استفاده از شبکه عصبی NARX به عنوان مدل جایگزین برای شبیه‌سازی بلند مدت شوری خروجی از مخازن دارای لایه‌بندی کیفی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه سازه‌های آبی، دانشکده کشاورزی، دانشگاه تربیت مدرس، تهران، ایران

2 استاد بخش مهندسی عمران، دانشگاه آزاد اسلامی واحد شهر قدس، تهران، ایران

3 گروه مهندسی عمران، دانشکده فنی دانشگاه شهید چمران، اهواز، ایران

چکیده

برنامه CE-QUAL-W2 یک مدل فیزیکی با اطمینان‌پذیری بالا جهت شبیه‌سازی هیدرودینامیکی-کیفی مخازن بوده که هزینه محاسباتی زیادی دارد. بنابراین یافتن مدل‌های جایگزین که نتایج این مدل را با دقت مطلوب و در زمان اندکی برآورد کنند از اهمیت کاربردی بالایی برخوردار است. در این تحقیق قابلیت مدل شبکه عصبی NARX به عنوان مدل جایگزین CE-QUAL-W2 جهت پیش‌بینی نتایج بلند مدت شوری خروجی از مخزن بررسی شده است. برای این منظور مدل CE-QUAL-W2 مخزن سد گتوند علیا تهیه و پس از واسنجی، برای شبیه‌سازی شوری خروجی از مخزن در یک دوره زمانی 10 ساله استفاده گردید. با توجه به امکان تخلیه از دریچه‌های مختلف مخزن، با تغییر ماهیانه نسبت تخلیه دریچه‌ها مسائل متعددی تعریف و کتابخانه‌ای از نتایج مدل فیزیکی تشکیل شد. سپس با معرفی سناریوهای مختلف معماری شبکه عصبی NARX، آموزش آن‌ها با استفاده از کتابخانه نتایج انجام شد. نتایج حاصل از سناریوهای مختلف بیانگر توانایی بالای شبکه عصبی NARX در برآورد روند شوری خروجی از مخزن بوده و ضریب تعیین همواره بیش از 91/0 است. در سناریوی منتخب ضریب تعیین 95/0، میانگین درصد خطای مطلق و ضریب نش-ساتکلیف به ترتیب 7/8 درصد و 79/0 بوده و انطباق خوبی بین نتایج دو مدل مشاهده می‌شود. مدت زمان شبیه‌سازی بلند مدت مخزن گتوند با استفاده از مدل شبکه عصبی کم‌تر از 0.06 درصد زمان لازم برای اجرای مدل فیزیکی است. نتایج این تحقیق نشان می‌دهد که مدل NARX را می‌توان جهت پیش‌بینی بلند مدت شوری خروجی از مخازن به عنوان مدل جایگزین برای CE-QUAL-W2 بکار برده و هم‌زمان هزینه‌ی محاسبات را به طور چشمگیری کاهش داد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Application of NARX Neural Network as Surrogate Model to Long-term Simulation of the Outlet Salinity from Strong Stratified Reservoirs

نویسندگان [English]

  • Morad Asadi 1
  • Jamal Mohamad Vali Samani 2
  • Hossein Mohamad Vali Samani 3
1 Water Structures Engineering Department, Tarbiat Modares University, Tehran, Iran
2 Department of Civil Engineering, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran
3 Department of Civil Engineering, Faculty of Engineering, Shahid Chamran University, Ahwaz, Iran
چکیده [English]

