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A Bayesian Generative Adversarial Networks (GAN) to Generate Synthetic Time-Series Data, Application In Combined Sewer Flow Prediction

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A Bayesian Generative Adversarial Networks (GAN) to Generate Synthetic Time-Series Data, Application In Combined Sewer Flow Prediction

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Bakhshipour, A.; Koochali, A.; Dittmer, U.; Haghighi, A.; Ahmed, S.; Dengel, A. (2024). A Bayesian Generative Adversarial Networks (GAN) to Generate Synthetic Time-Series Data, Application In Combined Sewer Flow Prediction. Editorial Universitat Politècnica de València. https://doi.org/10.4995/WDSA-CCWI2022.2022.14699

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/205935

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Title: A Bayesian Generative Adversarial Networks (GAN) to Generate Synthetic Time-Series Data, Application In Combined Sewer Flow Prediction
Author: Bakhshipour, Amin Koochali, Alireza Dittmer, Ulrich Haghighi, Ali Ahmed, Sheraz Dengel, Andreas
Issued date:
Abstract:
[EN] Despite various breakthroughs of machine learning and data analysis techniques for improving smart operation and management of urban water infrastructures, some key limitations obstruct this progress. Among these ...[+]
Subjects: Machine Learning , Urban Water Infrastructures , Generative Adversarial Networks , Time Series Prediction , Synthetic time series generation , Combined Sewer Flow Prediction
Copyrigths: Reconocimiento - No comercial - Compartir igual (by-nc-sa)
ISBN: 9788490489826
DOI: 10.4995/WDSA-CCWI2022.2022.14699
Publisher:
Editorial Universitat Politècnica de València
Publisher version: http://ocs.editorial.upv.es/index.php/WDSA-CCWI/WDSA-CCWI2022/paper/view/14699
Conference name: 2nd WDSA/CCWI Joint Conference
Conference place: Valencia, España
Conference date: Julio 18-22, 2022
Type: Capítulo de libro Comunicación en congreso

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