Data-driven input variable selection for rainfall–runoff modeling using binary-coded particle swarm optimization and Extreme Learning Machines von Taormina, Riccardo, Chau, Kwok-Wing

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Artificial neural network simulation of hourly groundwater levels in a coastal aquifer system of the Venice lagoon von Taormina, Riccardo, Chau, Kwok-wing, Sethi, Rajandrea

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The Potential of Deep Learning for Satellite Rainfall Detection over Data-Scarce Regions, the West African Savanna von Estébanez-Camarena, Mónica, Taormina, Riccardo, van de Giesen, Nick, ten Veldhuis, Marie-Claire

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Assessing the performances and transferability of graph neural network metamodels for water distribution systems von Kerimov, Bulat, Bentivoglio, Roberto, Garzón, Alexander, Isufi, Elvin, Tscheikner-Gratl, Franz, Steffelbauer, David Bernhard, Taormina, Riccardo

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Deep-Learning Approach to the Detection and Localization of Cyber-Physical Attacks on Water Distribution Systems von Taormina, Riccardo, Galelli, Stefano

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Advancing deep learning-based detection of floating litter using a novel open dataset von Jia, Tianlong, Vallendar, Andre Jehan, de Vries, Rinze, Kapelan, Zoran, Taormina, Riccardo

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The Role of Water Vapor Observations in Satellite Rainfall Detection Highlighted by a Deep Learning Approach von Estébanez-Camarena, Mónica, Curzi, Fabio, Taormina, Riccardo, van de Giesen, Nick, ten Veldhuis, Marie-Claire

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Predicting streamflow with LSTM networks using global datasets von Wilbrand, Katharina, Taormina, Riccardo, ten Veldhuis, Marie-Claire, Visser, Martijn, Hrachowitz, Markus, Nuttall, Jonathan, Dahm, Ruben

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Operational low-flow forecasting using LSTMs von Deng, Jing, Couasnon, Anaïs, Dahm, Ruben, Hrachowitz, Markus, van Heeringen, Klaas-Jan, Korving, Hans, Weerts, Albrecht, Taormina, Riccardo

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Neural network river forecasting through baseflow separation and binary-coded swarm optimization von Taormina, Riccardo, Chau, Kwok-Wing, Sivakumar, Bellie

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Towards transferable metamodels for water distribution systems with edge-based graph neural networks von Kerimov, Bulat, Taormina, Riccardo, Tscheikner-Gratl, Franz

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Deep learning methods for flood mapping: a review of existing applications and future research directions von Bentivoglio, Roberto, Isufi, Elvin, Jonkman, Sebastian Nicolaas, Taormina, Riccardo

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A Machine Learning-based framework and open-source software for Non Intrusive Water Monitoring von Gross, Marie-Philine, Taormina, Riccardo, Cominola, Andrea

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ANN-based interval forecasting of streamflow discharges using the LUBE method and MOFIPS von Taormina, Riccardo, Chau, Kwok-Wing

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Deep learning for detecting macroplastic litter in water bodies: A review von Jia, Tianlong, Kapelan, Zoran, de Vries, Rinze, Vriend, Paul, Peereboom, Eric Copius, Okkerman, Imke, Taormina, Riccardo

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Transferable and data efficient metamodeling of storm water system nodal depths using auto-regressive graph neural networks von Garzón, Alexander, Kapelan, Zoran, Langeveld, Jeroen, Taormina, Riccardo

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Characterizing Cyber-Physical Attacks on Water Distribution Systems von Taormina, Riccardo, Galelli, Stefano, Tippenhauer, Nils Ole, Salomons, Elad, Ostfeld, Avi

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A Review of Cybersecurity Incidents in the Water Sector von Hassanzadeh, Amin, Rasekh, Amin, Galelli, Stefano, Aghashahi, Mohsen, Taormina, Riccardo, Ostfeld, Avi, Banks, M. Katherine

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Leveraging Transfer Learning in LSTM Neural Networks for Data-Efficient Burst Detection in Water Distribution Systems von Glynis, Konstantinos, Kapelan, Zoran, Bakker, Martijn, Taormina, Riccardo

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Generalizing rapid flood predictions to unseen urban catchments with conditional generative adversarial networks von do Lago, Cesar A.F., Giacomoni, Marcio H., Bentivoglio, Roberto, Taormina, Riccardo, Gomes, Marcus N., Mendiondo, Eduardo M.

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