O centro
Volver al menú principal
Investigación
Laboratorios
Transferencia
Actualidade
Español
Publicacións
O CITEEC lidera a investigación en enxeñaría civil e edificación, enfocándose en solucións innovadoras para infraestruturas sostibles e materiais avanzados.
publicacións
Investigación de vangarda para o desenvolvemento sostible
O CITEEC realiza investigación avanzada en enxeñaría civil, materiais, sustentabilidade e infraestruturas. A través de proxectos multidisciplinarios e colaboracións internacionais, xéranse publicacións científicas que abordan retos crave do sector. Nesta sección preséntanse as principais publicacións e resultados obtidos polos nosos grupos de investigación.
Accede a unha ampla variedade de publicacións científicas filtradas por temática, autor, ano e máis. Utiliza os filtros dispoñibles para atopar investigacións relevantes e obter detalles sobre cada unha das nosas contribucións ao avance do coñecemento.
2024
Nogueira-Garea, Xesús; Fernández-Fidalgo, Javier; Ramos-García, Lucía; Couceiro-Aguiar, Iván; Ramírez-Palacios, Luis
Machine learning-based WENO5 scheme Journal Article
In: Computers & Mathematics with Applications, vol. 168, pp. 84–99, 2024, ISSN: 0898-1221.
Abstract | Links | BibTeX | Etiquetas: Euler equations, Finite difference, machine learning, Neural networks, WENO
@article{nogueira_machine_2024,
title = {Machine learning-based WENO5 scheme},
author = {Xesús Nogueira-Garea and Javier Fernández-Fidalgo and Lucía Ramos-García and Iván Couceiro-Aguiar and Luis Ramírez-Palacios},
url = {https://www.sciencedirect.com/science/article/pii/S0898122124002505},
doi = {10.1016/j.camwa.2024.05.031},
issn = {0898-1221},
year = {2024},
date = {2024-08-01},
urldate = {2026-04-21},
journal = {Computers & Mathematics with Applications},
volume = {168},
pages = {84–99},
abstract = {Machine learning (ML) is becoming a powerful tool in Computational Fluid Dynamics (CFD) to enhance the accuracy, efficiency, and automation of simulations. Currently, in the design of shock-capturing methods, there is still a heavy reliance on the expertise and scientific knowledge of each author, particularly in nonlinear components such as smoothness indicators and weighting functions. ML has the potential to reduce this dependency, since by leveraging large datasets, they can learn intricate patterns and make accurate predictions of these functions. In this work we present a neural network that compute the weighting functions in the WENO5 scheme. The proposed WENO5-NN scheme generalizes well for different resolutions, and in most of the cases tested, it outperforms the classical WENO5-JS scheme.},
keywords = {Euler equations, Finite difference, machine learning, Neural networks, WENO},
pubstate = {published},
tppubtype = {article}
}
Machine learning (ML) is becoming a powerful tool in Computational Fluid Dynamics (CFD) to enhance the accuracy, efficiency, and automation of simulations. Currently, in the design of shock-capturing methods, there is still a heavy reliance on the expertise and scientific knowledge of each author, particularly in nonlinear components such as smoothness indicators and weighting functions. ML has the potential to reduce this dependency, since by leveraging large datasets, they can learn intricate patterns and make accurate predictions of these functions. In this work we present a neural network that compute the weighting functions in the WENO5 scheme. The proposed WENO5-NN scheme generalizes well for different resolutions, and in most of the cases tested, it outperforms the classical WENO5-JS scheme.
Alvarellos-González, Alberto; Figuero-Pérez, Andrés; Rodríguez-Yáñez, Santiago; Sande-González-Cela, José; Peña-González, Enrique; Rosa-Santos, Paulo; Rabuñal-Dopico, Juan R.
Deep Learning-Based Wave Overtopping Prediction Journal Article
In: Applied Sciences, vol. 14, no. 6, pp. 2611, 2024, ISSN: 2076-3417.
Abstract | Links | BibTeX | Etiquetas: Deep learning, machine learning, Neural networks, port management, port security, wave overtopping prediction
@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}
}
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.
Cedrón, Francisco; Álvarez-González, Sara; Ribas-Rodríguez, Ana; Rodríguez-Yáñez, Santiago; Porto-Pazos, Ana Belén
Efficient Implementation of Multilayer Perceptrons: Reducing Execution Time and Memory Consumption Journal Article
In: Applied Sciences, vol. 14, no. 17, pp. 8020, 2024, ISSN: 2076-3417.
