50 citations as recorded by crossref.

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8. Data-driven rainfall-runoff modeling in an urban catchment using microwave link attenuation data Y. Song et al.
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11. Towards Interpreting Machine‐Learning Models for Multi‐Step Ahead Daily Streamflow Forecasting R. Hao & H. Yan
12. Urban flooding damage prediction in matrix scenarios of extreme rainfall using a convolutional neural network M. Wang et al.
13. Streamflow Simulation and Interpretability Analysis in Multi-Climatic Basins Using Physics-Based and Data-Driven Hybrid Models J. Chen et al.
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16. Predicting the urban stormwater drainage system state using the Graph-WaveNet M. Li et al.
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.
18. Regularized and explainable machine learning framework for anthropogenic–climate coupled prediction of wastewater influent in hyper-arid urban utilities A. Alsumaiei
19. An advanced tool integrating failure and sensitivity analysis into novel modeling of the stormwater flood volume F. Fatone et al.
20. Explainable ensemble learning for predicting pine wilt disease spread H. Zhou et al.
21. Advances in extraneous water detection of urban sewer networks: From conventional methods to data-driven approaches Q. Wei et al.
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.
23. Development of Rainfall-Runoff Models for Sustainable Stormwater Management in Urbanized Catchments B. Szeląg et al.
24. Global Paradigm Shifts in Urban Stormwater Management Optimization: A Bibliometric Analysis M. Wang et al.
25. Optimization of Low-Impact Development Spatial Layout Under Multi-Objective Constraints for Sponge City Retrofitting in Older Communities W. Zhang et al.
26. Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality? C. Varadharajan et al.
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.
28. Disentangling coastal groundwater level dynamics in a global dataset A. Nolte et al.
29. Enhancing Peak Runoff Forecasting through Feature Engineering Applied to X-Band Radar Data J. Álvarez-Estrella et al.
30. Explainable machine learning model for multi-step forecasting of reservoir inflow with uncertainty quantification M. Fan et al.
31. Advancing subseasonal reservoir inflow forecasts using an explainable machine learning method M. Fan et al.
32. Profitability Analysis of Selected Low Impact Development Methods for Decentralised Rainwater Management: A Case Study from Lublin Region, Poland M. Iwanek & P. Suchorab
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34. Is there differential engagement in urban flooding prevention in Detroit? T. Tran et al.
35. Investigating hydrological processes using explainable deep-learning models W. Liu et al.
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.
37. Interpretable machine learning for nitrate and phosphate prediction in the Mar Menor watershed, Spain, under data-scarce conditions V. López-Linares et al.
38. Impacts of blue-green infrastructures on combined sewer overflows S. Moghanlo & A. Raimondi
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.
40. Hydroclimatic time series features at multiple time scales G. Papacharalampous et al.
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
42. Explainable Artificial Intelligence in Hydrology: A Review M. Zounemat-Kermani & M. Kheimi
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44. Machine Learning-based Downscaling of GRACE Data to Enhance Assessment of Spatiotemporal Evolution of Coastal Plain Groundwater Storage C. Wu et al.
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