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26 results on '"Salcedo‐Sanz, Sancho"'

1. AI for Extreme Event Modeling and Understanding: Methodologies and Challenges

Author

Camps-Valls, Gustau, Fernández-Torres, Miguel-Ángel, Cohrs, Kai-Hendrik, Höhl, Adrian, Castelletti, Andrea, Pacal, Aytac, Robin, Claire, Martinuzzi, Francesco, Papoutsis, Ioannis, Prapas, Ioannis, Pérez-Aracil, Jorge, Weigel, Katja, Gonzalez-Calabuig, Maria, Reichstein, Markus, Rabel, Martin, Giuliani, Matteo, Mahecha, Miguel, Popescu, Oana-Iuliana, Pellicer-Valero, Oscar J., Ouala, Said, Salcedo-Sanz, Sancho, Sippel, Sebastian, Kondylatos, Spyros, Happé, Tamara, and Williams, Tristan

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

In recent years, artificial intelligence (AI) has deeply impacted various fields, including Earth system sciences. Here, AI improved weather forecasting, model emulation, parameter estimation, and the prediction of extreme events. However, the latter comes with specific challenges, such as developing accurate predictors from noisy, heterogeneous and limited annotated data. This paper reviews how AI is being used to analyze extreme events (like floods, droughts, wildfires and heatwaves), highlighting the importance of creating accurate, transparent, and reliable AI models. We discuss the hurdles of dealing with limited data, integrating information in real-time, deploying models, and making them understandable, all crucial for gaining the trust of stakeholders and meeting regulatory needs. We provide an overview of how AI can help identify and explain extreme events more effectively, improving disaster response and communication. We emphasize the need for collaboration across different fields to create AI solutions that are practical, understandable, and trustworthy for analyzing and predicting extreme events. Such collaborative efforts aim to enhance disaster readiness and disaster risk reduction.

Published
2024

2. Multivariate-Autoencoder Flow-Analogue Method for Heat Waves Reconstruction

Author

Marina, Cosmin M., Lorente-Ramos, Eugenio, Ayllón-Gavilán, Rafael, Gutiérrez, Pedro Antonio, Pérez-Aracil, Jorge, Salcedo-Sanz, Sancho, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Alonso-Betanzos, Amparo, editor, Guijarro-Berdiñas, Bertha, editor, Bolón-Canedo, Verónica, editor, Hernández-Pereira, Elena, editor, Fontenla-Romero, Oscar, editor, Camacho, David, editor, Rabuñal, Juan Ramón, editor, Ojeda-Aciego, Manuel, editor, Medina, Jesús, editor, Riquelme, José C., editor, and Troncoso, Alicia, editor

Published
2024
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3. Machine Learning as Applied to Shape Parameterization of Submerged Arch Structures

Author

LLamosas-Mayca, Waldemar Hugo, Lorente-Ramos, Eugenio, Pérez-Aracil, Jorge, Hernández-Díaz, Alejandro M., García-Román, Manuel Damián, Salcedo-Sanz, Sancho, 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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4. Medium- and Long-Term Wind Speed Prediction Using the Multi-task Learning Paradigm

Author

Gómez-Orellana, Antonio M., Vargas, Víctor M., Guijo-Rubio, David, Pérez-Aracil, Jorge, Gutiérrez, Pedro A., Salcedo-Sanz, Sancho, Hervás-Martínez, César, 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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5. Data Augmentation Techniques for Extreme Wind Prediction Improvement

Author

Vega-Bayo, Marta, Gómez-Orellana, Antonio Manuel, Yun, Víctor Manuel Vargas, Guijo-Rubio, David, Cornejo-Bueno, Laura, Pérez-Aracil, Jorge, Salcedo-Sanz, Sancho, 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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6. Energy Flux Prediction Using an Ordinal Soft Labelling Strategy

Author

Gómez-Orellana, Antonio M., Vargas, Víctor M., Gutiérrez, Pedro A., Pérez-Aracil, Jorge, Salcedo-Sanz, Sancho, Hervás-Martínez, César, Guijo-Rubio, David, 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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7. Machine Learning Health Estimation for Lithium-Ion Batteries Under Varied Conditions

Author

Leite, Gabriel M. C., Pérez-Aracil, Jorge, Marcelino, Carolina Gil, García-Gutiérrez, Gabriel, Prodanovic, Milan, García-Quismondo, Enrique, Pinilla, Sergio, Palma, Jesús, Jiménez-Fernández, Silvia, Salcedo-Sanz, Sancho, 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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26. NarmViz: A novel method for visualization of time series numerical association rules for smart agriculture.

Author

Fister, Iztok, Podgorelec, Vili, Salcedo‐Sanz, Sancho, and Holzinger, Andreas

Subjects
ASSOCIATION rule mining, DATA visualization, ARTIFICIAL intelligence, DATA mining, TIME series analysis
Abstract

Numerical association rule mining (NARM) is a popular method under the umbrella of data mining, focused on finding relationships between attributes in transaction databases. Numerical association rules for time series are a new paradigm that extends the applicability of NARM to the domain of time series. Association rule mining algorithms result in numerous rules, the interpretation of which is sometimes not easy for human experts. Therefore, various visualization methods have been developed to improve the explanation results of the rule mining process. This article is a novel contribution to the development of a new visualization method capable of presenting the association rules for time series developed according to the principles of explainable artificial intelligence. The experiments are conducted in the context of smart agriculture (i.e., agricultural time series data), and show the great potential of the proposed visualization method for the future. [ABSTRACT FROM AUTHOR]

Published
2024
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