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
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