50 citations as recorded by crossref.

1. Exploring the performance and interpretability of hybrid hydrologic model coupling physical mechanisms and deep learning M. He et al. https://doi.org/10.1016/j.jhydrol.2024.132440
2. Improving flood forecast accuracy based on explainable convolutional neural network by Grad-CAM method X. Xiang et al. https://doi.org/10.1016/j.jhydrol.2024.131867
3. Machine Learning-Based Reconstruction and Prediction of Groundwater Time Series in the Allertal, Germany T. Tran et al. https://doi.org/10.3390/w17030433
4. Assessment and Modeling of Green Roof System Hydrological Effectiveness in Runoff Control: A Case Study in Dublin M. Gholamnia et al. https://doi.org/10.1109/ACCESS.2024.3516313
5. Explainable machine learning for predicting stomatal conductance across multiple plant functional types S. Gaur & D. Drewry https://doi.org/10.1016/j.agrformet.2024.109955
6. Impact of impervious surface spatial morphologies on urban waterlogging: Insights from a cascade modeling chain at catchment scale X. Qin et al. https://doi.org/10.1016/j.scs.2025.106912
7. Enhancing Multi-Step Reservoir Inflow Forecasting: A Time-Variant Encoder–Decoder Approach M. Fan et al. https://doi.org/10.3390/geosciences15080279
8. Data-driven rainfall-runoff modeling in an urban catchment using microwave link attenuation data Y. Song et al. https://doi.org/10.1016/j.jhydrol.2025.134328
9. An XGBoost-SHAP approach to quantifying morphological impact on urban flooding susceptibility M. Wang et al. https://doi.org/10.1016/j.ecolind.2023.111137
10. Enhancing groundwater predictions by incorporating response lag effects in machine learning models F. Yang et al. https://doi.org/10.2166/hydro.2025.295
11. Towards Interpreting Machine‐Learning Models for Multi‐Step Ahead Daily Streamflow Forecasting R. Hao & H. Yan https://doi.org/10.1002/hyp.70163
12. Urban flooding damage prediction in matrix scenarios of extreme rainfall using a convolutional neural network M. Wang et al. https://doi.org/10.1016/j.jhydrol.2024.132069
13. Streamflow Simulation and Interpretability Analysis in Multi-Climatic Basins Using Physics-Based and Data-Driven Hybrid Models J. Chen et al. https://doi.org/10.1007/s11269-026-04566-7
14. Impact of baseflow separation on improving streamflow and its extremes with a hybrid model coupling hydrological and machine learning models K. Zhu et al. https://doi.org/10.1080/02626667.2025.2513478
15. Toward better atmospheric polycyclic aromatic hydrocarbons pollution control in the Northern Hemisphere: Process analysis based on interpretable deep learning models C. Tao et al. https://doi.org/10.1016/j.jclepro.2024.142442
16. Predicting the urban stormwater drainage system state using the Graph-WaveNet M. Li et al. https://doi.org/10.1016/j.scs.2024.105877
17. Multi-Criteria Assessment of Flood Risk on Railroads Using a Machine Learning Approach: A Case Study of Railroads in Minas Gerais F. Oliveira de Sousa et al. https://doi.org/10.3390/infrastructures10010012
18. Regularized and explainable machine learning framework for anthropogenic–climate coupled prediction of wastewater influent in hyper-arid urban utilities A. Alsumaiei https://doi.org/10.2166/wst.2026.205
19. An advanced tool integrating failure and sensitivity analysis into novel modeling of the stormwater flood volume F. Fatone et al. https://doi.org/10.5194/hess-27-3329-2023
20. Explainable ensemble learning for predicting pine wilt disease spread H. Zhou et al. https://doi.org/10.1016/j.jenvman.2025.126910
21. Advances in extraneous water detection of urban sewer networks: From conventional methods to data-driven approaches Q. Wei et al. https://doi.org/10.1080/10643389.2025.2576726
22. Advanced sensitivity analysis of the impact of the temporal distribution and intensity of rainfall on hydrograph parameters in urban catchments F. Fatone et al. https://doi.org/10.5194/hess-25-5493-2021
23. Development of Rainfall-Runoff Models for Sustainable Stormwater Management in Urbanized Catchments B. Szeląg et al. https://doi.org/10.3390/w14131997
24. Global Paradigm Shifts in Urban Stormwater Management Optimization: A Bibliometric Analysis M. Wang et al. https://doi.org/10.3390/w15234122
25. Optimization of Low-Impact Development Spatial Layout Under Multi-Objective Constraints for Sponge City Retrofitting in Older Communities W. Zhang et al. https://doi.org/10.3390/w18040513
26. Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality? C. Varadharajan et al. https://doi.org/10.1002/hyp.14565
27. Towards the development of a ‘land-river-lake’ two-stage deep learning model for water quality prediction and its application in a large plateau lake R. You et al. https://doi.org/10.1016/j.jhydrol.2024.132173
28. Disentangling coastal groundwater level dynamics in a global dataset A. Nolte et al. https://doi.org/10.5194/hess-28-1215-2024
29. Enhancing Peak Runoff Forecasting through Feature Engineering Applied to X-Band Radar Data J. Álvarez-Estrella et al. https://doi.org/10.3390/w16070968
30. Explainable machine learning model for multi-step forecasting of reservoir inflow with uncertainty quantification M. Fan et al. https://doi.org/10.1016/j.envsoft.2023.105849
31. Advancing subseasonal reservoir inflow forecasts using an explainable machine learning method M. Fan et al. https://doi.org/10.1016/j.ejrh.2023.101584
32. Profitability Analysis of Selected Low Impact Development Methods for Decentralised Rainwater Management: A Case Study from Lublin Region, Poland M. Iwanek & P. Suchorab https://doi.org/10.3390/w15142601
33. Development of a Wilks feature importance method with improved variable rankings for supporting hydrological inference and modelling K. Li et al. https://doi.org/10.5194/hess-25-4947-2021
34. Is there differential engagement in urban flooding prevention in Detroit? T. Tran et al. https://doi.org/10.1080/07352166.2026.2642133
35. Investigating hydrological processes using explainable deep-learning models W. Liu et al. https://doi.org/10.1080/02626667.2024.2423050
36. Explainable Artificial Intelligence in Hydrology: Interpreting Black-Box Snowmelt-Driven Streamflow Predictions in an Arid Andean Basin of North-Central Chile J. Núñez et al. https://doi.org/10.3390/w15193369
37. Interpretable machine learning for nitrate and phosphate prediction in the Mar Menor watershed, Spain, under data-scarce conditions V. López-Linares et al. https://doi.org/10.1016/j.ejrh.2026.103403
38. Impacts of blue-green infrastructures on combined sewer overflows S. Moghanlo & A. Raimondi https://doi.org/10.1016/j.nbsj.2024.100208
39. Response time of fast flowing hydrologic pathways controls sediment hysteresis in a low-gradient watershed, as evidenced from tracer results and machine learning models A. Marin-Ramirez et al. https://doi.org/10.1016/j.jhydrol.2024.132207
40. Hydroclimatic time series features at multiple time scales G. Papacharalampous et al. https://doi.org/10.1016/j.jhydrol.2023.129160
41. A Novel Machine Learning Based Cluster-Then-Forecast Framework for Sensor Placement Optimization and Real-Time Water Level Prediction in Low Impact Development (LID) Stormwater System E. Chen & X. Yu https://doi.org/10.1021/acsestwater.4c01277
42. Explainable Artificial Intelligence in Hydrology: A Review M. Zounemat-Kermani & M. Kheimi https://doi.org/10.1007/s11269-025-04435-9
43. Investigation of hydrometeorological influences on reservoir releases using explainable machine learning methods M. Fan et al. https://doi.org/10.3389/frwa.2023.1112970
44. Machine Learning-based Downscaling of GRACE Data to Enhance Assessment of Spatiotemporal Evolution of Coastal Plain Groundwater Storage C. Wu et al. https://doi.org/10.1007/s11269-025-04254-y
45. Fully automated simplification of urban drainage models on a city scale M. Pichler et al. https://doi.org/10.2166/wst.2024.337
46. Artificial Intelligence in Hydrology: Advancements in Soil, Water Resource Management, and Sustainable Development S. Biazar et al. https://doi.org/10.3390/su17052250
47. Predicting wastewater treatment plant influent in mixed, separate, and combined sewers using nearby surface water discharge for better wastewater-based epidemiology sampling design A. Marin-Ramirez et al. https://doi.org/10.1016/j.scitotenv.2023.167375
48. Spatial-temporal behavior of precipitation driven karst spring discharge in a mountain terrain X. Song et al. https://doi.org/10.1016/j.jhydrol.2022.128116
49. Interpretable deep learning for sewer network water level forecasting in a Northern Chinese City: Implications for enhancing real-time assessment of system operational conditions Z. Yi et al. https://doi.org/10.1016/j.ejrh.2026.103116
50. Predicting Dynamic Riverine Nitrogen Export in Unmonitored Watersheds: Leveraging Insights of AI from Data-Rich Regions R. Xiong et al. https://doi.org/10.1021/acs.est.2c02232