| Can artificial intelligence and data-driven machine learning models match or even replace process-driven hydrologic models for streamflow simulation?: A case study of four … T Kim, T Yang, S Gao, L Zhang, Z Ding, X Wen, JJ Gourley, Y Hong Journal of Hydrology 598, 126423, 2021 | 115 | 2021 |
| A large-scale comparison of Artificial Intelligence and Data Mining (AI&DM) techniques in simulating reservoir releases over the Upper Colorado Region T Yang, L Zhang, T Kim, Y Hong, D Zhang, Q Peng Journal of Hydrology 602, 126723, 2021 | 49 | 2021 |
| Evaluation of Subseasonal-to-Seasonal (S2S) precipitation forecast from the North American Multi-Model ensemble phase II (NMME-2) over the contiguous US L Zhang, T Kim, T Yang, Y Hong, Q Zhu Journal of Hydrology 603, 127058, 2021 | 41 | 2021 |
| A field investigation on rill development and flow hydrodynamics under different upslope inflow and slope gradient conditions P Tian, C Pan, X Xu, T Wu, T Yang, L Zhang Hydrology Research 51 (5), 1201-1220, 2020 | 33 | 2020 |
| Near real-time hurricane rainfall forecasting using convolutional neural network models with Integrated Multi-satellitE Retrievals for GPM (IMERG) product T Kim, T Yang, L Zhang, Y Hong Atmospheric Research 270, 106037, 2022 | 29 | 2022 |
| Improving Subseasonal-to-Seasonal forecasts in predicting the occurrence of extreme precipitation events over the contiguous US using machine learning models L Zhang, T Yang, S Gao, Y Hong, Q Zhang, X Wen, C Cheng Atmospheric Research 281, 106502, 2023 | 15 | 2023 |
| Investigation of hydrometeorological influences on reservoir releases using explainable machine learning methods M Fan, L Zhang, S Liu, T Yang, D Lu Frontiers in Water 5, 1112970, 2023 | 10 | 2023 |
| Identifying hydrometeorological factors influencing reservoir releases using machine learning methods M Fan, L Zhang, S Liu, T Yang, D Lu 2022 IEEE International Conference on Data Mining Workshops (ICDMW), 1102-1110, 2022 | 6 | 2022 |
| Effects assessment of water environment treatment projects based on SWMM-EFDC coupling simulation in Xinfeng River Basin C Yan, XIA Rui, W Lu, SUN Mingdong, Z Lujun, MA Shuqin, JIA Ruining, ... Journal of Environmental Engineering Technology 11 (4), 777-788, 2021 | 6* | 2021 |
| Adapting subseasonal-to-seasonal (S2S) precipitation forecast at watersheds for hydrologic ensemble streamflow forecasting with a machine learning-based post-processing approach L Zhang, S Gao, T Yang Journal of Hydrology 631, 130643, 2024 | 4 | 2024 |
| A Deep State Space Model for Rainfall-Runoff Simulations Y Wang, L Zhang, A Yu, NB Erichson, T Yang arXiv preprint arXiv:2501.14980, 2025 | | 2025 |
| Enhancing Subseasonal Precipitation Forecasts through a Hybrid Deep Learning and Statistical Approach L Zhang, T Yang, KM Dresback, C Szpilka, RL Kolar AGU24, 2024 | | 2024 |
| Using Long Short-term Memory (LSTM) to merge precipitation data over mountainous area in Sierra Nevada Y Wang, L Zhang arXiv preprint arXiv:2404.10135, 2024 | | 2024 |
| A Framework of Subseasonal-to-seasonal (S2S) Ensemble Hydrological Forecasting at a Watershed Scale Using Dynamical Precipitation Forecast: Forecast Verification, Adaptation … L Zhang | | 2023 |
| Investigating the Source of the Predictability of Streamflow at a Subseasonal-to-Seasonal (S2S) Timescale Using a Combined Ensemble Streamflow Prediction (ESP) and Reversed ESP … A Adhikari, L Zhang, T Yang AGU Fall Meeting Abstracts 2023 (1767), H23N-1767, 2023 | | 2023 |
| Adapting Subseasonal-to-Seasonal (S2S) precipitation forecasts at a watershed scale for hydrologic Ensemble Streamflow Prediction (ESP) using a Machine Learning-based post … L Zhang, S Gao, T Yang AGU Fall Meeting Abstracts 2023, A14E-04, 2023 | | 2023 |
| A Python-based script for coupling SWMM and EFDC models L Zhang, Z Yang, T Yang, M Sun, Y Chen, L Wang AGU Fall Meeting Abstracts 2019, H53K-1917, 2019 | | 2019 |
| Quantifying the Impact of Snow Water Equivalent on Spring-Summer Water Supply Forecasts in the Western US Using Gradient Boosting Regressor H Yue, Y Wang, L Zhang, T Yang AGU24, 0 | | |
| A Mass Conservation Relaxed (MCR) LSTM Model for Streamflow Simulation: A Large-Scale Verification over 531 Watersheds across CONUS T Yang, Y Wang, L Zhang, NB Erichson AGU24, 0 | | |
