Articles | Volume 25, issue 11
Research article 11 Nov 2021
Research article | 11 Nov 2021
Modeling and interpreting hydrological responses of sustainable urban drainage systems with explainable machine learning methods
Yang Yang and Ting Fong May Chui
No articles found.
D. H. Trinh and T. F. M. Chui
Hydrol. Earth Syst. Sci., 17, 4789–4801,
Related subject area
Subject: Urban Hydrology | Techniques and Approaches: Modelling approachesThe impact of the spatiotemporal structure of rainfall on flood frequency over a small urban watershed: an approach coupling stochastic storm transposition and hydrologic modelingSpace variability impacts on hydrological responses of nature-based solutions and the resulting uncertainty: a case study of Guyancourt (France)Urban surface water flood modelling – a comprehensive review of current models and future challengesResampling and ensemble techniques for improving ANN-based high-flow forecast accuracyEvaluating different machine learning methods to simulate runoff from extensive green roofsEvent selection and two-stage approach for calibrating models of green urban drainage systemsModeling the high-resolution dynamic exposure to flooding in a city regionDrainage area characterization for evaluating green infrastructure using the Storm Water Management ModelCritical scales to explain urban hydrological response: an application in Cranbrook, LondonIncrease in flood risk resulting from climate change in a developed urban watershed – the role of storm temporal patternsPatterns and comparisons of human-induced changes in river flood impacts in citiesScale effect challenges in urban hydrology highlighted with a distributed hydrological modelComparison of the impacts of urban development and climate change on exposing European cities to pluvial floodingSpatial and temporal variability of rainfall and their effects on hydrological response in urban areas – a reviewHydrodynamics of pedestrians' instability in floodwatersFormulating and testing a method for perturbing precipitation time series to reflect anticipated climatic changesUsing rainfall thresholds and ensemble precipitation forecasts to issue and improve urban inundation alertsEnhancing the T-shaped learning profile when teaching hydrology using data, modeling, and visualization activitiesOn the sensitivity of urban hydrodynamic modelling to rainfall spatial and temporal resolutionPrecipitation variability within an urban monitoring network via microcanonical cascade generatorsEstimation of peak discharges of historical floodsIndirect downscaling of hourly precipitation based on atmospheric circulation and temperatureAssessing the hydrologic restoration of an urbanized area via an integrated distributed hydrological modelUsing the Storm Water Management Model to predict urban headwater stream hydrological response to climate and land cover changeEvaluating scale and roughness effects in urban flood modelling using terrestrial LIDAR dataContribution of directly connected and isolated impervious areas to urban drainage network hydrographsThermal management of an unconsolidated shallow urban groundwater bodyOnline multistep-ahead inundation depth forecasts by recurrent NARX networksA statistical analysis of insurance damage claims related to rainfall extremesJoint impact of rainfall and tidal level on flood risk in a coastal city with a complex river network: a case study of Fuzhou City, ChinaUrbanization and climate change impacts on future urban flooding in Can Tho city, VietnamMulti-objective optimization for combined quality–quantity urban runoff controlDevelopment of flood probability charts for urban drainage network in coastal areas through a simplified joint assessment approachAuto-control of pumping operations in sewerage systems by rule-based fuzzy neural networksCoupling urban event-based and catchment continuous modelling for combined sewer overflow river impact assessmentDynamic neural networks for real-time water level predictions of sewerage systems-covering gauged and ungauged sites
Zhengzheng Zhou, James A. Smith, Mary Lynn Baeck, Daniel B. Wright, Brianne K. Smith, and Shuguang Liu
Hydrol. Earth Syst. Sci., 25, 4701–4717,Short summary
The role of rainfall space–time structure in flood response is an important research issue in urban hydrology. This study contributes to this understanding in small urban watersheds. Combining stochastically based rainfall scenarios with a hydrological model, the results show the complexities of flood response for various return periods, implying the common assumptions of spatially uniform rainfall in urban flood frequency are problematic, even for relatively small basin scales.
Yangzi Qiu, Igor da Silva Rocha Paz, Feihu Chen, Pierre-Antoine Versini, Daniel Schertzer, and Ioulia Tchiguirinskaia
Hydrol. Earth Syst. Sci., 25, 3137–3162,Short summary
Our original research objective is to investigate the uncertainties of the hydrological responses of nature-based solutions (NBSs) that result from the multiscale space variability in both the rainfall and the NBS distribution. Results show that the intersection effects of spatial variability in rainfall and the spatial arrangement of NBS can generate uncertainties of peak flow and total runoff volume estimations in NBS scenarios.
