پديد آورنده :
زماني، احمدرضا
عنوان :
پيش بيني امواج دريا به كمك مدل مبتني بر داده ها و تلفيق غير خطي داده ها در فضاي كاهش يافته
محل تحصيل :
اصفهان: دانشگاه صنعتي اصفهان، دانشكده مكانيك
صفحه شمار :
شانزده، 115، [II]ص.: مصور، جدول، نمودار
يادداشت :
ص.ع. به: فارسي و انگليسي
استاد راهنما :
احمدرضا عظيميان
استاد مشاور :
محمدسعيد سعيدي، محمدرضا احمدزاده
توصيفگر ها :
درياي خزر , شبكه عصبي بازگشتي , روش فيلتر كالمن , روش OI
تاريخ نمايه سازي :
14/12/87
استاد داور :
ابراهيم شيراني، عباسعلي علي اكبري بيدختي، محمود غياثي، محمد جواد كتابداري
كد ايرانداك :
ID220 دكتري
چكيده فارسي :
به فارسي و انگليسي: قابل رؤيت در نسخه ديجيتال
چكيده انگليسي :
Abstract Along with existing numerical process models describing the wind wave interaction the relatively recent development in the area of machine learning make the so called data driven models more and more popular This study presents a number of data driven models for wind wave process at the Caspian Sea The problem associated with these models is to forecast significant wave heights for several hours ahead using buoy measurements Models are based on Artificial Neural Network ANN and Instance Based Learning IBL To capture the wind wave relationship at measurement sites these models use the existing past time data describing the phenomenon in question Three feed forward ANN models have been built for time horizon of 1 3 and 6 hours with different inputs The relevant inputs are selected by analyzing the Average Mutual Information AMI The inputs consist of priori knowledge of wind and significant wave height The other six models are based on IBL method for the same forecast horizons Weighted k Nearest Neighbors k NN and Locally Weighted Regression LWR with Gaussian kernel were used In IBL based models forecast is made directly by combining instances from the training data that are close in the input space to the new incoming input vector These methods are applied to two sets of data at the Caspian Sea Experiments show that the ANNs yield slightly better agreement with the measured data than IBL ANNs can also predict extreme wave conditions better than the other existing methods Non linear data assimilation for a wind wave dynamical surrogate model in a reduced space is presented in next part of this study This surrogate provides a fast emulation of a wind wave model Such a fast dynamical surrogate is used for the evaluation of the system states in a small period of time The system state consists of wave height and wave direction in reduce space which is affected by reduce space wind field The projection from full space to reduced space is done by a principal component analysis It is computationally efficient to couple this surrogate with an Ensemble Kalman filter EnKF Ensemble methods require the evaluation of dynamics for a large number of statistical ensembles Application of the procedure is demonstrated through 6 month hindcast study of wind waves over a Caspian Sea using third generation wave model and analysis ECMWF wind field Also a dynamic Artificial Neural Network for surrogate model of wind wave process is used in this work The trained network is embedded into the stochastic environment and the EnKF is used to find estimates of the system states Experiments show that the proposed DA technique corrects the prediction of the wind waves with a modest execution time pdfMachine A pdf writer that produces quality PDF files with ease Produce quality PDF files in seconds and preserve the integrity of your original documents Compatible across nearly all Windows platforms if you can print from a windows application you can use pdfMachine Get yours now
استاد راهنما :
احمدرضا عظيميان
استاد مشاور :
محمدسعيد سعيدي، محمدرضا احمدزاده
استاد داور :
ابراهيم شيراني، عباسعلي علي اكبري بيدختي، محمود غياثي، محمد جواد كتابداري
