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تعیین ارتفاع موج شاخص در بندر انزلی با بکارگیری روشهای مبتنی بر هوشمصنوعی (GEP, SVM) و مقایسه نتایج با مدل سوان | ||
| مدل سازی در مهندسی | ||
| دوره 23، شماره 83، دی 1404، صفحه 133-144 اصل مقاله (582.85 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22075/jme.2025.36155.2772 | ||
| نویسندگان | ||
| محمد علی لطف اللهی یقین* ؛ علیرضا مجتهدی؛ عطا آقائی؛ میلاد محمودی | ||
| دانشکده مهندسی عمران، دانشگاه تبریز، تبریز، ایران | ||
| تاریخ دریافت: 20 آذر 1403، تاریخ بازنگری: 29 بهمن 1403، تاریخ پذیرش: 11 اسفند 1403 | ||
| چکیده | ||
| ارتفاع موج شاخص در طراحی و تحلیل سازههای دریایی و بهره برداری از آنها پارامتر بسیار با اهمیتی میباشد در نتیجه پیشبینی این پارامتر کمک شایانی به بهبود طراحی و آنالیز سازههای دریایی مینماید. مدلها و روشهای متعددی برای پیشبینی ارتفاع موج شاخص از جمله روشهای تجربی، نیمه تجربی و عددی ابداع شده است. در پژوهش حاضر، مدل سوان برای آبهای کم عمق و نیمه عمیق ساحلی توسعه داده شده است. دادههای سرعت باد بهعنوان ورودی مدلها در نظر گرفته شده است. برای محاسبه و پیشبینی ارتفاع موج شاخص، سه مدل سوان، مدل ماشین بردار پشتیبان و مدل برنامهنویسی بیان ژن در منطقه انزلی بهکار گرفته شده است. بدین منظور از اطلاعات میدانی باد و موج ثبت شده بویه انزلی در بازه زمانی مشخصی استفاده گردیده است. پس از اجرای مدلها و رسم نمودارهای مربوطه، میزان خطای هر مدل محاسبه شده است. با توجه به معیارهای آماری ارزیابی نتیجه گرفته شد که روند کلی ارتفاع موج شاخص حاصل از هر سه مدل با مقادیر واقعی ثبت شده بویه تطابق قابلقبولی دارند. همچنین دو مدل ماشین بردار پشتیبان و مدل برنامهنویسی بیان ژن توانستند با دقت بالایی ارتفاع موج شاخص را پیشبینی نمایند. مقایسه نتایج حاصل از مدلها نشان داد که مدل ماشین بردار پشتیبان با توجه به شاخصهای آماری (Bias = 0.13)، (RSME = 0.198)، (CC = 0.981) و (CV = 0.147) نسبت به مدل برنامهنویسی بیان ژن، با دقت بیشتری پارامتر هدف را تخمین زده است. هر دو مدل در شبیهسازی مقادیر اوج دقت کمتری داشتهاند. | ||
| کلیدواژهها | ||
| ارتفاع موج شاخص؛ بندر انزلی؛ مدل برنامه نویسی بیان ژن؛ مدل ماشین بردار پشتیبان؛ مدل عددی سوان | ||
| عنوان مقاله [English] | ||
| Determining Significant Wave Height in Anzali Port Using Artificial Intelligence-Based Methods (GEP, SVM) and Comparing the Results with SWAN Model | ||
| نویسندگان [English] | ||
| Mohammad Ali Lotfollahi Yaghin؛ Alireza Mojtahedi؛ Ata Aghayi؛ Milad Mahmoudi | ||
| Faculty of Civil Engineering, Tabriz University, Tabriz, Iran | ||
| چکیده [English] | ||
| The significant wave height is a highly important parameter in the design, analysis, and operation of marine structures. Therefore, predicting this parameter significantly contributes to improving the design and analysis of marine structures. Numerous models and methods, including empirical, semi-empirical, and numerical approaches, have been developed to predict significant wave height. In the present study, the SWAN model has been developed for shallow and semi-deep coastal waters. Wind speed data were used as input for the models. To calculate and predict significant wave height, three models including SWAN, Support Vector Machine (SVM), and Gene Expression Programming (GEP) were applied in the Anzali region. For this purpose, field data on wind and waves recorded by the Anzali buoy over a specific time period were utilized. After running the models and plotting the relevant graphs, the error rates of each model were calculated. Based on statistical evaluation criteria, it was concluded that the overall trend of significant wave height predicted by all three models shows acceptable agreement with the actual values recorded by the buoy. Additionally, both the Support Vector Machine and Gene Expression Programming models were able to predict significant wave height with high accuracy. A comparison of the results from the models revealed that the Support Vector Machine model, with statistical indices (Bias = 0.13, RMSE = 0.198, CC = 0.981, and CV = 0.147), estimated the target parameter more accurately than the Gene Expression Programming model. However, both models showed lower accuracy in simulating peak values. | ||
| کلیدواژهها [English] | ||
| Significant wave height, Anzali port, Gene expression programming model, Support vector mechine model, SWAN numerical model | ||
| مراجع | ||
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