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Title Χρήση τεχνητών νευρωνικών δικτύων και στατιστικών τεχνικών για τη βελτίωση της πρόγνωσης μετεωρολογικών παραμέτρων
Author Θεολογίτου, Παρασκευή
Thesis advisor Κατσαούνης, Θεόδωρος
Reviewer Κοσιώρης, Γεώργιος
Τσιρώνης, Γεώργιος
Abstract Accurate weather forecasting is an integral part of everyday life and concerns both the personal life and the industrial development of societies. The use of computational models such as Artificial Neural Networks (ANN), which reduce errors and make predictions valid, constitute the appropriate tools for successful forecasting. The aim of this thesis is the setting up of an ANN to predict wind velocities, based on observational and numerical data, for the city of Heraklion, Crete during severe weather events. The comparison of different types of training algorithms of a Feed Forward ANN, such as the Back Propagation Algorithm, the Random Optimization Algorithm as well as a hybrid model consisting of the combination of the two previous algorithms, was set as a first priority. Simultaneously, the effectiveness of the structural elements that make up the ANN was examined. The analysis was performed by processing data from averaged daily temperatures. Subsequently, using the most efficient ANN configuration, an attempt was made for direct prediction of wind velocity in Heraklion, for days when severe weather events occurred. The numerical data were from WRF model simulations, whereas observational data were obtained from a surface weather station situated in the Physics Department Building of the University of Crete. Moreover, with the aid of statistical analysis, an attempt was made to predict a correction factor for the wind velocity which, by applying it to the numerical values, will improve the prediction. For this reason, an algorithm was developed to improve the lag in time that is observed between numeric and observations for the examined events. Finally, the results were evaluated, and conclusions were drawn on the ability of the ANN to efficiently predict the values of the wind velocities. Also, suggestions are made for future directions of research in order to optimize the performance of the ANN.
Language Greek
Issue date 2020-07-24
Collection   Faculty/Department--Faculty of Sciences and Engineering--Department of Physics--Graduate theses
  Type of Work--Graduate theses
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