Prediction of Air Pollutants’ Hourly Concentration in Tehran Using Artificial Neural Network and Specteral Decomposition of Time Series Data, M.Sc. Thesis Sharif University of Technology ; Arhami, Mohammad (Supervisor)
Abstract
Recent progress in developing Artificial Neural Network (ANN) meta-models has paved the way for reliable use of these models in the prediction of air pollutant concentrations in urban atmosphere. However, improvement of prediction performance, proper selection of input parameters and model architecture remain key challenges to their practical use. This study’s main objectives are: selecting proper input parameters for ANN meta-models, optimizing the ANN models to achieve the most accurate hourly prediction for a case study (City of Tehran), coupling the ANN method with filtered input data and evaluating its privilege. In the current study the ANNs were constructed to predict criteria...
Cataloging briefPrediction of Air Pollutants’ Hourly Concentration in Tehran Using Artificial Neural Network and Specteral Decomposition of Time Series Data, M.Sc. Thesis Sharif University of Technology ; Arhami, Mohammad (Supervisor)
Abstract
Recent progress in developing Artificial Neural Network (ANN) meta-models has paved the way for reliable use of these models in the prediction of air pollutant concentrations in urban atmosphere. However, improvement of prediction performance, proper selection of input parameters and model architecture remain key challenges to their practical use. This study’s main objectives are: selecting proper input parameters for ANN meta-models, optimizing the ANN models to achieve the most accurate hourly prediction for a case study (City of Tehran), coupling the ANN method with filtered input data and evaluating its privilege. In the current study the ANNs were constructed to predict criteria...
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