Abstract | Links | BibTeX | Etiquetas: compressed weight matrix, multilayer perceptron, Neural networks, sparsity, weight density
@article{cedron_efficient_2024,
title = {Efficient Implementation of Multilayer Perceptrons: Reducing Execution Time and Memory Consumption},
author = {Francisco Cedrón and Sara Álvarez-González and Ana Ribas-Rodríguez and Santiago Rodríguez-Yáñez and Ana Belén Porto-Pazos},
url = {https://www.mdpi.com/2076-3417/14/17/8020},
doi = {10.3390/app14178020},
issn = {2076-3417},
year = {2024},
date = {2024-01-01},
urldate = {2026-04-28},
journal = {Applied Sciences},
volume = {14},
number = {17},
pages = {8020},
publisher = {Multidisciplinary Digital Publishing Institute},
abstract = {A technique is presented that reduces the required memory of neural networks through improving weight storage. In contrast to traditional methods, which have an exponential memory overhead with the increase in network size, the proposed method stores only the number of connections between neurons. The proposed method is evaluated on feedforward networks and demonstrates memory saving capabilities of up to almost 80% while also being more efficient, especially with larger architectures.},
keywords = {compressed weight matrix, multilayer perceptron, Neural networks, sparsity, weight density},
pubstate = {published},
tppubtype = {article}
}
A technique is presented that reduces the required memory of neural networks through improving weight storage. In contrast to traditional methods, which have an exponential memory overhead with the increase in network size, the proposed method stores only the number of connections between neurons. The proposed method is evaluated on feedforward networks and demonstrates memory saving capabilities of up to almost 80% while also being more efficient, especially with larger architectures.
2020
Farfán-Durán, Juan F.; Palacios, Karina; Ulloa, Jacinto; Avilés, Alex
In: Journal of Hydrology: Regional Studies, vol. 27, pp. 100652, 2020, ISSN: 2214-5818.
Abstract | Links | BibTeX | Etiquetas: Andean watersheds, Ecuador, Flow forecasting, Hydrological models, Neural networks
@article{farfan_hybrid_2020,
title = {A hybrid neural network-based technique to improve the flow forecasting of physical and data-driven models: Methodology and case studies in Andean watersheds},
author = {Juan F. Farfán-Durán and Karina Palacios and Jacinto Ulloa and Alex Avilés},
url = {https://www.sciencedirect.com/science/article/pii/S2214581818303409},
doi = {10.1016/j.ejrh.2019.100652},
issn = {2214-5818},
year = {2020},
date = {2020-02-01},
urldate = {2026-08-06},
journal = {Journal of Hydrology: Regional Studies},
volume = {27},
pages = {100652},
abstract = {Study region
The present study was conducted in the Machángara Alto and Chulco rivers, which belong to the Paute basin in the provinces of Azuay and Cañar in southern Ecuador.
Study focus
Andean watersheds are important providers of water supply for human consumption, food supply, energy generation, industrial water use, and ecosystem services and functions for many cities in Ecuador and in the rest of South America. In these regions, accurate quantification and prediction of water flow is challenging, mainly due to significant climatic variability and sparse monitoring networks. In the context of flow forecasting, this work evaluates the accuracy of two physical models (WEAP and GR2M) and two models based on artificial neural networks (ANN) that use meteorological data as input variables. Then, a hybrid technique is proposed, using the time series generated by the individual models as inputs of a new ANN. This approach aims to increase the accuracy of the simulated flow by combining and exploiting the information provided by physical and data-driven models. To assess the performance of the proposed methodology, statistical analyses are conducted for two case studies in the Andean region, where comparative analyses are performed for the individual models and the hybrid technique.
New hydrological insights
The results indicate that the proposed technique is able to improve the individual performance of physical and ANN-based models, yielding good results in the calibration and validation stages for the two case studies. Specifically, increases in NSE were observed from 0.64 to 0.99 in the MachÁngara Alto river, and from 0.88 to 0.99 in the Chulco river. Higher accuracy of the hybrid technique was observed for all evaluation criteria considered in the analyses. The performance of the hybrid technique was also reflected in terms of water supply and demand, suggesting possible applications for the regional management of water resources, where accurate flow predictions are of utmost importance.},
keywords = {Andean watersheds, Ecuador, Flow forecasting, Hydrological models, Neural networks},
pubstate = {published},
tppubtype = {article}
}
Study region
The present study was conducted in the Machángara Alto and Chulco rivers, which belong to the Paute basin in the provinces of Azuay and Cañar in southern Ecuador.
