کاربرد مدل ترکیبی موجک-شبکه عصبی در پیش‌بینی تغییرات کوتاه مدت تراز سطح دریا (مطالعه موردی: بندر چابهار)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار دانشکده مهندسی عمران، دانشگاه قم

2 دانشجوی دکتری عمران- سازه‌های هیدرولیکی، دانشگاه قم

چکیده

پیش‌بینی دقیق‌تر از سطح دریا در مناطق ساحلی در کاربردهای مهندسی سواحل بسیار با اهمیت ‌می‌باشد. با پیش‌بینی تراز سطح دریا مشاهده جریانات دریا و تغییرات آن‌ها در سطح، ارتفاع موج، سرعت باد و جزر و مد ممکن شده و این نقش بسزائی در برنامه‌ریزی و مدیریت سواحل دارد. این مطالعه، توانایی روش و مدل ترکیبی موجک-شبکه عصبی در پیش‌بینی کوتاه مدت تراز سطح دریا در بندر چابهار را مورد مطالعه و بررسی قرار ‌می‌دهد. مقایسه این روش با دو روش مدل شبکه عصبی و رگرسیون خطی با استفاده از پارامترهای آماری ضرایب خطا (E، RMSE) به عنوان معیار، مورد بررسی قرار می‌گیرد. اطلاعات گذشته در مورد تراز سطح دریا که بصورت ساعتی برداشت شده به عنوان ورودی مدل بوده و مدل برای پیش‌بینی 12 ساعت آینده (نیم روز) مورد استفاده قرار گرفته است. مقایسه مدل ترکیبی موجک-شبکه عصبی با دیگر مدل‌ها با استفاده از معیار خطاها، نتایج بهتر این مدل را در پیش‌بینی تراز سطح دریا در دوره کوتاه مدت 12 ساعته در این ایستگاه نشان ‌می‌دهد. ضریب E در سه حالت مدل ترکیبی موجک-شبکه عصبی، شبکه عصبی و رگرسیون خطی بترتیب 989/0، 878/0 و 848/0 ‌می‌باشد. این مدل با استفاده از تبدیل موجک و تجزیه سری زمانی تراز سطح دریا به زیرسری‌هایی با اطلاعات مفید و با تغییرات فرکانسی مختلف، فرآیند پیش‌بینی را بهبود ‌می‌بخشد.

کلیدواژه‌ها

عنوان مقاله [English]

Application of ANN and Wavelet Conjunction Model in Forcasting Short-Term Sea Level Variations (Case Study: Chabahar Port)

نویسندگان [English]

  • taher rajaei 1
  • akbar shahabi 2
چکیده [English]

Exact determination of sea level in coastal area is so important in coastal engineering applications. Due to sea level forecasting, observation of sea flow and its surface variation, wave height, wind velocity, and the tide phenomena have been possible. These observations have a significant role in coastal management and planning. ANN and Wavelet Conjunction Model (WCM) ability in short-term forecasting sea level in the Chabahar port is evaluated in the present paper. Error coefficient statistical parameters (RMSE, E) are used as an index to compare aforementioned models with neutral network and linear regression methods. Available data of sea level which are taken hourly are implemented as model inputs and the next twelve hours are predicted using mentioned model. Comparison of ANN and WCM results with other models indicates better performance of mentioned model, in forecasting short-term sea level in this station. Error coefficient value, E, is 0.989, 0.878, and 0.848 in ANN and WCM, neural network, and linear regression, respectively. Using of wavelet transform and decomposition of sea level time-series into subseries with useful information and different frequences, process of forecasting is improved.  