The CE-QUAL-W2 program as a physical model for quality and hydrodynamic simulation of water reservoirs has a high computational cost. Therefore, finding surrogate models to give optimal results in short term would have a great practical importance especially in simulation-optimization problems. In this study, the capability of the NARX model as a surrogate model was investigated to simulate the outlet salinity from strongly stratified reservoirs. For this purpose, the CE-QUAL-W2 model was used and calibrated to simulate the outlet salinity of the Upper Gotvand Reservoir over 10 years. Regarding the possibility of release from different reservoir intakes, by monthly change of release ratios, several problems were defined and a library of the physical model results was formed. Then different NARX architecture scenarios were introduced and trained using the library results. The results obtained from different scenarios indicate that the NARX neural network model has a high capability to simulate the CE-QUAL-W2 model results of outflow salinity, so that the correlation coefficient is always above 0.91. In the selected scenario, a very good agreement is observed between the results of the two models, with a correlation coefficient of 0.95, mean absolute percentage error of 8.7% and Nash-Sutcliffe coefficient of 0.79. The simulation time required for the NARX neural network model is less than 0.06% of the time required to run the physical model for the same problem. The results show that the NARX model can be used as a suitable surrogate model for CE-QUAL-W2 to predict the long-term reservoir outlet salinity and reduces the cost of computing while maintains accuracy.

کلیدواژه‌ها [English]

  • NARX neural network model
  • CE-QUAL-W2 physical model
  • Surrogate Model
  • Long-term simulation
  • Reservoir outlet salinity

