دوره 7، شماره 14 - ( پاییز و زمستان 1390 1390 )                   جلد 7 شماره 14 صفحات 117-123 | برگشت به فهرست نسخه ها

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nemati M, Karami Khaniki A. Online Significant Wave Height Prediction in Persian Gulf Using Artificial Neural Networks and Regression Trees. Journal Of Marine Engineering. 2012; 7 (14) :117-123
URL: http://marine-eng.ir/article-1-193-fa.html
نعمتی مریم، کرمی خانیکی علی. پیش یابی ارتفاع موج شاخص در خلیج فارس با استفاده از شبکه های عصبی مصنوعی و مقایسه آن با درخت های تصمیم رگرسیونی. مهندسی دریا. 1390; 7 (14) :117-123

URL: http://marine-eng.ir/article-1-193-fa.html


کارشناس ارشد فيزيک دريا دانشگاه آزاد اسلامی واحد علوم و تحقیقات
چکیده:   (6168 مشاهده)
Prediction of wave height is of great importance in marine and coastal engineering. In this study, the performances of artificial neural networks (feed forward with back propagation algorithm) for online significant wave heights prediction, in Persian Gulf, were investigated. The data set used in this study comprises wave and wind data gathered from shallow water location in Persian Gulf. Current wind speed (u) and those belonging up to eight previous hours are given as input variables, while the significant wave height with leading time of 1-24 hour are the output parameters. Results show that the artificial neural networks can perform very well in predicting significant wave height, when shorter intervals of predictions (6 hour) were involved. Small interval predictions were made more accurately than the large interval ones. Results of artificial neural networks were compared with those of regression trees. Results indicate that error statistics of neural networks and regression trees were nearly similar
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نوع مطالعه: يادداشت فنی | موضوع مقاله: Environmental Study
دریافت: ۱۳۹۱/۳/۲۲ | پذیرش: ۱۳۹۲/۷/۲۷

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