کلیدواژه‌ها [English]

  • Forecasting
  • ANN and wavelet conjunction model
  • sea level variations

مراجع

[1]     Chen, J. L., Shum, C. K., Wilson, C. R., Chambers, D. P., Tapley, B. D., “Seasonal Sea Level Change from Topex/Poseidon Observation and Thermal Contribution”, Journal of Geodesy. Vol. 73, pp. 638–647, 2000.
[2]     Douglas, B. C., Kearney, M. S., Leatherman, S. P. “Sea Level Rise History and Consequences”, International Geophysics Series. Vol. 75, Academic Press, London pp. 272, 2000.
[3]     Meyer F. W., “Hydrogeology, Ground-water Movement, and Subsurface Storage in the Florida Aquifer System in Southern Florida”, United States Geological Survey Professional Paper 1403-G. US Government PrintingOffice, Washington, 1989.
[4]     Thain, R. H., Priestley, A. D. and Davidson, M. A., “The Formation of a Tidal Intrusion Front at the Mouth of a Macro Tidal, Partially Mixed Estuary: a Field Study of the Dart Estuary UK.”, Estuarine, Coastal and Shelf Science, Vol. 61, pp. 161-172, 2004.
[5]     Vries, W. S., Huyskens, E. J., “Gravity Outlet Structures. In: Ritzema, H. P. (Ed), Drainage Principles and Applications”, International Institute for Land Reclamation and Improvement, Wageningen, The Netherlands, 1994.
[6]     Liang, S. X., Li, M. C. and Sun, Z. C. “Prediction Models for Tidal Level Including Strong Meteorologic Effects using a Neural Network", Ocean Engineering, Vol. 35, pp. 666–675, 2008.
[7]     Lee, T. L., Makarynskyy, O., Shao, C. C., “A Combined Harmonic Analysis-Artificial Neural Network Methodology for Tidal Predictions”, Journal of Coastal Research, Vol. 23, pp. 764-770, 2007.
[8]     Makarynskyy, O., Makarynska, D., Kuhn, M. and Featherstone, W. E., “Predicting Sea Level Variations with Artificial Neural Networks at Hillary Harbour, Western Australia. Estuarine”, Coastal and Shelf Sciences, Vol. 61, pp. 351–360, 2004.
[9]     More, A. and Deo, M.C., “Forecasting Wind with Neural Networks”, Marine Structures, Vol. 16, pp. 35–49, 2003.
[10]  Deo, M. C., Jagdale, S. S. “Prediction of Breaking Waves with Neural Networks”, Ocean Engineering, Vol. 30 , pp. 1163-1178, 2003.
[11]  Agrawal, J. D., Deo, M. C. “On-line Wave Prediction”, Journal of Marine Structures. Vol. 15, pp. 57–74, 2002.
[12]  Deo, M. C., Naidu, S. “Real-time Forecasting using Neural Networks”, Ocean Engineering. Vol. 26, pp. 191–203, 1999.
[13]  Makarynskyy, O., “Artificial Neural Networks for Wave Tracking, Retrieval and Prediction”, Pacific Oceanography. Vol. 3, pp. 21–30, 2005.
[14]  Cimen, M., Kisi, O. “Comparison of Two Different Data-Driven Techniques in Modeling Lake Level Fluctuations in Turkey”, Journal of Hydrology, Vol. 378, pp. 253–262, 2009.
[15]  Röske, F., “Sea Level Lorecasts using Neural Networks”, Deutsche Hydrografische Zeitschrift, Vol. 49, No. 1, pp. 71–99, 1997.
[16]  Chang, H. K. and Lin, L. C. H., “Multi-Point Tidal Prediction using Artificial Neural Network with Tide-Generating Forces”, Journal of  Coastal Engineering, Vol. 53, pp. 857–864, 2006.
[17]  Makarynskyy, O., Pires-Silva, A. A., Makarynska, D. and Ventura-Soares, C., “Artificial neural networks in wave predictions at the west coast of Portugal", Computers and Geosciences. Vol. 31, pp. 415–424, 2005.
