reza and Sarab seyed Ali rivers). Irrigation and Water Engineering, 2018; 8(4): 1-17 (In Persian). [21]. Haghizadeh A, Mohammadlou M, Noori F. Simulation of Rainfall-Runoff Process using multilayer perceptron and Adaptive Neuro-Fuzzy Interface System and multiple regression (Case Study: Khorramabd Watershed). Iranian journal of Eco hydrology, 2015; 2(2), 233-243 (In Persian). [22]. Sepehri M, Ildoromi AR, Hosseini SZ, Nouri H, Mohammadzade F, Artimani MM. The combination of neural networks and genetic algorithms is a way to estimate the Peak flood. Iranian Journal of Watershed Management Science and Engineering, 2018; 11(39): 23-32 (In Persian). [23]. Jain YK, Bhandare SK. Min max normalization based data perturbation method for privacy protection. International Journal of Computer and Communication Technology, 2011; 2(8): 45-50. [24]. Kisi Ö, Çobaner M. Modeling river stage‐discharge relationships using different neural network computing techniques. CLEAN–Soil, Air, Water, 2009; 37(2): 160-169. [25]. Dixon B. Applicability of neuro-fuzzy techniques in predicting ground-water vulnerability: a GIS-based sensitivity analysis. Journal of hydrology, 2005; 309(1-4): 17-38. [26]. Nadiri AA, Gharekhani M, Khatibi R. Mapping aquifer vulnerability indices using Artificial Intelligence-running Multiple Frameworks (AIMF) with supervised and unsupervised learning. Water Resources Management, 2018; 32: 3023-3040. [27]. Nadiri AA, Fijani E, Tsai F, Asghari Moghaddam A. Supervised committee machine with artificial intelligence for prediction of fluoride concentration. Journal of Hydroinformatics, 2013; 15(4): 1474–1490. [28]. Granata R, Saroli M, Marinis GD, Gargano R. Machine learning models for spring discharge forecasting. Geofluids, 2018; 2018: 1-13. [29]. Wang W, Men C, Lu W. Online prediction model based on support vector machine. Neurocomputing, 2008; 71(4-6): 550-558. [30]. Cao LJ, Tay FEH. Support vector machine with adaptive parameters in financial time series forecasting. IEEE Transactions on neural networks, 2003; 14(6): 1506-1518. [31]. Marsooli R, Aalami MT. Evaluation of total load sediment transport formulas using ANN. International Journal of Sediment Research, 2009; 24(3): 274-286. [32]. Yu PS, Chen ST, Chang IF. Flood stage forecasting using support vector machines. Geophysical Research Abstracts, 2005; 7: 41-76. [33]. Khan MS, Coulibaly P. Application of support vector machine in Lake water level prediction. Journal of Hydrologic Engineering, 2006; 11(3): 199-205. | ||
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