...) 12 03 2025 25 10 2025 © 2025 Emerald Publishing Limited 2025 Emerald Publishing Limited Licensed re-use rights only artificial intelligence bridges hazards hydraulics & hydrodynamics neural networks University of Danang – University of Science and Technology...

... parameters show complex non-linear characteristics, which bring great challenges to traditional modelling technology (Bhattacharya and Solomatine, 2005). The development of information technology, computer technology and artificial intelligence methods has, however, provided a great opportunity...

... features. The dilated convolution size was calculated as: 4 k ′ = d ( k − 1 ) + 1 artificial intelligence dams, barrages & reservoirs erosion remote sensing images silt storage dams Silt storage dams (SSDs) serve purposes such as flood detention, sediment...

... randomly into two groups with 70% for training and 30% for testing. The ranges for both training and testing datasets of scour parameters are demonstrated in Table 2 . artificial intelligence bridge abutment hydraulics machine learning scour depth Experimental study combined...

... assessment of the potential benefits of vegetation in stream flows (Liu et al., 2008). 09 03 2021 30 12 2021 Emerald Publishing Limited: All rights reserved 2022 artificial intelligence artificial neural network (ANN) compound channel floods & floodworks support...

... ; Mirzavand et al., 2015), which suggest the superiority of ANFIS over other models in terms of prediction capability. 14 06 2020 03 11 2020 ICE Publishing: All rights reserved 2020 artificial intelligence groundwater hydrology & water resource In most arid...

... that may not need soil moisture estimation at each time step. Figure 2. Relationship between soil moisture and number of days after each rainfall event artificial intelligence dams, barrages & reservoirs hydrology & water resource Data-driven models have been employed...