Study focus
Andean watersheds are important providers of water supply for human consumption, food supply, energy generation, industrial water use, and ecosystem services and functions for many cities in Ecuador and in the rest of South America. In these regions, accurate quantification and prediction of water flow is challenging, mainly due to significant climatic variability and sparse monitoring networks. In the context of flow forecasting, this work evaluates the accuracy of two physical models (WEAP and GR2M) and two models based on artificial neural networks (ANN) that use meteorological data as input variables. Then, a hybrid technique is proposed, using the time series generated by the individual models as inputs of a new ANN. This approach aims to increase the accuracy of the simulated flow by combining and exploiting the information provided by physical and data-driven models. To assess the performance of the proposed methodology, statistical analyses are conducted for two case studies in the Andean region, where comparative analyses are performed for the individual models and the hybrid technique.
New hydrological insights
The results indicate that the proposed technique is able to improve the individual performance of physical and ANN-based models, yielding good results in the calibration and validation stages for the two case studies. Specifically, increases in NSE were observed from 0.64 to 0.99 in the MachÁngara Alto river, and from 0.88 to 0.99 in the Chulco river. Higher accuracy of the hybrid technique was observed for all evaluation criteria considered in the analyses. The performance of the hybrid technique was also reflected in terms of water supply and demand, suggesting possible applications for the regional management of water resources, where accurate flow predictions are of utmost importance.
The present study was conducted in the Machángara Alto and Chulco rivers, which belong to the Paute basin in the provinces of Azuay and Cañar in southern Ecuador.
Study focus
Andean watersheds are important providers of water supply for human consumption, food supply, energy generation, industrial water use, and ecosystem services and functions for many cities in Ecuador and in the rest of South America. In these regions, accurate quantification and prediction of water flow is challenging, mainly due to significant climatic variability and sparse monitoring networks. In the context of flow forecasting, this work evaluates the accuracy of two physical models (WEAP and GR2M) and two models based on artificial neural networks (ANN) that use meteorological data as input variables. Then, a hybrid technique is proposed, using the time series generated by the individual models as inputs of a new ANN. This approach aims to increase the accuracy of the simulated flow by combining and exploiting the information provided by physical and data-driven models. To assess the performance of the proposed methodology, statistical analyses are conducted for two case studies in the Andean region, where comparative analyses are performed for the individual models and the hybrid technique.
New hydrological insights
The results indicate that the proposed technique is able to improve the individual performance of physical and ANN-based models, yielding good results in the calibration and validation stages for the two case studies. Specifically, increases in NSE were observed from 0.64 to 0.99 in the MachÁngara Alto river, and from 0.88 to 0.99 in the Chulco river. Higher accuracy of the hybrid technique was observed for all evaluation criteria considered in the analyses. The performance of the hybrid technique was also reflected in terms of water supply and demand, suggesting possible applications for the regional management of water resources, where accurate flow predictions are of utmost importance.
2011
Rodriguez, Álvaro; Bermúdez, María; Rabuñal-Dopico, Juan R.; Puertas-Agudo, Jerónimo; Dorado, Julián; Pena-Mosquera, Luis; Balairón, Luis
Optical fish trajectory measurement in fishways through computer vision and artificial neural networks Journal Article
In: Journal of Computing in Civil Engineering, vol. 25, no. 4, pp. 291-301, 2011, ISSN: 08873801, (cited By 36).
