| Mapping flood susceptibility in mountainous areas on a national scale in China G Zhao, B Pang, Z Xu, J Yue, T Tu Science of the Total Environment 615, 1133-1142, 2018 | 359 | 2018 |
| XGBoost-based method for flash flood risk assessment M Ma, G Zhao, B He, Q Li, H Dong, S Wang, Z Wang Journal of Hydrology 598, 126382, 2021 | 284 | 2021 |
| Assessment of urban flood susceptibility using semi-supervised machine learning model G Zhao, B Pang, Z Xu, D Peng, L Xu Science of the Total Environment 659, 940-949, 2019 | 246 | 2019 |
| Urban flood susceptibility assessment based on convolutional neural networks G Zhao, B Pang, Z Xu, D Peng, D Zuo Journal of Hydrology 590, 125235, 2020 | 119 | 2020 |
| Statistical downscaling of temperature with the random forest model B Pang, J Yue, G Zhao, Z Xu Advances in Meteorology 2017 (1), 7265178, 2017 | 93 | 2017 |
| Flash flood risk analysis based on machine learning techniques in the Yunnan Province, China M Ma, C Liu, G Zhao, H Xie, P Jia, D Wang, H Wang, Y Hong Remote Sensing 11 (2), 170, 2019 | 65 | 2019 |
| An enhanced inundation method for urban flood hazard mapping at the large catchment scale G Zhao, Z Xu, B Pang, T Tu, L Xu, L Du Journal of Hydrology 571, 873-882, 2019 | 59 | 2019 |
| Improving urban flood susceptibility mapping using transfer learning G Zhao, B Pang, Z Xu, L Cui, J Wang, D Zuo, D Peng Journal of Hydrology 602, 126777, 2021 | 58 | 2021 |
| Impact of urbanization on rainfall-runoff processes: case study in the Liangshui River Basin in Beijing, China Z Xu, G Zhao Proceedings of the International Association of Hydrological Sciences 373, 7-12, 2016 | 54 | 2016 |
| A hybrid machine learning framework for real-time water level prediction in high sediment load reaches G Zhao, B Pang, Z Xu, L Xu Journal of Hydrology 581, 124422, 2020 | 34 | 2020 |
| Spatial and temporal variations of precipitation during 1979–2015 in Jinan City, China X Chang, Z Xu, G Zhao, T Cheng, S Song Journal of Water and Climate Change 9 (3), 540-554, 2018 | 30 | 2018 |
| Flood susceptibility assessment with random sampling strategy in ensemble learning (RF and XGBoost) H Ren, B Pang, P Bai, G Zhao, S Liu, Y Liu, M Li Remote Sensing 16 (2), 320, 2024 | 28 | 2024 |
| The impact of dams on design floods in the conterminous US G Zhao, P Bates, J Neal Water Resources Research 56 (3), e2019WR025380, 2020 | 26 | 2020 |
| Large-scale flash flood warning in China using deep learning G Zhao, R Liu, M Yang, T Tu, M Ma, Y Hong, X Wang Journal of Hydrology 604, 127222, 2022 | 25 | 2022 |
| Design flood estimation for global river networks based on machine learning models G Zhao, P Bates, J Neal, B Pang Hydrology and Earth System Sciences 25 (11), 5981-5999, 2021 | 24 | 2021 |
| 基于 SWMM 模型的城市雨洪模拟与 LID 效果评价——以北京市清河流域为例 常晓栋， 徐宗学， 赵刚， 杜龙刚 水力发电学报 35 (11), 84-93, 2016 | 22 | 2016 |
| Sensitivity analysis on SWMM model parameters based on Sobol method X Chang, Z Xu, G Zhao, H Li J. Hydro-Electr. Engineering 37, 59-68, 2018 | 19 | 2018 |
| 快速城市化对产汇流影响的研究: 以凉水河流域为例 赵刚， 史蓉， 庞博， 徐宗学， 杜龙刚， 常晓栋 水力发电学报 35 (5), 55-64, 2016 | 19 | 2016 |
| SWMM 模型在城市暴雨洪水模拟中的参数敏感性分析 史蓉， 庞博， 赵刚， 杜龙刚， 钟一丹， 左萍 北京师范大学学报 (自然科学版), 456-460, 2014 | 17 | 2014 |
| Uncertainty assessment of urban hydrological modelling from a multiple objective perspective B Pang, S Shi, G Zhao, R Shi, D Peng, Z Zhu Water 12 (5), 1393, 2020 | 16 | 2020 |
