| Artificial neural network simulation of hourly groundwater levels in a coastal aquifer system of the Venice lagoon R Taormina, K Chau, R Sethi Engineering Applications of Artificial Intelligence 25 (8), 1670-1676, 2012 | 460 | 2012 |
| Data-driven input variable selection for rainfall–runoff modeling using binary-coded particle swarm optimization and Extreme Learning Machines R Taormina, KW Chau Journal of hydrology 529, 1617-1632, 2015 | 325 | 2015 |
| Neural network river forecasting through baseflow separation and binary-coded swarm optimization R Taormina, KW Chau, B Sivakumar Journal of Hydrology 529, 1788-1797, 2015 | 232 | 2015 |
| ANN-based interval forecasting of streamflow discharges using the LUBE method and MOFIPS R Taormina, KW Chau Engineering Applications of Artificial Intelligence 45, 429-440, 2015 | 224 | 2015 |
| Battle of the attack detection algorithms: Disclosing cyber attacks on water distribution networks R Taormina, S Galelli, NO Tippenhauer, E Salomons, A Ostfeld, ... Journal of Water Resources Planning and Management 144 (8), 04018048, 2018 | 183 | 2018 |
| A review of cybersecurity incidents in the water sector A Hassanzadeh, A Rasekh, S Galelli, M Aghashahi, R Taormina, ... Journal of Environmental Engineering 146 (5), 03120003, 2020 | 173 | 2020 |
| Characterizing cyber-physical attacks on water distribution systems R Taormina, S Galelli, NO Tippenhauer, E Salomons, A Ostfeld Journal of Water Resources Planning and Management 143 (5), 04017009, 2017 | 173 | 2017 |
| Neural network river forecasting with multi-objective fully informed particle swarm optimization R Taormina, K Chau Journal of Hydroinformatics 17 (1), 99-113, 2015 | 134 | 2015 |
| Deep learning methods for flood mapping: a review of existing applications and future research directions R Bentivoglio, E Isufi, SN Jonkman, R Taormina Hydrology and earth system sciences 26 (16), 4345-4378, 2022 | 104 | 2022 |
| Deep-learning approach to the detection and localization of cyber-physical attacks on water distribution systems R Taormina, S Galelli Journal of Water Resources Planning and Management 144 (10), 04018065, 2018 | 98 | 2018 |
| A toolbox for assessing the impacts of cyber-physical attacks on water distribution systems R Taormina, S Galelli, HC Douglas, NO Tippenhauer, E Salomons, ... Environmental modelling & software 112, 46-51, 2019 | 60 | 2019 |
| Identifying (quasi) equally informative subsets in feature selection problems for classification: a max-relevance min-redundancy approach G Karakaya, S Galelli, SD Ahipaşaoğlu, R Taormina IEEE transactions on cybernetics 46 (6), 1424-1437, 2015 | 58 | 2015 |
| Constrained concealment attacks against reconstruction-based anomaly detectors in industrial control systems A Erba, R Taormina, S Galelli, M Pogliani, M Carminati, S Zanero, ... Proceedings of the 36th Annual Computer Security Applications Conference …, 2020 | 51 | 2020 |
| Machine learning‐based surrogate modeling for urban water networks: review and future research directions A Garzón, Z Kapelan, J Langeveld, R Taormina Water Resources Research 58 (5), e2021WR031808, 2022 | 43 | 2022 |
| BattLeDIM: Battle of the leakage detection and isolation methods SG Vrachimis, DG Eliades, R Taormina, A Ostfeld, Z Kapelan, S Liu, ... Proc., 2nd Int. CCWI/WDSA Joint Conf, 1-6, 2020 | 40 | 2020 |
| Beyond image analysis in processing archaeomagnetic geophysical data: case studies of chamber tombs with dromos S Piro, L Sambuelli, A Godio, R Taormina Near Surface Geophysics 5 (6), 405-414, 2007 | 38 | 2007 |
| Battle of the leakage detection and isolation methods SG Vrachimis, DG Eliades, R Taormina, Z Kapelan, A Ostfeld, S Liu, ... Journal of Water Resources Planning and Management 148 (12), 04022068, 2022 | 28 | 2022 |
| Real-time evasion attacks with physical constraints on deep learning-based anomaly detectors in industrial control systems A Erba, R Taormina, S Galelli, M Pogliani, M Carminati, S Zanero, ... arXiv preprint arXiv:1907.07487, 2019 | 23 | 2019 |
| An information theoretic approach to select alternate subsets of predictors for data-driven hydrological models R Taormina, S Galelli, G Karakaya, SD Ahipasaoglu Journal of hydrology 542, 18-34, 2016 | 23 | 2016 |
| Deep learning for detecting macroplastic litter in water bodies: A review T Jia, Z Kapelan, R de Vries, P Vriend, EC Peereboom, I Okkerman, ... Water Research 231, 119632, 2023 | 22 | 2023 |
Stefano GalelliAssociate Professor, Cornell UniversityΗ διεύθυνση ηλεκτρονικού ταχυδρομείου έχει επαληθευτεί στον τομέα cornell.edu