Kaihua Guo, Mingfu Guan, and Dapeng Yu
Hydrol. Earth Syst. Sci., 25, 2843–2860,Short summary
This study presents a comprehensive review of models and emerging approaches for predicting urban surface water flooding driven by intense rainfall. It explores the advantages and limitations of existing models and identifies major challenges. Issues of model complexities, scale effects, and computational efficiency are also analysed. The results will inform scientists, engineers, and decision-makers of the latest developments and guide the model selection based on desired objectives.
Everett Snieder, Karen Abogadil, and Usman T. Khan
Hydrol. Earth Syst. Sci., 25, 2543–2566,Short summary
Flow distributions are highly skewed, resulting in low prediction accuracy of high flows when using artificial neural networks for flood forecasting. We investigate the use of resampling and ensemble techniques to address the problem of skewed datasets to improve high flow prediction. The methods are implemented both independently and in combined, hybrid techniques. This research presents the first analysis of the effects of combining these methods on high flow prediction accuracy.
Elhadi Mohsen Hassan Abdalla, Vincent Pons, Virginia Stovin, Simon De-Ville, Elizabeth Fassman-Beck, Knut Alfredsen, and Tone Merete Muthanna
Hydrol. Earth Syst. Sci. Discuss.,
Revised manuscript accepted for HESS
Ico Broekhuizen, Günther Leonhardt, Jiri Marsalek, and Maria Viklander
Hydrol. Earth Syst. Sci., 24, 869–885,Short summary
Urban drainage models are usually calibrated using a few events so that they accurately represent a real-world site. This paper compares 14 single- and two-stage strategies for selecting these events and found significant variation between them in terms of model performance and the obtained values of model parameters. Calibrating parameters for green and impermeable areas in two separate stages improved model performance in the validation period while making calibration easier and faster.
Xuehong Zhu, Qiang Dai, Dawei Han, Lu Zhuo, Shaonan Zhu, and Shuliang Zhang
Hydrol. Earth Syst. Sci., 23, 3353–3372,Short summary
Urban flooding exposure is generally investigated with the assumption of stationary disasters and disaster-hit bodies during an event, and thus it cannot satisfy the increasingly elaborate modeling and management of urban floods. In this study, a comprehensive method was proposed to simulate dynamic exposure to urban flooding considering human mobility. Several scenarios, including diverse flooding types and various responses of residents to flooding, were considered.
Joong Gwang Lee, Christopher T. Nietch, and Srinivas Panguluri
Hydrol. Earth Syst. Sci., 22, 2615–2635,Short summary
This paper demonstrates an approach to spatial discretization for analyzing green infrastructure (GI) using SWMM. Besides DCIA, pervious buffers should be identified for GI modeling. Runoff contributions from different spatial components and flow pathways would impact GI performance. The presented approach can reduce the number of calibration parameters and apply scale–independently to a watershed scale. Hydrograph separation can add insights for developing GI scenarios.
Elena Cristiano, Marie-Claire ten Veldhuis, Santiago Gaitan, Susana Ochoa Rodriguez, and Nick van de Giesen
Hydrol. Earth Syst. Sci., 22, 2425–2447,Short summary
In this work we investigate the influence rainfall and catchment scales have on hydrological response. This problem is quite relevant in urban areas, where the response is fast due to the high degree of imperviousness. We presented a new approach to classify rainfall variability in space and time and use this classification to investigate rainfall aggregation effects on urban hydrological response. This classification allows the spatial extension of the main core of the storm to be identified.
Suresh Hettiarachchi, Conrad Wasko, and Ashish Sharma
Hydrol. Earth Syst. Sci., 22, 2041–2056,Short summary
The study examines the impact of higher temperatures expected in a future climate on how rainfall varies with time during severe storm events. The results show that these impacts increase future flood risk in urban environments and that current design guidelines need to be adjusted so that effective adaptation measures can be implemented.
Stephanie Clark, Ashish Sharma, and Scott A. Sisson
Hydrol. Earth Syst. Sci., 22, 1793–1810,Short summary
This study investigates global patterns relating urban river flood impacts to socioeconomic development and changing hydrologic conditions, and comparisons are provided between 98 individual cities. This paper condenses and communicates large amounts of information to accelerate the understanding of relationships between local urban conditions and global processes, and to potentially motivate knowledge transfer between decision-makers facing similar circumstances.