[18]  Makarynskyy, O. “Artificial Neural Networks in Wave Predictions at the West Coast of Portugal", Indian Journal of Marine Sciences, Vol. 39, pp. 7–17, 2007.
[19]  Günaydın, K., “The Estimation of Monthly Mean Significant Wave Heights by using Artificial Neural Network and Regression Methods”, Ocean Engineering, Vol. 35, pp. 1406–1415, 2008.
[20]  Ultsch, A. and Röske, F., “Self-organizing Feature Maps Predicting Sea Levels”, Inference Science, Vol. 144, pp. 91–125, 2002.
[21]  Karimi, S., Kisi, O., Shiri, J. and Makarynskyy, O., “Neuro-Fuzzy and Neural Network Techniques for Forecasting Sea Level in Darwin Harbor, Australia”, Computers & Geosciences. Vol. 52, pp. 50–59, 2013.
[22]  Shiri, J., Makarynskyy, O., Kisi, O., Dierickx and W., Fakheri Fard, A., “Prediction of Short-Term Operational Sea Water Level using an Adaptive Neuro-Fuzzy Inference System”, ASCE Journal of Waterway, Port, Coastal and Ocean Engineering. Vol. 137, pp. 344-355, 2011.
[23]  Yasseri, S. F., Bahai, H., Bazargan, H., Aminzadeh, A., “Prediction of Safe Sea-State using Finite Element Method and Artificial Neural Networks”, Ocean Eng. Vol. 37, pp. 200–207, 2010.
[24]  Ghorbani, M. A., Khatibi, R., Aytek, A., Makarynskyy, O., Shiri, J., “Sea Water Level Forecast using Genetic Programming and Comparing the Performance with Artificial Neural Networks”, Comput. Geosci. Vol. 36, pp. 620–627, 2010.
[25]  Nourani, V., Komasi, M. and Mano, A., “A Multivariate ANN-wavelet Approach for Rainfall-Runoff Modeling”, Water Resour. Manage. Vol. 23, pp. 2877–2894, 2009.
[26]  Rajaee, T., Nourani, V., Zounemat-Kermani, M. and Kisi, O., “River Suspended Sediment Load Prediction: Application of ANN and Wavelet Conjunction Model”, ASCE Journal of Hydrologic Engineering, Vol. 16, pp. 613-627, Aug. 2011.
[27]  Rajaee, T., “Wavelet and Neuro-Fuzzy Conjunction Approach for Suspended Sediment Prediction”, Clean: Soil, Air, Water. Vol. 38, pp. 275–286, 2010.
[28]  Rajaee, T., Mirbagheri, S. A,, Nourani, V. and Alikhani A., “Prediction of Daily Suspended Sediment Load using Wavelet and Neurofuzzy Combined Model”, International Journal of Environmental Science and Technology, Vol. 7, pp. 93-110, 2011.
[29]  Chen, B. F., Wang, H. D., Chu, Ch. C. H., “Wavelet and Artificial Neural Network Analyses of Tide Forecast and Supplement of Tides Around Taiwan and South China Sea”, Ocean Engineering, Vol. 34, pp. 2161–2175, 2007.
[30]  Addison, P. S., Murrary, K. B., and Watson, J. N.  “Wavelet Transform Analysis of Open Channel Wake Flows”, Journal of Engineering Mechanics, Vol. 127, pp. 58–70, 2001.
[31]  ASCE Task Committee on Application of ANNs in Hydrology, “Artificial Neural Networks in Hydrology, II: Hydrologic Application”, Journal of Hydrologic Engineering, Vol. 5, pp. 124–137, 2000.
Nash, J. E. and Sutcliffe, J. V., “River Flow Forecasting Through Conceptual Models, Part I: A Discussion of Principles”, J. Hydrol. Vol. 10, pp. 282–290, 1970

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  • تاریخ دریافت: 16 مهر 1392
  • تاریخ بازنگری: 20 مرداد 1392
  • تاریخ پذیرش: 22 آذر 1393

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