Abstract | Links | BibTeX | Etiquetas: Animal behavior; Biological variables; Camera systems; Computer vision techniques; Fish behavior; Fish management; Fish passage; Fish species; Hydrodynamic properties; Trajectory measurements; Upstream migration; Vertical-slot, Civil engineering; Computer applications; Computer networks; Computer vision; Fish; Fisheries; Fishways; Hydraulics; Systems analysis, Neural networks
@article{Rodriguez2011291,
title = {Optical fish trajectory measurement in fishways through computer vision and artificial neural networks},
author = {Álvaro Rodriguez and María Bermúdez and Juan R. Rabuñal-Dopico and Jerónimo Puertas-Agudo and Julián Dorado and Luis Pena-Mosquera and Luis Balairón},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-79960207630&doi=10.1061%2f%28ASCE%29CP.1943-5487.0000092&partnerID=40&md5=042dcd4e0017d3618043c98df392f627},
doi = {10.1061/(ASCE)CP.1943-5487.0000092},
issn = {08873801},
year = {2011},
date = {2011-01-01},
urldate = {2011-01-01},
journal = {Journal of Computing in Civil Engineering},
volume = {25},
number = {4},
pages = {291-301},
abstract = {Vertical slot fishways are hydraulic structures that allow the upstream migration of fish through obstructions in rivers. The appropriate design of a vertical slot fishway depends on the interplay between hydraulic and biological variables because the hydrodynamic properties of the fishway must match the requirements of the fish species for which it is intended. One of the primary difficulties associated with studies of real fish behavior in fishway models is that the existing mechanisms to measure the behavior of the fish in these assays, such as direct observation or placement of sensors on the specimens, are impractical or unduly affect the animal behavior. This paper proposes a new procedure for measuring the behavior of the fish. The proposed technique uses artificial neural networks and computer vision techniques to analyze images obtained from the assays by means of a camera system designed for fishway integration. It is expected that this technique will provide detailed information about the fish behavior, and it will help to improve fish passage devices, which is currently a subject of interest in the area of civil engineering. A series of assays has been performed to validate this new approach in a full-scale fishway model with living fish. We have obtained very promising results that allow accurate reconstruction of the movements of the fish within the fishway. © 2011 American Society of Civil Engineers.},
note = {cited By 36},
keywords = {Animal behavior; Biological variables; Camera systems; Computer vision techniques; Fish behavior; Fish management; Fish passage; Fish species; Hydrodynamic properties; Trajectory measurements; Upstream migration; Vertical-slot, Civil engineering; Computer applications; Computer networks; Computer vision; Fish; Fisheries; Fishways; Hydraulics; Systems analysis, Neural networks},
pubstate = {published},
tppubtype = {article}
}
Vertical slot fishways are hydraulic structures that allow the upstream migration of fish through obstructions in rivers. The appropriate design of a vertical slot fishway depends on the interplay between hydraulic and biological variables because the hydrodynamic properties of the fishway must match the requirements of the fish species for which it is intended. One of the primary difficulties associated with studies of real fish behavior in fishway models is that the existing mechanisms to measure the behavior of the fish in these assays, such as direct observation or placement of sensors on the specimens, are impractical or unduly affect the animal behavior. This paper proposes a new procedure for measuring the behavior of the fish. The proposed technique uses artificial neural networks and computer vision techniques to analyze images obtained from the assays by means of a camera system designed for fishway integration. It is expected that this technique will provide detailed information about the fish behavior, and it will help to improve fish passage devices, which is currently a subject of interest in the area of civil engineering. A series of assays has been performed to validate this new approach in a full-scale fishway model with living fish. We have obtained very promising results that allow accurate reconstruction of the movements of the fish within the fishway. © 2011 American Society of Civil Engineers.
2010
Rivero, Daniel; Dorado, Julián; Rabuñal-Dopico, Juan R.; Pazos, Alejandro
Generation and simplification of Artificial Neural Networks by means of Genetic Programming Journal Article
In: Neurocomputing, vol. 73, no. 16-18, pp. 3200-3223, 2010, ISSN: 09252312, (cited By 27).
Abstract | Links | BibTeX | Etiquetas: Artificial Neural Network; Artificial neural networks; Evolutionary computations; Human expert; Specific problems; Training methods; Worst case, Calculations; Experiments; Genetic algorithms; Genetic programming, Neural networks
@article{Rivero20103200,
title = {Generation and simplification of Artificial Neural Networks by means of Genetic Programming},
author = {Daniel Rivero and Julián Dorado and Juan R. Rabuñal-Dopico and Alejandro Pazos},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-78650178182&doi=10.1016%2fj.neucom.2010.05.010&partnerID=40&md5=12773785cb929f079930e938f161e628},
doi = {10.1016/j.neucom.2010.05.010},
issn = {09252312},
year = {2010},
date = {2010-01-01},
urldate = {2010-01-01},
journal = {Neurocomputing},
volume = {73},
number = {16-18},
pages = {3200-3223},
abstract = {The development of Artificial Neural Networks (ANNs) is traditionally a slow process in which human experts are needed to experiment on different architectural procedures until they find the one that presents the correct results that solve a specific problem. This work describes a new technique that uses Genetic Programming (GP) in order to automatically develop simple ANNs, with a low number of neurons and connections. Experiments have been carried out in order to measure the behavior of the system and also to compare the results obtained using other ANN generation and training methods with evolutionary computation (EC) tools. The obtained results are, in the worst case, at least comparable to existing techniques and, in many cases, substantially better. As explained herein, the system has other important features such as variable discrimination, which provides new information on the problems to be solved. © 2010 Elsevier B.V.},
note = {cited By 27},
keywords = {Artificial Neural Network; Artificial neural networks; Evolutionary computations; Human expert; Specific problems; Training methods; Worst case, Calculations; Experiments; Genetic algorithms; Genetic programming, Neural networks},
pubstate = {published},
tppubtype = {article}
}
The development of Artificial Neural Networks (ANNs) is traditionally a slow process in which human experts are needed to experiment on different architectural procedures until they find the one that presents the correct results that solve a specific problem. This work describes a new technique that uses Genetic Programming (GP) in order to automatically develop simple ANNs, with a low number of neurons and connections. Experiments have been carried out in order to measure the behavior of the system and also to compare the results obtained using other ANN generation and training methods with evolutionary computation (EC) tools. The obtained results are, in the worst case, at least comparable to existing techniques and, in many cases, substantially better. As explained herein, the system has other important features such as variable discrimination, which provides new information on the problems to be solved. © 2010 Elsevier B.V.
