29 citations as recorded by crossref.

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2. Explainable machine learning for predicting stomatal conductance across multiple plant functional types S. Gaur & D. Drewry 10.1016/j.agrformet.2024.109955
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4. Enhancing Peak Runoff Forecasting through Feature Engineering Applied to X-Band Radar Data J. Álvarez-Estrella et al. 10.3390/w16070968
5. Explainable machine learning model for multi-step forecasting of reservoir inflow with uncertainty quantification M. Fan et al. 10.1016/j.envsoft.2023.105849
6. Advancing subseasonal reservoir inflow forecasts using an explainable machine learning method M. Fan et al. 10.1016/j.ejrh.2023.101584
7. Profitability Analysis of Selected Low Impact Development Methods for Decentralised Rainwater Management: A Case Study from Lublin Region, Poland M. Iwanek & P. Suchorab 10.3390/w15142601
8. An XGBoost-SHAP approach to quantifying morphological impact on urban flooding susceptibility M. Wang et al. 10.1016/j.ecolind.2023.111137
9. Investigating hydrological processes using explainable deep-learning models W. Liu et al. 10.1080/02626667.2024.2423050
10. Urban flooding damage prediction in matrix scenarios of extreme rainfall using a convolutional neural network M. Wang et al. 10.1016/j.jhydrol.2024.132069
11. 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. 10.3390/w15193369
12. Toward better atmospheric polycyclic aromatic hydrocarbons pollution control in the Northern Hemisphere: Process analysis based on interpretable deep learning models C. Tao et al. 10.1016/j.jclepro.2024.142442
13. Predicting the urban stormwater drainage system state using the Graph-WaveNet M. Li et al. 10.1016/j.scs.2024.105877
14. 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. 10.1016/j.jhydrol.2024.132207
15. Hydroclimatic time series features at multiple time scales G. Papacharalampous et al. 10.1016/j.jhydrol.2023.129160
16. An advanced tool integrating failure and sensitivity analysis into novel modeling of the stormwater flood volume F. Fatone et al. 10.5194/hess-27-3329-2023
17. Investigation of hydrometeorological influences on reservoir releases using explainable machine learning methods M. Fan et al. 10.3389/frwa.2023.1112970
18. Development of Rainfall-Runoff Models for Sustainable Stormwater Management in Urbanized Catchments B. Szeląg et al. 10.3390/w14131997
19. Global Paradigm Shifts in Urban Stormwater Management Optimization: A Bibliometric Analysis M. Wang et al. 10.3390/w15234122
20. Fully automated simplification of urban drainage models on a city scale M. Pichler et al. 10.2166/wst.2024.337
21. 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. 10.1016/j.scitotenv.2023.167375
22. Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality? C. Varadharajan et al. 10.1002/hyp.14565
23. 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. 10.1016/j.jhydrol.2024.132173
24. Spatial-temporal behavior of precipitation driven karst spring discharge in a mountain terrain X. Song et al. 10.1016/j.jhydrol.2022.128116
25. Predicting Dynamic Riverine Nitrogen Export in Unmonitored Watersheds: Leveraging Insights of AI from Data-Rich Regions R. Xiong et al. 10.1021/acs.est.2c02232
26. Machine learning approach towards explaining water quality dynamics in an urbanised river B. Schäfer et al. 10.1038/s41598-022-16342-9
27. Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality? C. Varadharajan et al. 10.1002/hyp.14565
28. Advanced sensitivity analysis of the impact of the temporal distribution and intensity of rainfall on hydrograph parameters in urban catchments F. Fatone et al. 10.5194/hess-25-5493-2021
29. Development of a Wilks feature importance method with improved variable rankings for supporting hydrological inference and modelling K. Li et al. 10.5194/hess-25-4947-2021