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Your search keyword '"Peláez-Rodríguez, C."' showing total 15 results
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15 results on '"Peláez-Rodríguez, C."'

1. Identifying Key Drivers of Heatwaves: A Novel Spatio-Temporal Framework for Extreme Event Detection

Author

Pérez-Aracil, J., Peláez-Rodríguez, C., McAdam, Ronan, Squintu, Antonello, Marina, Cosmin M., Lorente-Ramos, Eugenio, Luther, Niklas, Torralba, Veronica, Scoccimarro, Enrico, Cavicchia, Leone, Giuliani, Matteo, Zorita, Eduardo, Hansen, Felicitas, Barriopedro, David, Garcia-Herrera, Ricardo, Gutiérrez, Pedro A., Luterbacher, Jürg, Xoplaki, Elena, Castelletti, Andrea, and Salcedo-Sanz, S.

Subjects
Physics - Atmospheric and Oceanic Physics, Computer Science - Artificial Intelligence
Abstract

Heatwaves (HWs) are extreme atmospheric events that produce significant societal and environmental impacts. Predicting these extreme events remains challenging, as their complex interactions with large-scale atmospheric and climatic variables are difficult to capture with traditional statistical and dynamical models. This work presents a general method for driver identification in extreme climate events. A novel framework (STCO-FS) is proposed to identify key immediate (short-term) HW drivers by combining clustering algorithms with an ensemble evolutionary algorithm. The framework analyzes spatio-temporal data, reduces dimensionality by grouping similar geographical nodes for each variable, and develops driver selection in spatial and temporal domains, identifying the best time lags between predictive variables and HW occurrences. The proposed method has been applied to analyze HWs in the Adda river basin in Italy. The approach effectively identifies significant variables influencing HWs in this region. This research can potentially enhance our understanding of HW drivers and predictability., Comment: 28 pages, 10 figures, 4 tables

Published
2024

2. Prediction of Extreme Wave Heights via a Fuzzy-Based Cascade Ensemble Model

Author

Peláez-Rodríguez, C., Cornejo-Bueno, L., Fister, Dušan, Pérez-Aracil, J., Salcedo-Sanz, S., Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, van Leeuwen, Jan, Series Editor, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Kobsa, Alfred, Series Editor, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Nierstrasz, Oscar, Series Editor, Pandu Rangan, C., Editorial Board Member, Sudan, Madhu, Series Editor, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Vardi, Moshe Y, Series Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Ferrández Vicente, José Manuel, editor, Val Calvo, Mikel, editor, and Adeli, Hojjat, editor

Published
2024
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3. Autoencoder Framework for General Forecasting

Author

Fister, Dušan, Peláez-Rodríguez, C., Cornejo-Bueno, L., Pérez-Aracil, J., Salcedo-Sanz, S., Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, van Leeuwen, Jan, Series Editor, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Kobsa, Alfred, Series Editor, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Nierstrasz, Oscar, Series Editor, Pandu Rangan, C., Editorial Board Member, Sudan, Madhu, Series Editor, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Vardi, Moshe Y, Series Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Ferrández Vicente, José Manuel, editor, Val Calvo, Mikel, editor, and Adeli, Hojjat, editor

Published
2024
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14. Accurate long-term air temperature prediction with Machine Learning models and data reduction techniques

Author

Ingeniería de comunicaciones, Komunikazioen ingeniaritza, Fister, D., Pérez Aracil, J., Peláez Rodríguez, C., Del Ser Lorente, Javier, Salcedo Sanz, S., Ingeniería de comunicaciones, Komunikazioen ingeniaritza, Fister, D., Pérez Aracil, J., Peláez Rodríguez, C., Del Ser Lorente, Javier, and Salcedo Sanz, S.

Abstract

In this paper, three customised Artificial Intelligence (AI) frameworks, considering Deep Learning, Machine Learning (ML) algorithms and data reduction techniques, are proposed for a problem of long-term summer air temperature prediction. Specifically, the prediction of the average air temperature in the first and second August fortnights, using input data from previous months, at two different locations (Paris, France) and (Córdoba, Spain), is considered. The target variable, mainly in the first August fortnight, can contain signals of extreme events such as heatwaves, like the heatwave of 2003, which affected France and the Iberian Peninsula. Three different computational frameworks for air temperature prediction are proposed: a Convolutional Neural Network (CNN), with video-to-image translation, several ML approaches including Lasso regression, Decision Trees and Random Forest, and finally a CNN with pre-processing step using Recurrence Plots, which convert time series into images. Using these frameworks, a very good prediction skill has been obtained in both Paris and Córdoba regions, showing that the proposed approaches can be an excellent option for seasonal climate prediction problems.

Published
2023

15. An iterative neural network approach applied to human-induced force reconstruction using a non-linear electrodynamic shaker.

Author

Peláez-Rodríguez C, Magdaleno Á, García Terán JM, Pérez-Aracil J, Salcedo-Sanz S, and Lorenzana A

Abstract

Human-induced force analysis plays an important role across a wide range of disciplines, including biomechanics, sport engineering, health monitoring or structural engineering. Specifically, this paper focuses on the replication of ground reaction forces (GRF) generated by humans during movement. They can provide critical information about human-mechanics and be used to optimize athletic performance, prevent and rehabilitate injuries and assess structural vibrations in engineering applications. It is presented an experimental approach that uses an electrodynamic shaker (APS 400) to replicate GRFs generated by humans during movement, with a high degree of accuracy. Successful force reconstruction implies a high fidelity in signal reproduction with the electrodynamic shaker, which leads to an inverse problem, where a reference signal must be replicated with a nonlinear and non-invertible system. The solution presented in this paper relies on the development of an iterative neural network and an inversion-free approach, which aims to generate the most effective drive signal that minimizes the error between the experimental force signal exerted by the shaker and the reference. After the optimization process, the weights of the neural network are updated to make the shaker behave as desired, achieving excellent results in both time and frequency domains., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Author(s).)

Published
2024
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