Abdellah Ichiba, Auguste Gires, Ioulia Tchiguirinskaia, Daniel Schertzer, Philippe Bompard, and Marie-Claire Ten Veldhuis
Hydrol. Earth Syst. Sci., 22, 331–350,Short summary
This paper proposes a two-step investigation to illustrate the extent of scale effects in urban hydrology. First, fractal tools are used to highlight the scale dependency observed within GIS data inputted in urban hydrological models. Then an intensive multi-scale modelling work was carried out to confirm effects on model performances. The model was implemented at 17 spatial resolutions ranging from 100 to 5 m. Results allow the understanding of scale challenges in hydrology modelling.
Per Skougaard Kaspersen, Nanna Høegh Ravn, Karsten Arnbjerg-Nielsen, Henrik Madsen, and Martin Drews
Hydrol. Earth Syst. Sci., 21, 4131–4147,
Elena Cristiano, Marie-Claire ten Veldhuis, and Nick van de Giesen
Hydrol. Earth Syst. Sci., 21, 3859–3878,Short summary
In the last decades, new instruments were developed to measure rainfall and hydrological processes at high resolution. Weather radars are used, for example, to measure how rainfall varies in space and time. At the same time, new models were proposed to reproduce and predict hydrological response, in order to prevent flooding in urban areas. This paper presents a review of our current knowledge of rainfall and hydrological processes in urban areas, focusing on their variability in time and space.
Chiara Arrighi, Hocine Oumeraci, and Fabio Castelli
Hydrol. Earth Syst. Sci., 21, 515–531,Short summary
In developed countries, the majority of fatalities during floods occurs as a consequence of inappropriate high-risk behaviour such as walking or driving in floodwaters. This work addresses pedestrians' instability in floodwaters. It analyses both the contribution of flood and human physical characteristics in the loss of stability highlighting the key role of subject height (submergence) and flow regime. The method consists of a re-analysis of experiments and numerical modelling.
Hjalte Jomo Danielsen Sørup, Stylianos Georgiadis, Ida Bülow Gregersen, and Karsten Arnbjerg-Nielsen
Hydrol. Earth Syst. Sci., 21, 345–355,Short summary
In this study we propose a methodology changing present-day precipitation time series to reflect future changed climate. Present-day time series have a much finer resolution than what is provided by climate models and thus have a much broader application range. The proposed methodology is able to replicate most expectations of climate change precipitation. These time series can be used to run fine-scale hydrological and hydraulic models and thereby assess the influence of climate change on them.
Tsun-Hua Yang, Gong-Do Hwang, Chin-Cheng Tsai, and Jui-Yi Ho
Hydrol. Earth Syst. Sci., 20, 4731–4745,Short summary
Taiwan continues to suffer from floods. This study proposes the integration of rainfall thresholds and ensemble precipitation forecasts to provide probabilistic urban inundation forecasts. Utilization of ensemble precipitation forecasts can extend forecast lead times to 72 h, preceding peak flows and allowing response agencies to take necessary preparatory measures. This study also develops a hybrid of real-time observation and rainfall forecasts to improve the first 24 h inundation forecasts.
Christopher A. Sanchez, Benjamin L. Ruddell, Roy Schiesser, and Venkatesh Merwade
Hydrol. Earth Syst. Sci., 20, 1289–1299,Short summary
The use of authentic learning activities is especially important for place-based geosciences like hydrology, where professional breadth and technical depth are critical for practicing hydrologists. The current study found that integrating computerized learning content into the learning experience, using only a simple spreadsheet tool and readily available hydrological data, can effectively bring the "real world" into the classroom and provide an enriching educational experience.