2009
Rivero, Daniel; Dorado, Julián; Rabuñal-Dopico, Juan R.; Pazos, Alejandro
Evolving simple feed-forward and recurrent ANNs for signal classification: A comparison Conference
2009, ISBN: 9781424435531, (cited By 8).
Abstract | Links | BibTeX | Etiquetas: Artificial neural networks; Classification tasks; EEG signals; Epileptic seizures; Evolutionary method; Feed-Forward; Hidden neurons; Human expert; Machine learning techniques; Real-world problem; Signal classification; Training parameters, Learning algorithms, Neural networks
@conference{Rivero20092685,
title = {Evolving simple feed-forward and recurrent ANNs for signal classification: A comparison},
author = {Daniel Rivero and Julián Dorado and Juan R. Rabuñal-Dopico and Alejandro Pazos},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-70449395916&doi=10.1109%2fIJCNN.2009.5178621&partnerID=40&md5=19cbca88fdb79d64facaeeed89a4e26e},
doi = {10.1109/IJCNN.2009.5178621},
isbn = {9781424435531},
year = {2009},
date = {2009-01-01},
urldate = {2009-01-01},
journal = {Proceedings of the International Joint Conference on Neural Networks},
pages = {2685-2692},
abstract = {Among all of the Machine Learning techniques used for classification tasks, Artificial Neural Networks (ANNs) have obtained much success in their applications. However, their development usually requires a manual effort from the human expert in which several parameter configurations (architectures, training parameters, etc) are tried. This paper proposes a new evolutionary method that evolves ANNs without any participation from the human expert. This system can be used to evolve feed-forward and recurrent ANNs. A real-world problem has been used to test the behaviour of this system: detection of epileptic seizures in EEG signals. A comparison of the results obtained using recurrent and feedforward ANNs to solve this problem is presented in this paper. This comparison shows the good accuracies obtained by this method (almost 100%). Moreover, these results show an important feature: the system tries to evolve simple ANNs, with a low number of neurons and connections (in many cases, the networks have only 1 hidden neuron). ©2009 IEEE.},
note = {cited By 8},
keywords = {Artificial neural networks; Classification tasks; EEG signals; Epileptic seizures; Evolutionary method; Feed-Forward; Hidden neurons; Human expert; Machine learning techniques; Real-world problem; Signal classification; Training parameters, Learning algorithms, Neural networks},
pubstate = {published},
tppubtype = {conference}
}
Among all of the Machine Learning techniques used for classification tasks, Artificial Neural Networks (ANNs) have obtained much success in their applications. However, their development usually requires a manual effort from the human expert in which several parameter configurations (architectures, training parameters, etc) are tried. This paper proposes a new evolutionary method that evolves ANNs without any participation from the human expert. This system can be used to evolve feed-forward and recurrent ANNs. A real-world problem has been used to test the behaviour of this system: detection of epileptic seizures in EEG signals. A comparison of the results obtained using recurrent and feedforward ANNs to solve this problem is presented in this paper. This comparison shows the good accuracies obtained by this method (almost 100%). Moreover, these results show an important feature: the system tries to evolve simple ANNs, with a low number of neurons and connections (in many cases, the networks have only 1 hidden neuron). ©2009 IEEE.
Miguélez, Mónica; Puertas-Agudo, Jerónimo; Rabuñal-Dopico, Juan R.
Artificial neural networks in urban runoff forecast Journal Article
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5517 LNCS, no. PART 1, pp. 1192-1199, 2009, ISSN: 03029743, (cited By 4).
