Riccardo Taormina

About Riccardo Taormina

Riccardo Taormina, With an exceptional h-index of 20 and a recent h-index of 19 (since 2020), a distinguished researcher at Technische Universiteit Delft, specializes in the field of Digital Water, Artificial Intelligence, Machine Learning, Cyber-Physical Security of Water Infrastructure.

His recent articles reflect a diverse array of research interests and contributions to the field:

Conformal Prediction Intervals For Water Demand Forecasting

The Potential of Generative AI for the Urban Water Sector

Towards Fully Distributed Rainfall-Runoff Modelling with Graph Neural Networks

Multi-scale hydraulic-based graph neural networks: generalizing spatial flood mapping to irregular meshes and time-varying boundary condition

Operational low-flow forecasting using Long Short-Term Memory networks

Detecting Floating Macroplastic Litter with Semi-Supervised Deep Learning

Macrolitter budget and spatial distribution in a groyne field along the Waal river

Vegetation Response to Climatic Variability: Implications for Root Zone Storage and Streamflow Predictions

Riccardo Taormina Information

University

Position

Assistant Professor in Urban Water Infrastructure (Tenure Track)

Citations(all)

2571

Citations(since 2020)

1838

Cited By

1423

hIndex(all)

20

hIndex(since 2020)

19

i10Index(all)

32

i10Index(since 2020)

30

Email

University Profile Page

Google Scholar

Riccardo Taormina Skills & Research Interests

Digital Water

Artificial Intelligence

Machine Learning

Cyber-Physical Security of Water Infrastructure

Top articles of Riccardo Taormina

Conformal Prediction Intervals For Water Demand Forecasting

2024/3/7

Riccardo Taormina
Riccardo Taormina

H-Index: 13

The Potential of Generative AI for the Urban Water Sector

2024/3/7

Riccardo Taormina
Riccardo Taormina

H-Index: 13

Towards Fully Distributed Rainfall-Runoff Modelling with Graph Neural Networks

2024/3/7

Multi-scale hydraulic-based graph neural networks: generalizing spatial flood mapping to irregular meshes and time-varying boundary condition

2024/3/7

Elvin Isufi
Elvin Isufi

H-Index: 14

Riccardo Taormina
Riccardo Taormina

H-Index: 13

Operational low-flow forecasting using Long Short-Term Memory networks

2024/3/7

Detecting Floating Macroplastic Litter with Semi-Supervised Deep Learning

2024/3/7

Riccardo Taormina
Riccardo Taormina

H-Index: 13

Macrolitter budget and spatial distribution in a groyne field along the Waal river

Marine Pollution Bulletin

2024/3/1

Vegetation Response to Climatic Variability: Implications for Root Zone Storage and Streamflow Predictions

EGUsphere

2024/2/26

Riccardo Taormina
Riccardo Taormina

H-Index: 13

Markus Hrachowitz
Markus Hrachowitz

H-Index: 35

Operational low-flow forecasting using LSTMs

Frontiers in Water

2024/1/17

Synthetic Generation of Extra-Tropical Cyclones' fields with Generative Adversarial Networks

EGU General Assembly Conference Abstracts

2023/5

The potential of deep learning for satellite rainfall detection over data-scarce regions, the west African savanna

Remote Sensing

2023/4/3

Generalizing rapid flood predictions to unseen urban catchments with conditional generative adversarial networks

Journal of Hydrology

2023/3/1

Riccardo Taormina
Riccardo Taormina

H-Index: 13

Deep learning for detecting macroplastic litter in water bodies: A review

2023/3/1

Paul Vriend
Paul Vriend

H-Index: 3

Riccardo Taormina
Riccardo Taormina

H-Index: 13

Advancing deep learning-based detection of floating litter using a novel open dataset

Frontiers in Water

2023/12/6

Riccardo Taormina
Riccardo Taormina

H-Index: 13

Leveraging transfer learning in LSTM neural networks for data-efficient burst detection in water distribution systems

Water Resources Management

2023/12

Riccardo Taormina
Riccardo Taormina

H-Index: 13

Rapid spatio-temporal flood modelling via hydraulics-based graph neural networks

Hydrology and Earth System Sciences

2023/11/30

Elvin Isufi
Elvin Isufi

H-Index: 14

Riccardo Taormina
Riccardo Taormina

H-Index: 13

Assessing the performances and transferability of graph neural network metamodels for water distribution systems

Journal of Hydroinformatics

2023/11/1

RainRunner: A Deep Learning satellite rainfall retrieval model for West Africa

2023/6/30

Predicting streamflow with LSTM networks using global datasets

Frontiers in Water

2023/6/5

The Role of Water Vapor Observations in Satellite Rainfall Detection Highlighted by a Deep Learning Approach

Atmosphere

2023/6/2

See List of Professors in Riccardo Taormina University(Technische Universiteit Delft)

Co-Authors

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