G. Bruni, R. Reinoso, N. C. van de Giesen, F. H. L. R. Clemens, and J. A. E. ten Veldhuis
Hydrol. Earth Syst. Sci., 19, 691–709,
P. Licznar, C. De Michele, and W. Adamowski
Hydrol. Earth Syst. Sci., 19, 485–506,
J. Herget, T. Roggenkamp, and M. Krell
Hydrol. Earth Syst. Sci., 18, 4029–4037,
F. Beck and A. Bárdossy
Hydrol. Earth Syst. Sci., 17, 4851–4863,
D. H. Trinh and T. F. M. Chui
Hydrol. Earth Syst. Sci., 17, 4789–4801,
J. Y. Wu, J. R. Thompson, R. K. Kolka, K. J. Franz, and T. W. Stewart
Hydrol. Earth Syst. Sci., 17, 4743–4758,
H. Ozdemir, C. C. Sampson, G. A. M. de Almeida, and P. D. Bates
Hydrol. Earth Syst. Sci., 17, 4015–4030,
Y. Seo, N.-J. Choi, and A. R. Schmidt
Hydrol. Earth Syst. Sci., 17, 3473–3483,
J. Epting, F. Händel, and P. Huggenberger
Hydrol. Earth Syst. Sci., 17, 1851–1869,
H.-Y. Shen and L.-C. Chang
Hydrol. Earth Syst. Sci., 17, 935–945,
M. H. Spekkers, M. Kok, F. H. L. R. Clemens, and J. A. E. ten Veldhuis
Hydrol. Earth Syst. Sci., 17, 913–922,
J. J. Lian, K. Xu, and C. Ma
Hydrol. Earth Syst. Sci., 17, 679–689,
H. T. L. Huong and A. Pathirana
Hydrol. Earth Syst. Sci., 17, 379–394,
S. Oraei Zare, B. Saghafian, and A. Shamsai
Hydrol. Earth Syst. Sci., 16, 4531–4542,
R. Archetti, A. Bolognesi, A. Casadio, and M. Maglionico
Hydrol. Earth Syst. Sci., 15, 3115–3122,
Y.-M. Chiang, L.-C. Chang, M.-J. Tsai, Y.-F. Wang, and F.-J. Chang
Hydrol. Earth Syst. Sci., 15, 185–196,
I. Andrés-Doménech, J. C. Múnera, F. Francés, and J. B. Marco
Hydrol. Earth Syst. Sci., 14, 2057–2072,
Yen-Ming Chiang, Li-Chiu Chang, Meng-Jung Tsai, Yi-Fung Wang, and Fi-John Chang
Hydrol. Earth Syst. Sci., 14, 1309–1319,
Ahmad, M. A., Teredesai, A., and Eckert, C.: Interpretable machine learning in healthcare, in: Proceedings – 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018, p. 447, 4 to 7 June 2018, New York City, NY, USA, 2018.
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R., and Herrera, F.: Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI, Inf. Fusion, 58, 82–115, https://doi.org/10.1016/J.INFFUS.2019.12.012, 2020.
Beven, K. and Freer, J.: Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology, J. Hydrol., 249, 11–29, https://doi.org/10.1016/S0022-1694(01)00421-8, 2001.
Bischl, B., Richter, J., Bossek, J., Horn, D., Thomas, J., and Lang, M.: mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions, arXiv [preprint], arXiv:1703.03373v3, 2017.
Bojanowski, P., Joulin, A., Paz, D. L., and Szlam, A.: Optimizing the latent space of generative networks, in: 35th International Conference on Machine Learning, ICML 2018, vol. 2, 960–972, Stockholm, Sweden, 10 to 15 July 2018, 2018.
Bouaziz, L. J. E., Fenicia, F., Thirel, G., de Boer-Euser, T., Buitink, J., Brauer, C. C., De Niel, J., Dewals, B. J., Drogue, G., Grelier, B., Melsen, L. A., Moustakas, S., Nossent, J., Pereira, F., Sprokkereef, E., Stam, J., Weerts, A. H., Willems, P., Savenije, H. H. G., and Hrachowitz, M.: Behind the scenes of streamflow model performance, Hydrol. Earth Syst. Sci., 25, 1069–1095, https://doi.org/10.5194/hess-25-1069-2021, 2021.
Charlesworth, S. M.: A review of the adaptation and mitigation of global climate change using sustainable drainage in cities, J. Water Clim. Chang., 1, 165–180, https://doi.org/10.2166/wcc.2010.035, 2010.
Chen, H., Janizek, J. D., Lundberg, S., and Lee, S. I.: True to the model or true to the data?, arXiv [preprint], arXiv:1805.11783, 2020.
Chen, T. and Guestrin, C.: XGBoost: A scalable tree boosting system, in: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 13–17 August, 785–794, San Francisco, CA, USA, 2016.
Chen, T. and He, T.: xgboost: eXtreme Gradient Boosting, available at: https://cran.r-project.org/web/packages/xgboost/vignettes/xgboost.pdf, last access: 29 June 2020.
Damodaram, C., Giacomoni, M. H., Prakash Khedun, C., Holmes, H., Ryan, A., Saour, W., and Zechman, E. M.: Simulation of combined best management practices and low impact development for sustainable stormwater management, J. Am. Water Resour. Assoc., 46, 907–918, https://doi.org/10.1111/j.1752-1688.2010.00462.x, 2010.
Darner, R. A. and Dumouchelle, D. H.: Hydraulic Characteristics of Low-Impact Development Practices in Northeastern Ohio, 2008-2010: U.S. Geological Survey Scientific Investigations Report 2011–5165, available at: https://pubs.usgs.gov/sir/2011/5165/ (last access: 7 July 2020), 2011.
