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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. • The study underscores the value of AI in enhancing sediment transport model calibration. • The simulations revealed accurate results, with minor discrepancies in upstream regions. • The DNNs, demonstrated reliable predictive capabilities in forecasting ASV curves. • AI, coupled with Monte Carlo method, reduces sediment model computation time. [ABSTRACT FROM AUTHOR]

Eco-hydraulic models are wide extended tools to assess physical habitat suitability on aquatic environments. Currently, the application of these tools is limited to short river stretches and steady flow simulations. However, this limitation can be overcome with the application of a high-performance computing technique: graphics processing unit (GPU) computing. R-Iber is a GPU-based hydrodynamic code parallelised in CUDA Fortran that, with the integration of a biological module, performs as an eco-hydraulic numerical tool. R-Iber was validated and applied to real cases by using an optimised instream flow incremental methodology in long river reaches and long-term simulations. R-Iber reduces the computation time considerably, reaching speed-ups of two orders of magnitude compared to traditional computing. R-Iber allows for overcoming the current limitations of the eco-hydraulic tools with the simulation of high-resolution numerical models calculated in a reasonable computation timeframe, which provides a better representation of the hydrodynamics and the physical habitat. • A hydrodynamic HPC-based model for fish habitat assessment. • Code parallelisation with CUDA Fortran programming language. • Code optimization for multi-GPU computing reaching speed-ups above 100-times. • Integration, improve and assessment of the IFIM (WUA-Q relation) in a single model. • High-resolution and long-term habitat modelling in long river reaches. [ABSTRACT FROM AUTHOR]