| Machine learning algorithms for the forecasting of wastewater quality indicators F Granata, S Papirio, G Esposito, R Gargano, G De Marinis Water 9 (2), 105, 2017 | 228 | 2017 |
| Support vector regression for rainfall-runoff modeling in urban drainage: A comparison with the EPA’s storm water management model F Granata, R Gargano, G De Marinis Water 8 (3), 69, 2016 | 186 | 2016 |
| Battle of the water networks II A Marchi, E Salomons, A Ostfeld, Z Kapelan, AR Simpson, AC Zecchin, ... Journal of water resources planning and management 140 (7), 04014009, 2014 | 167 | 2014 |
| Hydraulics of circular drop manholes F Granata, G de Marinis, R Gargano, WH Hager Journal of Irrigation and Drainage Engineering 137 (2), 102-111, 2011 | 99 | 2011 |
| Artificial intelligence based approaches to evaluate actual evapotranspiration in wetlands F Granata, R Gargano, G de Marinis Science of The Total Environment 703, 135653, 2020 | 92 | 2020 |
| Stacked machine learning algorithms and bidirectional long short-term memory networks for multi-step ahead streamflow forecasting: A comparative study F Granata, F Di Nunno, G de Marinis Journal of Hydrology 613, 128431, 2022 | 90 | 2022 |
| Hydraulic transients in viscoelastic branched pipelines S Evangelista, A Leopardi, R Pignatelli, G de Marinis Journal of Hydraulic Engineering 141 (8), 04015016, 2015 | 64 | 2015 |
| Peak residential water demand C Tricarico, G De Marinis, R Gargano, A Leopardi Proceedings of the Institution of Civil Engineers-Water Management 160 (2 …, 2007 | 61 | 2007 |
| Potential of a Quorum Quenching Bacteria Isolate Ochrobactrum intermedium D-2 Against Soft Rot Pathogen Pectobacterium carotovorum subsp. carotovorum X Fan, T Ye, Q Li, P Bhatt, L Zhang, S Chen Frontiers in microbiology 11, 898, 2020 | 55 | 2020 |
| Machine learning models for spring discharge forecasting F Granata, M Saroli, G de Marinis, R Gargano Geofluids 2018 (1), 8328167, 2018 | 53 | 2018 |
| Probabilistic models for the peak residential water demand R Gargano, C Tricarico, F Granata, S Santopietro, G De Marinis Water 9 (6), 417, 2017 | 48 | 2017 |
| Air-water flows in circular drop manholes F Granata, G de Marinis, R Gargano Urban Water Journal 12 (6), 477-487, 2015 | 45 | 2015 |
| Prediction of spring flows using nonlinear autoregressive exogenous (NARX) neural network models F Di Nunno, F Granata, R Gargano, G de Marinis Environmental Monitoring and Assessment 193 (6), 350, 2021 | 44 | 2021 |
| Forecasting of extreme storm tide events using NARX neural network-based models F Di Nunno, F Granata, R Gargano, G de Marinis Atmosphere 12 (4), 512, 2021 | 44 | 2021 |
| Tide prediction in the Venice Lagoon using nonlinear autoregressive exogenous (NARX) neural network F Di Nunno, G de Marinis, R Gargano, F Granata Water 13 (9), 1173, 2021 | 40 | 2021 |
| River flow rate prediction in the Des Moines watershed (Iowa, USA): A machine learning approach A Elbeltagi, F Di Nunno, NL Kushwaha, G De Marinis, F Granata Stochastic Environmental Research and Risk Assessment 36 (11), 3835-3855, 2022 | 39 | 2022 |
| Flow-improving elements in circular drop manholes F Granata, G de Marinis, R Gargano Journal of Hydraulic Research 52 (3), 347-355, 2014 | 39 | 2014 |
| Machine learning methods for wastewater hydraulics F Granata, G de Marinis Flow Measurement and Instrumentation 57, 1-9, 2017 | 38 | 2017 |
| A stochastic approach for the water demand of residential end users R Gargano, F Di Palma, G de Marinis, F Granata, R Greco Urban Water Journal 13 (6), 569-582, 2016 | 36 | 2016 |
| Creep functions for transients in HDPE pipes C Apollonio, DIC Covas, G de Marinis, A Leopardi, HM Ramos Urban Water Journal 11 (2), 160-166, 2014 | 36 | 2014 |
