Articles | Volume 25, issue 11
https://doi.org/10.5194/hess-25-5839-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-5839-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
11 Nov 2021
Research article |
| 11 Nov 2021
| 11 Nov 2021
Modeling and interpreting hydrological responses of sustainable urban drainage systems with explainable machine learning methods
Yang Yang
Department of Civil Engineering, University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China
Ting Fong May Chui
CORRESPONDING AUTHOR
Department of Civil Engineering, University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China
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- Investigation of hydrometeorological influences on reservoir releases using explainable machine learning methods M. Fan et al. 10.3389/frwa.2023.1112970
- Development of Rainfall-Runoff Models for Sustainable Stormwater Management in Urbanized Catchments B. Szeląg et al. 10.3390/w14131997
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- 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
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- Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality? C. Varadharajan et al. 10.1002/hyp.14565
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- 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
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- Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality? C. Varadharajan et al. 10.1002/hyp.14565
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- 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
18 citations as recorded by crossref.
- Hydroclimatic time series features at multiple time scales G. Papacharalampous et al. 10.1016/j.jhydrol.2023.129160
- Explainable machine learning for predicting stomatal conductance across multiple plant functional types S. Gaur & D. Drewry 10.1016/j.agrformet.2024.109955
- 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
- Disentangling coastal groundwater level dynamics in a global dataset A. Nolte et al. 10.5194/hess-28-1215-2024
- Enhancing Peak Runoff Forecasting through Feature Engineering Applied to X-Band Radar Data J. Álvarez-Estrella et al. 10.3390/w16070968
- Explainable machine learning model for multi-step forecasting of reservoir inflow with uncertainty quantification M. Fan et al. 10.1016/j.envsoft.2023.105849
- Advancing subseasonal reservoir inflow forecasts using an explainable machine learning method M. Fan et al. 10.1016/j.ejrh.2023.101584
- 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
- An XGBoost-SHAP approach to quantifying morphological impact on urban flooding susceptibility M. Wang et al. 10.1016/j.ecolind.2023.111137
- Investigation of hydrometeorological influences on reservoir releases using explainable machine learning methods M. Fan et al. 10.3389/frwa.2023.1112970
- Development of Rainfall-Runoff Models for Sustainable Stormwater Management in Urbanized Catchments B. Szeląg et al. 10.3390/w14131997
- Global Paradigm Shifts in Urban Stormwater Management Optimization: A Bibliometric Analysis M. Wang et al. 10.3390/w15234122
- 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
- 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
- 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
- Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality? C. Varadharajan et al. 10.1002/hyp.14565
- Spatial-temporal behavior of precipitation driven karst spring discharge in a mountain terrain X. Song et al. 10.1016/j.jhydrol.2022.128116
- 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
4 citations as recorded by crossref.
- Machine learning approach towards explaining water quality dynamics in an urbanised river B. Schäfer et al. 10.1038/s41598-022-16342-9
- Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality? C. Varadharajan et al. 10.1002/hyp.14565
- 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
- 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
Latest update: 29 Jun 2024
Short summary
This study uses explainable machine learning methods to model and interpret the statistical correlations between rainfall and the discharge of urban catchments with sustainable urban drainage systems. The resulting models have good prediction accuracies. However, the right predictions may be made for the wrong reasons as the model cannot provide physically plausible explanations as to why a prediction is made.
This study uses explainable machine learning methods to model and interpret the statistical...