مراجع

Afshar, A., Kazemi, H., & Saadatpour, M. (2011). Particle Swarm Optimization for Automatic Calibration of Large Scale Water Quality Model (CE-QUAL-W2): Application to Karkheh Reservoir, Iran. Water Resources Management, 25(10), 2613–2632. https://doi.org/10.1007/s11269-011-9829-7.
Afshar, A., & Masoumi, F. (2016). Waste load reallocation in river–reservoir systems: simulation–optimization approach. Environmental Earth Sciences, 75(1), 1–14. https://doi.org/10.1007/s12665-015-4812-x.
Aghdam, J. A., Zare, M., Capaccioni, B., Raeisi, E., & Forti, P. (2012). The Karun River waters in the Ambal ridge region ( Zagros mountain Range , southwestern Iran ): mixing calculation and hydrogeological implications, 251–267. https://doi.org/10.1007/s13146-012-0083-8.
Ahmadi, E., Abooie, M. H., Jasemi, M., & Mehrjardi, Y. Z. (2016). A Nonlinear Autoregressive Model with Exogenous Variables Neural Network for Stock Market Timing : The Candlestick Technical Analysis, 29(12), 1717–1725.
Alarcon, V. J. (2019). Predicting Sediment Concentrations Using a Nonlinear Autoregressive Exogenous Neural Network. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11621 LNCS). Springer International Publishing. https://doi.org/10.1007/978-3-030-24302-9_42.
Amani, P., Kihl, M., & Robertsson, A. (2011). NARX-based Multi-step Ahead Response Time Prediction for Database Servers. In [Host Publication Title Missing] (Pp. 813-818). IEEE--Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ISDA.2011.6121757.
Amirkhani, M., Haddad, O. B., Ashofteh, P.-S., & Lo?iciga, H. A. (2016). Determination of the optimal level of water releases from a reservoir to control water quality. Journal of Hazardous, Toxic, and Radioactive Waste, 20(2), 1–7. https://doi.org/10.1061/(ASCE)HZ.2153-5515.0000295.
Andalib, A., & Atry, F. (2009). Multi-step ahead forecasts for electricity prices using NARX : A new approach , a critical analysis of one-step ahead forecasts. Energy Conversion and Management, 50(3), 739–747. https://doi.org/10.1016/j.enconman.2008.09.040.
Asadi, M., Samani, J.M. V., Samani, H.M.V., 2020. Investigation of Solutions for Introducing Concentrated Side-Inflows to Reservoirs in The 2D Hydrodynamic and Qualitative CE-QUAL-W2 Model, Case Study: Upper Gotvand Dam (In Farsi). In: Proceedings of 18th Iranian Hydraulic Association, 5-6 Feb, Tehran University, Tehran, Iran.
Banihabib, M. E., Ahmadian, A., & Jamali, F. S. (2017). Hybrid DARIMA-NARX model for forecasting long-term daily inflow to Dez reservoir using the North Atlantic Oscillation (NAO) and rainfall data. GeoResJ, 13, 9–16. https://doi.org/10.1016/j.grj.2016.12.002.
Boussaada, Z., Curea, O., Remaci, A., Camblong, H., & Bellaaj, N. M. (2018). Daily Direct Solar Radiation. MDPI, (energies). https://doi.org/10.3390/en11030620.
Castelletti, A., Pianosi, F., Soncini-Sessa, R., & Antenucci, J. P. (2010). A multiobjective response surface approach for improved water quality planning in lakes and reservoirs. Water Resources Research, 46(6), 1–16. https://doi.org/10.1029/2009WR008389.
Dhar, A., & Datta, B. (2008). Optimal operation of reservoirs for downstream water quality control using linked simulation optimization, 853(June 2007), 842–853. https://doi.org/10.1002/hyp.
Emamgholizadeh, S., Kashi, H., Marofpoor, I., & Zalaghi, E. (2014). Prediction of water quality parameters of Karoon River ( Iran ) by artificial intelligence-based models, 645–656. https://doi.org/10.1007/s13762-013-0378-x.
Guzman, S. M., Paz, J. O., & Tagert, M. L. M. (2017). The Use of NARX Neural Networks to Forecast Daily Groundwater Levels. Water Resources Management, 31(5), 1591–1603. https://doi.org/10.1007/s11269-017-1598-5.
Haddout, S., & Maslouhi, A. (2017). Two-dimensional modeling of the vertical circulation of salt intrusion in the Sebou estuary under different hydrological conditions. ISH Journal of Hydraulic Engineering, 5010(October), 1–18. https://doi.org/10.1080/09715010.2017.1391134.
Horne, A., Szemis, J. M., Kaur, S., Webb, J. A., Stewardson, M. J., Costa, A., & Boland, N. (2016). Environmental Modelling & Software Optimization tools for environmental water decisions : A review of strengths, weaknesses, and opportunities to improve adoption. Environmental Modelling and Software, 84, 326–338. https://doi.org/10.1016/j.envsoft.2016.06.028.
Jalali, L., Zarei, M., & Guti, F. (2019). Salinization of reservoirs in regions with exposed evaporites . The unique case of Upper Gotvand Dam , Iran, 157. https://doi.org/10.1016/j.watres.2019.04.015.
Jeznach, L. C., Jones, C., Matthews, T., Tobiason, J. E., & Ahlfeld, D. P. (2016). A framework for modeling contaminant impacts on reservoir water quality. JOURNAL OF HYDROLOGY, 537, 322–333. https://doi.org/10.1016/j.jhydrol.2016.03.041.
