| 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 | 203 | 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 | 171 | 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 | 148 | 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 | 95 | 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 | 75 | 2020 |
| Hydraulic transients in viscoelastic branched pipelines S Evangelista, A Leopardi, R Pignatelli, G de Marinis Journal of Hydraulic Engineering 141 (8), 04015016, 2015 | 61 | 2015 |
| 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 | 60 | 2022 |
| 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 | 57 | 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 | 48 | 2020 |
| Machine learning models for spring discharge forecasting F Granata, M Saroli, G de Marinis, R Gargano Geofluids 2018, 1-13, 2018 | 47 | 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 | 45 | 2017 |
| Air-water flows in circular drop manholes F Granata, G de Marinis, R Gargano Urban Water Journal 12 (6), 477-487, 2015 | 43 | 2015 |
| 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 | 41 | 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 | 38 | 2021 |
| Machine learning methods for wastewater hydraulics F Granata, G de Marinis Flow Measurement and Instrumentation 57, 1-9, 2017 | 38 | 2017 |
| Flow-improving elements in circular drop manholes F Granata, G de Marinis, R Gargano Journal of Hydraulic Research 52 (3), 347-355, 2014 | 38 | 2014 |
| 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 | 34 | 2014 |
| Illicit intrusion characterization in sewer systems BK Banik, C Di Cristo, A Leopardi, G de Marinis Urban water journal 14 (4), 416-426, 2017 | 33 | 2017 |
| Economic level of reliability for the rehabilitation of hydraulic networks C Tricarico, R Gargano, Z Kapelan, D Savic, G De Marinis Civil engineering and environmental systems 23 (3), 191-207, 2006 | 33 | 2006 |
| 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 | 32 | 2021 |
