| Forecasting river water temperature time series using a wavelet–neural network hybrid modelling approach R Graf, S Zhu, B Sivakumar Journal of Hydrology 578, 124115, 2019 | 177 | 2019 |
| Forecasting of water level in multiple temperate lakes using machine learning models S Zhu, B Hrnjica, M Ptak, A Choiński, B Sivakumar Journal of Hydrology 585, 124819, 2020 | 148 | 2020 |
| Mercury transport and fate models in aquatic systems: A review and synthesis S Zhu, Z Zhang, D Žagar Science of the Total environment 639, 538-549, 2018 | 112 | 2018 |
| Modelling of daily lake surface water temperature from air temperature: Extremely randomized trees (ERT) versus Air2Water, MARS, M5Tree, RF and MLPNN S Heddam, M Ptak, S Zhu Journal of Hydrology 588, 125130, 2020 | 109 | 2020 |
| Modelling daily water temperature from air temperature for the Missouri River S Zhu, EK Nyarko, M Hadzima-Nyarko PeerJ 6, e4894, 2018 | 106 | 2018 |
| Modeling daily water temperature for rivers: comparison between adaptive neuro-fuzzy inference systems and artificial neural networks models S Zhu, S Heddam, EK Nyarko, M Hadzima-Nyarko, S Piccolroaz, S Wu Environmental Science and Pollution Research 26, 402-420, 2019 | 105 | 2019 |
| Energy price prediction using data-driven models: A decade review H Lu, X Ma, M Ma, S Zhu Computer Science Review 39, 100356, 2021 | 92 | 2021 |
| Prediction of dissolved oxygen in urban rivers at the Three Gorges Reservoir, China: extreme learning machines (ELM) versus artificial neural network (ANN) S Zhu, S Heddam Water Quality Research Journal 55 (1), 106-118, 2020 | 91 | 2020 |
| Forecasting surface water temperature in lakes: A comparison of approaches S Zhu, M Ptak, ZM Yaseen, J Dai, B Sivakumar Journal of Hydrology 585, 124809, 2020 | 88 | 2020 |
| Extreme learning machine-based prediction of daily water temperature for rivers S Zhu, S Heddam, S Wu, J Dai, B Jia Environmental Earth Sciences 78, 1-17, 2019 | 75 | 2019 |
| River/stream water temperature forecasting using artificial intelligence models: a systematic review S Zhu, AP Piotrowski Acta Geophysica 68, 1433-1442, 2020 | 70 | 2020 |
| Impacts of a large river-to-lake water diversion project on lacustrine phytoplankton communities J Dai, S Wu, X Wu, X Lv, B Sivakumar, F Wang, Y Zhang, Q Yang, A Gao, ... Journal of Hydrology 587, 124938, 2020 | 67 | 2020 |
| Lake water-level fluctuation forecasting using machine learning models: a systematic review S Zhu, H Lu, M Ptak, J Dai, Q Ji Environmental Science and Pollution Research 27 (36), 44807-44819, 2020 | 65 | 2020 |
| Machine learning approaches for estimation of compressive strength of concrete M Hadzima-Nyarko, EK Nyarko, H Lu, S Zhu The European Physical Journal Plus 135 (8), 682, 2020 | 58 | 2020 |
| A stacked machine learning model for multi-step ahead prediction of lake surface water temperature F Di Nunno, S Zhu, M Ptak, M Sojka, F Granata Science of The Total Environment 890, 164323, 2023 | 52 | 2023 |
| Two hybrid data-driven models for modeling water-air temperature relationship in rivers S Zhu, M Hadzima-Nyarko, A Gao, F Wang, J Wu, S Wu Environmental Science and Pollution Research 26, 12622-12630, 2019 | 49 | 2019 |
| Warming of lowland Polish lakes under future climate change scenarios and consequences for ice cover and mixing dynamics S Piccolroaz, S Zhu, M Ptak, M Sojka, X Du Journal of Hydrology: Regional Studies 34, 100780, 2021 | 48 | 2021 |
| Assessing the performance of a suite of machine learning models for daily river water temperature prediction S Zhu, EK Nyarko, M Hadzima-Nyarko, S Heddam, S Wu PeerJ 7, e7065, 2019 | 44 | 2019 |
| Identification of EDI trend using Mann-Kendall and Şen-innovative trend methods (Uttarakhand, India) A Malik, A Kumar, QB Pham, S Zhu, NTT Linh, DQ Tri Arabian Journal of Geosciences 13, 1-15, 2020 | 38 | 2020 |
| Comparing artificial intelligence techniques for chlorophyll-a prediction in US lakes W Luo, S Zhu, S Wu, J Dai Environmental Science and Pollution Research 26, 30524-30532, 2019 | 37 | 2019 |
