Titlebook: Advances in Hydroinformatics—SimHydro 2023 Volume 2; New Modelling Paradi Philippe Gourbesville,Guy Caignaert Conference proceedings 2024 T

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2364-6934 and to assess the added value of emerging concepts and methods such as Artificial Intelligence (AI) and Digital Twins that are gaining interests. It addresses the interests of practitioners, stakeholders, resea978-981-97-4078-9978-981-97-4076-5Series ISSN 2364-6934 Series E-ISSN 2364-8198

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Impact of Training Dataset Size and Its Hydrometeorological Typology on LSTM Performance for Rainfael performance. This study reveals LSTM sensitivity to training periods, aiding the optimization of training duration for better discharge prediction accuracy. Future research will delve into year selection within typological clusters.

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Bayesian Model Averaging Approach for Urban Drainage Water Quality Modelling,and average their output response to reduce the related uncertainty. In the current report, the Bayesian Model Averaging is applied to a real catchment and is compared with several single water quality models. The analysis showed that the Bayesian Model Averaging approach outperformed all single model applications.

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https://doi.org/10.1007/978-3-642-91420-1have been carried out with rainfall–runoff relations to estimate its floods, but this article uses the Engineering Institute Method, based on an analysis of mean daily flows to obtain design hydrographs for different return periods.