Abstract | Links | BibTeX | Etiquetas: AI techniques; Artificial neural network; Artificial neural networks; Predictive structures; Specific areas; Urban runoff, Backpropagation; Civil engineering; Data handling; Genetic algorithms; Mining; Runoff; Sewage; Time series, Neural networks
@article{Miguélez20091192,
title = {Artificial neural networks in urban runoff forecast},
author = {Mónica Miguélez and Jerónimo Puertas-Agudo and Juan R. Rabuñal-Dopico},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-68749088897&doi=10.1007%2f978-3-642-02478-8_149&partnerID=40&md5=f0f5fea54102aca3715419f81383a183},
doi = {10.1007/978-3-642-02478-8_149},
issn = {03029743},
year = {2009},
date = {2009-01-01},
urldate = {2009-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {5517 LNCS},
number = {PART 1},
pages = {1192-1199},
abstract = {One of the applications of Data Mining is the extraction of knowledge from time series [1][2]. The Artificial Neural Networks (ANNs), one of the techniques of Artificial Intelligence (AI), have proved to be suitable in Data Mining for handling this type of series. This paper presents the use of ANNs and Genetic Algorithms (GA) with a time series in the field of Civil Engineering where the predictive structure does not follow the classic paradigms. In this specific case, the AI technique is applied to a phenomenon that models the process where, for a specific area, the fallen rain concentrates and flows on the surface. © 2009 Springer Berlin Heidelberg.},
note = {cited By 4},
keywords = {AI techniques; Artificial neural network; Artificial neural networks; Predictive structures; Specific areas; Urban runoff, Backpropagation; Civil engineering; Data handling; Genetic algorithms; Mining; Runoff; Sewage; Time series, Neural networks},
pubstate = {published},
tppubtype = {article}
}
One of the applications of Data Mining is the extraction of knowledge from time series [1][2]. The Artificial Neural Networks (ANNs), one of the techniques of Artificial Intelligence (AI), have proved to be suitable in Data Mining for handling this type of series. This paper presents the use of ANNs and Genetic Algorithms (GA) with a time series in the field of Civil Engineering where the predictive structure does not follow the classic paradigms. In this specific case, the AI technique is applied to a phenomenon that models the process where, for a specific area, the fallen rain concentrates and flows on the surface. © 2009 Springer Berlin Heidelberg.
2008
Iglesias, Gregorio; Rabuñal-Dopico, Juan R.; Losada, Miguel A.; Pachón, H.; Castro, Alberte; Carballo, Rodrigo
A virtual laboratory for stability tests of rubble-mound breakwaters Journal Article
In: Ocean Engineering, vol. 35, no. 11-12, pp. 1113-1120, 2008, ISSN: 00298018, (cited By 22).
Abstract | Links | BibTeX | Etiquetas: Activation function (AF); Applied (CO); Artificial neural network (ANNs); Elsevier (CO); Experimental campaign; In order; Physical model (PM); Physical model testing; Rubble mound breakwaters; Stability testing; Virtual laboratory (VL); wave actions, artificial intelligence; artificial neural network; breakwater; coastal engineering; model test; wave action, Artificial intelligence; Backpropagation; Breakwaters; Computer architecture; Computer networks; Electric fault location; Hydraulic structures; Lightning; Mathematical models; Metropolitan area networks; Network architecture; Network protocols; System stability; Testing; Vegetation, Neural networks
@article{Iglesias20081113,
title = {A virtual laboratory for stability tests of rubble-mound breakwaters},
author = {Gregorio Iglesias and Juan R. Rabuñal-Dopico and Miguel A. Losada and H. Pachón and Alberte Castro and Rodrigo Carballo},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-46749118744&doi=10.1016%2fj.oceaneng.2008.04.014&partnerID=40&md5=a0210a41b2a52f5f2bf3222449993c6d},
doi = {10.1016/j.oceaneng.2008.04.014},
issn = {00298018},
year = {2008},
date = {2008-01-01},
urldate = {2008-01-01},
journal = {Ocean Engineering},
volume = {35},
number = {11-12},
pages = {1113-1120},
abstract = {The prediction of rubble-mound breakwater damage under wave action has usually relied on costly and time-consuming physical model tests. In this work, artificial neural networks (ANNs) are applied to estimate the outcome of a physical model throughout an experimental campaign comprising of 127 stability tests. In order to choose the network best suited to the problem data, five different activation function options and 38 network architectures are compared. The good agreement found between the physical model and the neural network shows that an ANN may well serve as a virtual laboratory, reducing the number of physical model tests necessary for a project. © 2008 Elsevier Ltd. All rights reserved.},
note = {cited By 22},
keywords = {Activation function (AF); Applied (CO); Artificial neural network (ANNs); Elsevier (CO); Experimental campaign; In order; Physical model (PM); Physical model testing; Rubble mound breakwaters; Stability testing; Virtual laboratory (VL); wave actions, artificial intelligence; artificial neural network; breakwater; coastal engineering; model test; wave action, Artificial intelligence; Backpropagation; Breakwaters; Computer architecture; Computer networks; Electric fault location; Hydraulic structures; Lightning; Mathematical models; Metropolitan area networks; Network architecture; Network protocols; System stability; Testing; Vegetation, Neural networks},
pubstate = {published},
tppubtype = {article}
}
The prediction of rubble-mound breakwater damage under wave action has usually relied on costly and time-consuming physical model tests. In this work, artificial neural networks (ANNs) are applied to estimate the outcome of a physical model throughout an experimental campaign comprising of 127 stability tests. In order to choose the network best suited to the problem data, five different activation function options and 38 network architectures are compared. The good agreement found between the physical model and the neural network shows that an ANN may well serve as a virtual laboratory, reducing the number of physical model tests necessary for a project. © 2008 Elsevier Ltd. All rights reserved.