Darner, R. A., Shuster, W. D., and Dumouchelle, D. H.: Hydrologic Characteristics of Low-Impact Stormwater Control Measures at Two Sites in Northeastern Ohio, 2008–2013: U.S. Geological Survey Scientific Investigations Report 2015-5030, U.S. Geological Survey, Reston, VA, USA, 2015.
DeBusk, K. M., Hunt, W. F., and Line, D. E.: Bioretention Outflow: Does It Mimic Nonurban Watershed Shallow Interflow?, J. Hydrol. Eng., 16, 274–279, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000315, 2011.
Demirdjian, D., Taycher, L., Shakhnarovich, G., Grauman, K., and Darrell, T.: Avoiding the “streetlight effect”: Tracking by exploring likelihood modes, in: Proceedings of the IEEE International Conference on Computer Vision, vol. I, 357–364, San Diego, CA, USA, 20 to 26 June 2005, 2005.
Eckart, K., McPhee, Z., and Bolisetti, T.: Performance and implementation of low impact development – A review, Sci. Total Environ., 607–608, 413–432, https://doi.org/10.1016/j.scitotenv.2017.06.254, 2017.
Elliott, A. H. and Trowsdale, S. A.: A review of models for low impact urban stormwater drainage, Environ. Model. Softw., 22, 394–405, https://doi.org/10.1016/j.envsoft.2005.12.005, 2007.
EPA: Flow and Rainfall Data used for SHC Headwatershed SWMM Calibration, EPA [data set], https://doi.org/10.23719/1378947, 2017.
Eric, M., Li, J., and Joksimovic, D.: Performance Evaluation of Low Impact Development Practices Using Linear Regression, Br. J. Environ. Clim. Chang., 5, 78–90, https://doi.org/10.9734/bjecc/2015/11578, 2015.
Fassman-Beck, E., Hunt, W., Berghage, R., Carpenter, D., Kurtz, T., Stovin, V., and Wadzuk, B.: Curve number and runoff coefficients for extensive living roofs, J. Hydrol. Eng., 21, 04015073, https://doi.org/10.1061/(ASCE)HE.1943-5584.0001318, 2016.
Fletcher, T. D., Shuster, W., Hunt, W. F., Ashley, R., Butler, D., Arthur, S., Trowsdale, S., Barraud, S., Semadeni-Davies, A., Bertrand-Krajewski, J. L., Mikkelsen, P. S., Rivard, G., Uhl, M., Dagenais, D., and Viklander, M.: SUDS, LID, BMPs, WSUD and more – The evolution and application of terminology surrounding urban drainage, Urban Water J., 12, 525–542, https://doi.org/10.1080/1573062X.2014.916314, 2015.
Frazier, P. I.: A tutorial on bayesian optimization, arXiv [preprint], arXiv:1807.02811, 2018.
Friedman, J. H.: Greedy function approximation: A gradient boosting machine, Ann. Stat., 29, 1189–1232, https://doi.org/10.1214/aos/1013203451, 2001.
Gimenez-Maranges, M., Breuste, J., and Hof, A.: Sustainable Drainage Systems for transitioning to sustainable urban flood management in the European Union: A review, J. Clean. Prod., 255, 120191, https://doi.org/10.1016/j.jclepro.2020.120191, 2020.
Grolemund, G. and Wickham, H.: Dates and Times Made Easy with lubridate, J. Stat. Softw., 40, 1–25, 2011.
Gauch, M., Kratzert, F., Klotz, D., Nearing, G., Lin, J., and Hochreiter, S.: Rainfall–runoff prediction at multiple timescales with a single Long Short-Term Memory network, Hydrol. Earth Syst. Sci., 25, 2045–2062, https://doi.org/10.5194/hess-25-2045-2021, 2021.
Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., and Pedreschi, D.: A Survey of Methods for Explaining Black Box Models, ACM Comput. Surv., 51, 1–42, https://doi.org/10.1145/3236009, 2019.
Guo, Y. and Senior, M. J.: Climate model simulation of point rainfall frequency characteristics, J. Hydrol. Eng., 11, 547–554, https://doi.org/10.1061/(ASCE)1084-0699(2006)11:6(547), 2006.
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R., Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A., del Río, J. F., Wiebe, M., Peterson, P., Gérard-Marchant, P., Sheppard, K., Reddy, T., Weckesser, W., Abbasi, H., Gohlke, C., and Oliphant, T. E.: Array programming with NumPy, Nature, 585, 357–362, https://doi.org/10.1038/s41586-020-2649-2, 2020.
Hastie, T., Tibshirani, R., and Friedman, J.: The Elements of Statistical Learning, Springer New York, New York, NY, USA, 2009.
