1. Optimizing sediment transport models by using the Monte Carlo simulation and deep neural network (DNN): A case study of the Riba-Roja reservoir
- Author
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Universitat Politècnica de Catalunya. Departament d'Enginyeria Civil i Ambiental, Universitat Politècnica de Catalunya. Doctorat en Enginyeria Civil, Universitat Politècnica de Catalunya. Geo2Aqua - Monitoring, modelling and geomatics for hydro-geomorphological processes, Dehghan-Souraki, Danial, López Gómez, David, Bladé i Castellet, Ernest, Larese De Tetto, Antonia, Sanz Ramos, Marcos, Universitat Politècnica de Catalunya. Departament d'Enginyeria Civil i Ambiental, Universitat Politècnica de Catalunya. Doctorat en Enginyeria Civil, Universitat Politècnica de Catalunya. Geo2Aqua - Monitoring, modelling and geomatics for hydro-geomorphological processes, Dehghan-Souraki, Danial, López Gómez, David, Bladé i Castellet, Ernest, Larese De Tetto, Antonia, and Sanz Ramos, Marcos
- Abstract
This study emphasizes the importance of accurate calibration in sediment transport models and highlights the transformative role of artificial intelligence (AI), specifically machine learning, in improving accuracy and computational efficiency. Extensive experiments were carried out in the Riba-Roja reservoir, which is located in the northeastern Iberian Peninsula. The accumulated sediment volume (ASV) curve was used to calibrate these experiments. The optimal ASV curve was found to be very close to the experimental data, with only minor differences in upstream areas. The results revealed a consistent rate of sediment transport and settling. Furthermore, the study investigated the capabilities of deep neural networks (DNNs) in predicting ASV curves and observing variable performance. In essence, the study highlights AI's potential for enhancing sediment transport models., Postprint (published version)
- Published
- 2024