Publicacións

@article{farfan-duran_integrating_2025,

title = {Integrating net rainfall calculation in deep learning-based surrogate modeling frameworks for 2D flood prediction},

author = {Juan F. Farfán-Durán and Carlos I. Montalvo-Montenegro and Luis Cea-Gómez and João P. Leitão},

url = {https://www.sciencedirect.com/science/article/pii/S0022169425009709},

doi = {10.1016/j.jhydrol.2025.133632},

issn = {0022-1694},

year  = {2025},

date = {2025-11-01},

urldate = {2025-11-01},

journal = {Journal of Hydrology},

volume = {661},

pages = {133632},

abstract = {This study proposes a novel deep learning (DL)-based surrogate model that incorporates the calculation of net rainfall using the SCS-CN method, providing a flexible framework for evaluating the influence of rainfall events under different antecedent moisture conditions (AMC). The proposed framework involves establishing a ground truth model (Iber-SWMM) and defining the necessary terrain features and rainfall patterns for training the surrogate. A benchmark surrogate model using only gross rainfall, replicating methodologies from previous studies, is also developed for comparison. The trained models are then applied to predict water depth maps using test rainfall patterns under different scenarios, both with and without net rainfall. The results demonstrate that the proposed surrogate model reduces the computational times of Iber-SWMM by 2 to 4 orders of magnitude while outperforming the benchmark surrogate in all the measures. It presents satisfactory accuracy in water depth prediction, with 80% to 95% of predictions within a -0.2 to 0.2 m error range and hit ratios between 0.87 to 0.91 in terms of flooded pixels in the more extreme events. These outcomes are comparable to those achieved by a physics-based model on one of the test events. The study also suggests future lines for refinement.},

keywords = {Deep learning, Flood prediction, Hydrological modeling, Surrogate model, Urban hydrology},

pubstate = {published},

tppubtype = {article}

}

@article{alvarellos_deep_2024,

title = {Deep Learning-Based Wave Overtopping Prediction},

author = {Alberto Alvarellos-González and Andrés Figuero-Pérez and Santiago Rodríguez-Yáñez and José Sande-González-Cela and Enrique Peña-González and Paulo Rosa-Santos and Juan R. Rabuñal-Dopico},

url = {https://www.mdpi.com/2076-3417/14/6/2611},

doi = {10.3390/app14062611},

issn = {2076-3417},

year  = {2024},

date = {2024-01-01},

urldate = {2026-04-20},

journal = {Applied Sciences},

volume = {14},

number = {6},

pages = {2611},

publisher = {Multidisciplinary Digital Publishing Institute},

abstract = {This paper analyses the application of deep learning techniques for predicting wave overtopping events in port environments using sea state and weather forecasts as inputs. The study was conducted in the outer port of Punta Langosteira, A Coruña, Spain. A video-recording infrastructure was installed to monitor overtopping events from 2015 to 2022, identifying 3709 overtopping events. The data collected were merged with actual and predicted data for the sea state and weather conditions during the overtopping events, creating three datasets. We used these datasets to create several machine learning models to predict whether an overtopping event would occur based on sea state and weather conditions. The final models achieved a high accuracy level during the training and testing stages: 0.81, 0.73, and 0.84 average accuracy during training and 0.67, 0.48, and 0.86 average accuracy during testing, respectively. The results of this study have significant implications for port safety and efficiency, as wave overtopping events can cause disruptions and potential damage. Using deep learning techniques for overtopping prediction can help port managers take preventative measures and optimize operations, ultimately improving safety and helping to minimize the economic impact that overtopping events have on the port’s activities.},

keywords = {Deep learning, machine learning, Neural networks, port management, port security, wave overtopping prediction},

pubstate = {published},

tppubtype = {article}

}