Hoghooghi, N., Golden, H. E., Bledsoe, B. P., Barnhart, B. L., Brookes, A. F., Djang, K. S., Halama, J. J., McKane, R. B., Nietch, C. T., and Pettus, P. P.: Cumulative effects of Low Impact Development on watershed hydrology in a mixed land-cover system, Water, 10, 991, https://doi.org/10.3390/w10080991, 2018.
Hopkins, K. G., Bhaskar, A. S., Woznicki, S. A., and Fanelli, R. M.: Changes in event-based streamflow magnitude and timing after suburban development with infiltration-based stormwater management, Hydrol. Process., 34, 387–403, https://doi.org/10.1002/hyp.13593, 2020.
Ilyas, A., Santurkar, S., Tsipras, D., Engstrom, L., Tran, B., and Madry, A.: Adversarial examples are not bugs, they are features, in: Advances in Neural Information Processing Systems, vol. 32, GitHub [data set], available at: http://git.io/adv-datasets (last access: 28 June 2021), 2019.
Janzing, D., Minorics, L., and Blöbaum, P.: Feature relevance quantification in explainable ai: A causal problem, arXiv [preprint], arXiv:1910.13413, 2019.
Johannessen, B. G., Hanslin, H. M., and Muthanna, T. M.: Green roof performance potential in cold and wet regions, Ecol. Eng., 106, 436–447, https://doi.org/10.1016/j.ecoleng.2017.06.011, 2017.
Jones, P. and Macdonald, N.: Making space for unruly water: Sustainable drainage systems and the disciplining of surface runoff, Geoforum, 38, 534–544, https://doi.org/10.1016/j.geoforum.2006.10.005, 2007.
Khan, U. T., Valeo, C., Chu, A., and He, J.: A data driven approach to bioretention cell performance: Prediction and design, Water, 5, 13–28, https://doi.org/10.3390/w5010013, 2013.
Kirchner, J. W.: Getting the right answers for the right reasons: Linking measurements, analyses, and models to advance the science of hydrology, Water Resour. Res., 42, W03S04, https://doi.org/10.1029/2005WR004362, 2006.
Kratzert, F., Herrnegger, M., Klotz, D., Hochreiter, S., and Klambauer, G.: NeuralHydrology – Interpreting LSTMs in Hydrology, in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11700 LNCS, 347–362, Springer, Cham, Switzerland, 2019.
Kuhn, M. and Johnson, K.: Applied predictive modeling, Springer, New York, NY, USA, 2013.
Kuhn, M. and Johnson, K.: Feature Engineering and Selection: a Practical Approach for Predictive Models., Chapman and Hall/CRC, available at: https://www.routledge.com/Feature-Engineering-and-Selection-A-Practical-Approach-for-Predictive-Models/Kuhn-Johnson/p/book/9781138079229 (last access: 24 July 2020), 2019.
Kuhn, M. and Wickham, H.: Tidymodels: a collection of packages for modeling and machine learning using tidyverse principles, available at: https://www.tidymodels.org (last access: 8 November 2021), 2020.
Lee, J. G., Nietch, C. T., and Panguluri, S.: Drainage area characterization for evaluating green infrastructure using the Storm Water Management Model, Hydrol. Earth Syst. Sci., 22, 2615–2635, https://doi.org/10.5194/hess-22-2615-2018, 2018a.
Lee, J. G., Nietch, C. T., and Panguluri, S.: SWMM Modeling Methods for Simulating Green Infrastructure at a Suburban Headwatershed: User's Guide, U.S. Environ. Prot. Agency, October, 157, available at: https://nepis.epa.gov/Exe/ZyPDF.cgi/P100TJ39.PDF?Dockey=P100TJ39.PDF%0A (last access: 11 July 2020b), 2018b.
Li, S., Kazemi, H., and Rockaway, T. D.: Performance assessment of stormwater GI practices using artificial neural networks, Sci. Total Environ., 651, 2811–2819, https://doi.org/10.1016/j.scitotenv.2018.10.155, 2019.
Liu, J., Sample, D., Bell, C., and Guan, Y.: Review and Research Needs of Bioretention Used for the Treatment of Urban Stormwater, Water, 6, 1069–1099, https://doi.org/10.3390/w6041069, 2014.
Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., and Zhang, G.: Learning under Concept Drift: A Review, IEEE Trans. Knowl. Data Eng., 31, 2346–2363, https://doi.org/10.1109/TKDE.2018.2876857, 2019.
Lundberg, S. M. and Lee, S. I.: A unified approach to interpreting model predictions, in: Advances in Neural Information Processing Systems, December 2017, 4766–4775, available at: https://github.com/slundberg/shap (last access: 30 June 2020), 2017.
Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., and Lee, S.-I.: From local explanations to global understanding with explainable AI for trees, Nat. Mach. Intell., 2, 56–67, https://doi.org/10.1038/s42256-019-0138-9, 2020.
Maier, H. R. and Dandy, G. C.: Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications, Environ. Model. Softw., 15, 101–124, https://doi.org/10.1016/S1364-8152(99)00007-9, 2000.
Mitchell, R. and Frank, E.: Accelerating the XGBoost algorithm using GPU computing, PeerJ Comput. Sci., 2017, e127, https://doi.org/10.7717/peerj-cs.127, 2017.
Montalto, F., Behr, C., Alfredo, K., Wolf, M., Arye, M., and Walsh, M.: Rapid assessment of the cost-effectiveness of low impact development for CSO control, Landscape Urban Plan., 82, 117–131, https://doi.org/10.1016/j.landurbplan.2007.02.004, 2007.
Morton, A.: Mathematical models: Questions of trustworthiness, Br. J. Philos. Sci., 44, 659–674, https://doi.org/10.1093/bjps/44.4.659, 1993.
Muthanna, T. M., Viklander, M., and Thorolfsson, S. T.: Seasonal climatic effects on the hydrology of a rain garden, Hydrol. Process., 22, 1640–1649, https://doi.org/10.1002/hyp.6732, 2008.
Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual models part I – A discussion of principles, J. Hydrol., 10, 282–290, https://doi.org/10.1016/0022-1694(70)90255-6, 1970.
Nearing, G. S., Kratzert, F., Sampson, A. K., Pelissier, C. S., Klotz, D., Frame, J. M., Prieto, C., and Gupta, H. V: What Role Does Hydrological Science Play in the Age of Machine Learning?, Water Resour. Res., 57, e2020WR028091, https://doi.org/10.1029/2020WR028091, 2021.
Niazi, M., Nietch, C., Maghrebi, M., Jackson, N., Bennett, B. R., Tryby, M., and Massoudieh, A.: Storm Water Management Model: Performance Review and Gap Analysis, J. Sustain. Water Built Environ., 3, 04017002, https://doi.org/10.1061/JSWBAY.0000817, 2017.
Nielsen, A.: Practical Time Series Analysis, O'Reilly Media, Inc., available at: https://www.oreilly.com/library/view/practical-time-series/9781492041641/ (last access: 30 June 2020), 2019.
Nielsen, D.: Tree Boosting With XGBoost: Why does XGBoost win every machine learning competition?, Master's Thesis, Norwegian University of Science and Technolgy, http://hdl.handle.net/11250/2433761 (last access: 10 November 2021), Norwegian University of Science and Technology, Norway, 2016.
Oreskes, N., Shrader-Frechette, K., and Belitz, K.: Verification, validation, and confirmation of numerical models in the earth sciences, Science, 80, 641–646, https://doi.org/10.1126/science.263.5147.641, 1994.
Osborne, M. J. and Rubinstein, A.: A course in game theory, MIT press, Cambridge, MA, USA, 1994.
Pelletier, A. and Andréassian, V.: Hydrograph separation: an impartial parametrisation for an imperfect method, Hydrol. Earth Syst. Sci., 24, 1171–1187, https://doi.org/10.5194/hess-24-1171-2020, 2020.
Rosa, D. J., Clausen, J. C., and Dietz, M. E.: Calibration and Verification of SWMM for Low Impact Development, J. Am. Water Resour. Assoc., 51, 746–757, https://doi.org/10.1111/jawr.12272, 2015.
Ross, A., Hughes, M. C., and Doshi-Velez, F.: Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations, available at: https://github.com/dtak/rrr (last access: 2 September 2021), 2017.
Rudin, C.: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead, Nat. Mach. Intell., 1, 206–215, https://doi.org/10.1038/s42256-019-0048-x, 2019.
Schmidt, L., Heße, F., Attinger, S., and Kumar, R.: Challenges in Applying Machine Learning Models for Hydrological Inference: A Case Study for Flooding Events Across Germany, Water Resour. Res., 56, e2019WR025924, https://doi.org/10.1029/2019WR025924, 2020.
Selbig, W. R., Buer, N., and Danz, M. E.: Stormwater-quality performance of lined permeable pavement systems, J. Environ. Manage., 251, 109510, https://doi.org/10.1016/j.jenvman.2019.109510, 2019.
Shahriari, B., Swersky, K., Wang, Z., Adams, R. P., and De Freitas, N.: Taking the human out of the loop: A review of Bayesian optimization, Proc. IEEE, 104, 148–175, https://doi.org/10.1109/JPROC.2015.2494218, 2016.