2007
Rivero, Daniel; Dorado, Julián; Rabuñal-Dopico, Juan R.; Gestal, Marcos
A comparison between ANN generation and training methods and their development by means of graph evolution: 2 sample problems Journal Article
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4507 LNCS, pp. 94-101, 2007, ISSN: 03029743, (cited By 0).
Abstract | Links | BibTeX | Etiquetas: Computer aided software engineering; Evolutionary algorithms; Graph theory; Problem solving; Real time systems, Development tools; Network encoding, Neural networks
@article{Rivero200794,
title = {A comparison between ANN generation and training methods and their development by means of graph evolution: 2 sample problems},
author = {Daniel Rivero and Julián Dorado and Juan R. Rabuñal-Dopico and Marcos Gestal},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-38049110614&doi=10.1007%2f978-3-540-73007-1_12&partnerID=40&md5=b9b32d8e81c8a0a77de0c1b8aa62d788},
doi = {10.1007/978-3-540-73007-1_12},
issn = {03029743},
year = {2007},
date = {2007-01-01},
urldate = {2007-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {4507 LNCS},
pages = {94-101},
publisher = {Springer Verlag},
abstract = {This paper presents a study in which a new technique for automatically developing Artificial Neural Networks (ANNs) by means of Evolutionary Computation (EC) tools is compared with the traditional evolutionary techniques used for ANN development. The technique used here is based on network encoding on graphs and also their performance and evolution. For this comparison, 2 different real-world problems have been solved using various tools, and the results are presented here. According to them, the results obtained with this technique can beat those obtained with other ANN development tools. © Springer-Verlag Berlin Heidelberg 2007.},
note = {cited By 0},
keywords = {Computer aided software engineering; Evolutionary algorithms; Graph theory; Problem solving; Real time systems, Development tools; Network encoding, Neural networks},
pubstate = {published},
tppubtype = {article}
}
This paper presents a study in which a new technique for automatically developing Artificial Neural Networks (ANNs) by means of Evolutionary Computation (EC) tools is compared with the traditional evolutionary techniques used for ANN development. The technique used here is based on network encoding on graphs and also their performance and evolution. For this comparison, 2 different real-world problems have been solved using various tools, and the results are presented here. According to them, the results obtained with this technique can beat those obtained with other ANN development tools. © Springer-Verlag Berlin Heidelberg 2007.
Rivero, Daniel; Rabuñal-Dopico, Juan R.; Dorado, Julián; Pazos, Alejandro
Automatic design of ANNs by means of GP for data mining tasks: Iris flower classification problem Journal Article
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4431 LNCS, no. PART 1, pp. 276-285, 2007, ISSN: 03029743, (cited By 11).