Karamouz, M., Nazif, S., Sherafat, M. A., & Zahmatkesh, Z. (2014). Development of an Optimal Reservoir Operation Scheme Using Extended Evolutionary Computing Algorithms Based on Conflict Resolution Approach : A Case Study. Water Resources Management, 28, 3539–3554. https://doi.org/10.1007/s11269-014-0686-z.
Kim, Y., & Kim, B. (2017). Lake and Reservoir Management Application of a 2-Dimensional Water Quality Model ( CE-QUAL-W2 ) to the Turbidity Interflow in a Deep Reservoir ( Lake Soyang , Korea ) Application of a 2-Dimensional Water Quality Model ( CE-QUAL-W2) to the Turbidity Inter. Lake and Reservoir Management, 2381(March). https://doi.org/10.1080/07438140609353898.
Ma, J., Liu, D., Wells, S. A., Tang, H., Ji, D., & Yang, Z. (2015). Modeling density currents in a typical tributary of the Three Gorges. Ecological Modelling, 296, 113–125. https://doi.org/10.1016/j.ecolmodel.2014.10.030.
Park, Y., Hwa, K., Kang, J., Won, S., & Ha, J. (2014). Science of the Total Environment Developing a fl ow control strategy to reduce nutrient load in a reclaimed multi-reservoir system using a 2D hydrodynamic and water quality model. Science of the Total Environment, The, 466–467, 871–880. https://doi.org/10.1016/j.scitotenv.2013.07.041.
Parlos, A. G., Rais, O. T., & Atiya, A. F. (2000). Multi-step-ahead prediction using dynamic recurrent neural networks. Neural Networks, (February 2015). https://doi.org/10.1109/IJCNN.1999.831517.
Rani, D., & Madalena, M. (2010). Simulation – Optimization Modeling : A Survey and Potential Application in Reservoir Systems Operation, 1107–1138. https://doi.org/10.1007/s11269-009-9488-0.
Razavi, S., Tolson, B. A., & Burn, D. H. (2012). Review of surrogate modeling in water resources, 48(October 2011). https://doi.org/10.1029/2011WR011527.
Saadatpour, M., Afshar, A., & Edinger, J. E. (2017). Meta-Model Assisted 2D Hydrodynamic and Thermal Simulation Model ( CE-QUAL-W2 ) in Deriving Optimal Reservoir Operational Strategy in Selective Withdrawal Scheme. Water Resources Management, 2729–2744. https://doi.org/10.1007/s11269-017-1658-x.
Schardong, A., & Simonovic, S. P. (2015). Coupled Self-Adaptive Multiobjective Differential Evolution and Network Flow Algorithm Approach for Optimal Reservoir Operation. Journal of Water Resources Planning and Management, 141(10), 04015015. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000525.
Sedlá, J., & Nováková, T. (2017). Science of the Total Environment Sedimentary record and anthropogenic pollution of a complex , multiple source fed dam reservoirs : An example from the Nové Mlýny reservoir , Czech Republic, 574, 1456–1471. https://doi.org/10.1016/j.scitotenv.2016.08.127.
Shaw, A. R., Sawyer, H. S., LeBoeuf, E. J., McDonald, M. P., & Hadjerioua, B. (2017). Hydropower optimization using artificial neural network surrogate models of a high-fidelity hydrodynamics and water quality Model. Water Resources Research, 53, 1–18. https://doi.org/10.1002/2017WR021039.
Soleimani, S., Bozorg-haddad, O., Saadatpour, M., & Loáiciga, H. A. (2018). Simulating thermal stratification and modeling outlet water temperature in reservoirs with a data mining method. Journal of Water Supply: Research and Technology—AQUA, 1–13. https://doi.org/10.2166/aqua.2018.036.
Wang, Q., Li, S., Peng, J., Qi, C., & Ding, F. (2014). A review of hydrological/water-quality models. Frontiers of Agricultural Science and Engineering, 1(4), 267. https://doi.org/10.15302/J-FASE-2014041.
Wei, G. L., Yang, Z. F., Cui, B. S., Li, B., Chen, H., Bai, J. H., & Dong, S. K. (2009). Impact of dam construction on water quality and water self-purification capacity of the Lancang River, China. Water Resources Management, 23(9), 1763–1780. https://doi.org/10.1007/s11269-008-9351-8.
Wu, B., Yi, Z., Wu, X., Tian, Y., Han, F., Liu, J., & Zheng, C. (2015). Optimizing water resources management in large river basins with integrated surface water-groundwater modeling: A surrogate-based approach Bin. Water Resources Research, 51, 9127–9140.
https://doi.org/10.1002/2014WR016259.
Xie, H., Tang, H. A. O., & Liao, Y. (2009). TIME SERIES PREDICTION BASED ON NARX NEURAL NETWORKS : AN ADVANCED APPROACH, (July), 12–15.
Yazdi, J., & Moridi, A. (2017). Interactive Reservoir-Watershed Modeling Framework for Integrated Water Quality Management, 2105–2125. https://doi.org/10.1007/s11269-017-1627-4.
Yousefi, P., Saadatpour, M., & Afshar, A. (2019). S urrogate Based Simulation-Optimization Approach in Optimal Operation of Waterbody Considering Quality and Quantity Aspects. https://doi.org/10.22093/wwj.2019.132788.2689.
Zhang, J., Wang, X., Liu, P., Lei, X., Li, Z., Gong, W., … Wang, H. (2017). Assessing the weighted multi-objective adaptive surrogate model optimization to derive large-scale reservoir operating rules with sensitivity analysis. Journal of Hydrology, 544, 613–627. https://doi.org/10.1016/j.jhydrol.2016.12.008.
تحقیقات آب و خاک ایران
دوره 51، شماره 8
آبان 1399
صفحه 1971-1982

فایل ها

هم رسانی

ارجاع به این مقاله

آمار