Snoek, J., Larochelle, H., and Adams, R. P.: Practical Bayesian optimization of machine learning algorithms, in: Advances in Neural Information Processing Systems, vol. 4, 2951–2959, arXiv [preprint], arXiv:1206.2944v2, 2012.
Shapley, L. S.: A value of n-person games. Contributions to the Theory of Games, 307–317, Princeton University Press, Princeton, NJ, USA, 1953.
Solomatine, D. P. and Dulal, K. N.: Model trees as an alternative to neural networks in rainfall-runoff modelling, Hydrol. Sci. J., 48, 399–411, https://doi.org/10.1623/hysj.48.3.399.45291, 2003.
Solomatine, D. P. and Ostfeld, A.: Data-driven modelling: Some past experiences and new approaches, J. Hydroinform., 10, 3–22, 2008.
Starn, J. J., Kauffman, L. J., Carlson, C. S., Reddy, J. E., and Fienen, M. N.: Three-Dimensional Distribution of Groundwater Residence Time Metrics in the Glaciated United States Using Metamodels Trained on General Numerical Simulation Models, Water Resour. Res., 57, e2020WR027335, https://doi.org/10.1029/2020WR027335, 2021.
stsfk: stsfk/ExplainableML_SuDS: (v1.0), Zenodo [code], https://doi.org/10.5281/zenodo.5652719, 2021.
Sundararajan, M. and Najmi, A.: The many shapley values for model explanation, in: 37th International Conference on Machine Learning, ICML 2020, vol. PartF16814, 9210–9220, 13 to 18 July 2020, 2020.
Sundararajan, M., Taly, A., and Yan, Q.: Axiomatic attribution for deep networks, in 34th International Conference on Machine Learning, ICML 2017, vol. 7, pp. 5109–5118, Sydney, Australia, 6 to 11 August 2017, 2017.
Teetor, N.: zeallot: Multiple, Unpacking, and Destructuring Assignment, R package version 0.1.0, available at: https://CRAN.R-project.org/package=zeallot (last access: 8 November 2021), 2018.
Trinh, D. H. and Chui, T. F. M.: Assessing the hydrologic restoration of an urbanized area via an integrated distributed hydrological model, Hydrol. Earth Syst. Sci., 17, 4789–4801, https://doi.org/10.5194/hess-17-4789-2013, 2013.
Ushey, K.: RcppRoll: Efficient Rolling/Windowed Operations, R package version 0.3.0, available at: https://CRAN.R-project.org/package=RcppRoll (last access: 8 November 2021), 2018.
Wani, O., Beckers, J. V. L., Weerts, A. H., and Solomatine, D. P.: Residual uncertainty estimation using instance-based learning with applications to hydrologic forecasting, Hydrol. Earth Syst. Sci., 21, 4021–4036, https://doi.org/10.5194/hess-21-4021-2017, 2017.
Yang, Y. and Chui, T. F. M.: Hydrologic Performance Simulation of Green Infrastructures: Why Data-Driven Modelling Can Be Useful?, in: New Trends in Urban Drainage Modelling, 480–484, Springer International Publishing, Cham, Switzerland, 2019.
Yang, Y. and Chui, T. F. M.: Reliability Assessment of Machine Learning Models in Hydrological Predictions through Metamorphic Testing, Water Resour. Res., 57, 1–27, https://doi.org/10.1029/2020wr029471, 2021.
Yong, C. F., McCarthy, D. T., and Deletic, A.: Predicting physical clogging of porous and permeable pavements, J. Hydrol., 481, 48–55, https://doi.org/10.1016/j.jhydrol.2012.12.009, 2013.
Zambrano-Bigiarini, M.: hydroGOF: Goodness-of-fit functions for comparison of simulated and observed hydrological time series R package version 0.4-0, Zenodo [code], https://doi.org/10.5281/zenodo.840087, 2020.
Zeng, X. and Martinez, T. R.: Distribution-balanced stratified cross-validation for accuracy estimation, J. Exp. Theor. Artif. Intell., 12, 1–12, https://doi.org/10.1080/095281300146272, 2000.
Zhang, K. and Chui, T. F. M.: A review on implementing infiltration-based green infrastructure in shallow groundwater environments: Challenges, approaches, and progress, J. Hydrol., 579, 124089, https://doi.org/10.1016/j.jhydrol.2019.124089, 2019.
Zhou, Q.: A Review of Sustainable Urban Drainage Systems Considering the Climate Change and Urbanization Impacts, Water, 6, 976–992, https://doi.org/10.3390/w6040976, 2014.
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...