Abstract | Links | BibTeX | Etiquetas: Automatic design; Iris flower, Classification (of information); Data mining; Genetic programming; Problem solving, Neural networks
@article{Rivero2007276,
title = {Automatic design of ANNs by means of GP for data mining tasks: Iris flower classification problem},
author = {Daniel Rivero and Juan R. Rabuñal-Dopico and Julián Dorado and Alejandro Pazos},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-38049057122&doi=10.1007%2f978-3-540-71618-1_31&partnerID=40&md5=5d24b505d810e5442f35a3058b0a7d81},
doi = {10.1007/978-3-540-71618-1_31},
issn = {03029743},
year = {2007},
date = {2007-01-01},
urldate = {2007-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {4431 LNCS},
number = {PART 1},
pages = {276-285},
publisher = {Springer Verlag},
abstract = {This paper describes a new technique for automatically developing Artificial Neural Networks (ANNs) by means of an Evolutionary Computation (EC) tool, called Genetic Programming (GP). This paper also describes a practical application in the field of Data Mining. This application is the Iris flower classification problem. This problem has already been extensively studied with other techniques, and therefore this allows the comparison with other tools. Results show how this technique improves the results obtained with other techniques. Moreover, the obtained networks are simpler than the existing ones, with a lower number of hidden neurons and connections, and the additional advantage that there has been a discrimination of the input variables. As it is explained in the text, this variable discrimination gives new knowledge to the problem, since now it is possible to know which variables are important to achieve good results. © Springer-Verlag Berlin Heidelberg 2007.},
note = {cited By 11},
keywords = {Automatic design; Iris flower, Classification (of information); Data mining; Genetic programming; Problem solving, Neural networks},
pubstate = {published},
tppubtype = {article}
}
This paper describes a new technique for automatically developing Artificial Neural Networks (ANNs) by means of an Evolutionary Computation (EC) tool, called Genetic Programming (GP). This paper also describes a practical application in the field of Data Mining. This application is the Iris flower classification problem. This problem has already been extensively studied with other techniques, and therefore this allows the comparison with other tools. Results show how this technique improves the results obtained with other techniques. Moreover, the obtained networks are simpler than the existing ones, with a lower number of hidden neurons and connections, and the additional advantage that there has been a discrimination of the input variables. As it is explained in the text, this variable discrimination gives new knowledge to the problem, since now it is possible to know which variables are important to achieve good results. © Springer-Verlag Berlin Heidelberg 2007.
2006
Gestal, Marcos; Rabuñal-Dopico, Juan R.; Dorado, Julián; Pereira, Javier
2006, ISBN: 0889866104; 9780889866102, (cited By 0).
Abstract | Links | BibTeX | Etiquetas: Artificial intelligence; Backpropagation; Computer programming; Electric fault location; Genetic algorithms; Genetic programming; Learning algorithms; Learning systems; Recurrent neural networks; Soft computing; Trees (mathematics), Artificial neural networks; Expression trees; Generalisation; Generalisation capabilities; Image identifications; Recurrent artificial neural networks; Recurrent networks; Rule extraction; Rule extractions; Series prediction; Temporal evolutions, Neural networks
@conference{Gestal2006323,
title = {Description of RANNs and their generalisation capabilities by means of rule extraction by genetic programming},
author = {Marcos Gestal and Juan R. Rabuñal-Dopico and Julián Dorado and Javier Pereira},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-56149111314&partnerID=40&md5=739d0f3a9cbdfb7842154980eb6b540a},
isbn = {0889866104; 9780889866102},
year = {2006},
date = {2006-01-01},
urldate = {2006-01-01},
journal = {Proceedings of the 10th IASTED International Conference on Artificial Intelligence and Soft Computing, ASC 2006},
pages = {323-328},
abstract = {Artificial Neural Networks have achieved satisfactory results in different fields such as example classification or image identification. Real-world processes usually have a temporal evolution, and they are the type of processes where Recurrent Networks have special success. Nevertheless they are still reluctantly used, mainly due to the fact that they do not adequately justify their response. But, if ANNs offer good results, why giving them up? Suffice it to find a method that might search an explanation to the outputs that the ANN provides. This work presents a technique, totally independent from ANN architecture and the learning algorithm used, which makes possible the justification of the ANN outputs by means of expression trees.},
note = {cited By 0},
keywords = {Artificial intelligence; Backpropagation; Computer programming; Electric fault location; Genetic algorithms; Genetic programming; Learning algorithms; Learning systems; Recurrent neural networks; Soft computing; Trees (mathematics), Artificial neural networks; Expression trees; Generalisation; Generalisation capabilities; Image identifications; Recurrent artificial neural networks; Recurrent networks; Rule extraction; Rule extractions; Series prediction; Temporal evolutions, Neural networks},
pubstate = {published},
tppubtype = {conference}
}
Artificial Neural Networks have achieved satisfactory results in different fields such as example classification or image identification. Real-world processes usually have a temporal evolution, and they are the type of processes where Recurrent Networks have special success. Nevertheless they are still reluctantly used, mainly due to the fact that they do not adequately justify their response. But, if ANNs offer good results, why giving them up? Suffice it to find a method that might search an explanation to the outputs that the ANN provides. This work presents a technique, totally independent from ANN architecture and the learning algorithm used, which makes possible the justification of the ANN outputs by means of expression trees.
CITEEC
Centro de Innovación Tecnolóxica en Edificación e Enxeñería Civil
Campus de Elviña S/N - 15008 - A Coruña
Contáctanos 
