Abstract
Sensitivity analysis of urban flood model parameters is important for urban flood simulation. Efficient and accurate acquisition of sensitive parameters is the key to real-time model calibration. In order to quickly obtain the sensitive runoff parameters of the urban flood simulation model, this study proposes an artificial neural network-based identification method for sensitive parameters. Artificial neural network (ANN) models were constructed with the binary classification and multi-classification methods, and used environmental indicators that affect the parameter sensitivity of different hydrological response units as the input, with the sensitivity parameters of the Storm water management model (SWMM) being the output. The optimization of the ANN was realized by adjusting the number of nodes in the hidden layer and the maximum number of iterations. An example application was conducted in Zhengzhou, China. The results show that the binary classification ANN quickly identified sensitive parameters, and the prediction accuracy of all parameters exceeded 96%. Convergence can be achieved when the number of nodes in the hidden layer does not exceed twice the number of input nodes, and the maximum number of iterations does not exceed 200. Rapid and accurate identification of the sensitive runoff parameters of the urban flood simulation model was achieved, which reduced the time required for parameter sensitivity analysis.
Similar content being viewed by others
References
Abebe Y, Kabir G, Tesfamariam S (2018) Assessing Urban Areas Vulnerability to Pluvial Flooding Using Gis Applications and Bayesian Belief Network Model. J Clean Prod 174:1629–1641. https://doi.org/10.1016/j.jclepro.2017.11.066
Aryal SK, Ashbolt S, McIntosh BS, Petrone KP, Maheepala S, Chowdhury RK, Gardener T, Gardiner R (2016) Assessing and Mitigating the Hydrological Impacts of Urbanisation in Semi-Urban Catchments Using the Storm Water Management Model. Water Resour Manage 30:5437–5454. https://doi.org/10.1007/s11269-016-1499-z
Barco J, Wong KM, Stenstrom MK (2008) Automatic Calibration of the Us Epa Swmm Model for a Large Urban Catchment. Journal of Hydraulic Engineering-Asce 134:466–474. https://doi.org/10.1061/(asce)0733-9429(2008)134:4(466)
Bates PD, Horritt MS, Fewtrell TJ (2010) A Simple Inertial Formulation of the Shallow Water Equations for Efficient Two-Dimensional Flood Inundation Modelling. J Hydrol 387:33–45. https://doi.org/10.1016/j.jhydrol.2010.03.027
Beling FA, Garcia J, Paiva E, Bastos G, Paiva J (2011) Analysis of the SWMM model parameters for runoff evaluation in periurban basins from southern Brazil. 12th International Conference on Urban Drainage
Cao XJ, Lyu H, Ni GH, Tian FQ, Ma Y, Grimmond CSB (2020) Spatial Scale Effect of Surface Routing and Its Parameter Upscaling for Urban Flood Simulation Using a Grid-Based Model. Water Resour Res 56:22. https://doi.org/10.1029/2019wr025468
Chen JF, Li Q, Wang HM, Deng MH (2020) A Machine Learning Ensemble Approach Based on Random Forest and Radial Basis Function Neural Network for Risk Evaluation of Regional Flood Disaster: A Case Study of the Yangtze River Delta, China. Int J Environ Res Public Health 17(21):49. https://doi.org/10.3390/ijerph17010049
Di Lazzaro M, Zarlenga A, Volpi E (2015) Hydrological effects of within-catchment heterogeneity of drainage density. Adv Water Resour 76:157–67. https://doi.org/10.1016/j.advwatres.2014.12.011
Fletcher TD, Andrieu H, Hamel P (2013) Understanding, Management and Modelling of Urban Hydrology and Its Consequences for Receiving Waters: A State of the Art. Adv Water Resour 51:261–279. https://doi.org/10.1016/j.advwatres.2012.09.001
Francos A, Elorza FJ, Bouraoui F, Bidoglio G, Galbiati L (2003) Sensitivity analysis of distributed environmental simulation models: understanding the model behaviour in hydrological studies at the catchment scale. Reliab Eng Syst Saf 79(2):205–218. https://doi.org/10.1016/s0951-8320(02)00231-4
Gudiyangada T, Sepideh TP, Khalil G, Omid G, Thomas B (2020) Flood Susceptibility Mapping with Machine Learning, Multi-Criteria Decision Analysis and Ensemble Using Dempster Shafer Theory. Journal of Hydrology 590. https://doi.org/10.1016/j.jhydrol.2020.125275
Hallegatte S, Green C, Nicholls RJ, Corfee-Morlot J (2013) Future Flood Losses in Major Coastal Cities. Nat Clim Chang 3:802–806. https://doi.org/10.1038/nclimate1979
Hidayat S, Soekarno S (2020) Sensitivity Analysis of Surface Runoff Parameters Towards Peak Discharge and Flood Volume. IOP Conference Series: Earth and Environmental Science 451: 012083 (7 pp.)-83 (7 pp.). https://doi.org/10.1088/1755-1315/451/1/012083
Huang JK, Lee KT (2009) Influences of spatially heterogeneous roughness on flow hydrographs. Adv Water Resour 32:1580–1587. https://doi.org/10.1016/j.advwatres.2009.08.002
Jiang Y, Liu CM, Li XY, Liu LF, Wang HR (2015) Rainfall-Runoff Modeling, Parameter Estimation and Sensitivity Analysis in a Semiarid Catchment. Environ Model Softw 67:72–88. https://doi.org/10.1016/j.envsoft.2015.01.008
Ke Q, Xin T, Jeremy B, Zhan T, Junguo L (2020) Urban Pluvial Flooding Prediction by Machine Learning Approaches – a Case Study of Shenzhen City, China. Advances in Water Resources 103719. https://doi.org/10.1016/j.advwatres.2020.103719
Knighton J, Lennon E, Bastidas L, White E (2016) Stormwater Detention System Parameter Sensitivity and Uncertainty Analysis Using Swmm. J Hydrol Eng 21:15. https://doi.org/10.1061/(asce)he.1943-5584.0001382
Knighton J, White E, Lennon E, Rajan R (2014) Development of Probability Distributions for Urban Hydrologic Model Parameters and a Monte Carlo Analysis of Model Sensitivity. Hydrol Process 28:5131–5139. https://doi.org/10.1002/hyp.10009
Kundzewicz ZW, Kanae S, Seneviratne SI, Handmer J, Nicholls N, Peduzzi P, Mechler R (2014) Flood Risk and Climate Change: Global and Regional Perspectives. Hydrological Sciences Journal Des Sciences Hydrologiques 59:1–28. https://doi.org/10.1080/02626667.2013.857411
LeCun Y, Bengio Y, Hinton G (2015) Deep Learning. Nature 521:436–444. https://doi.org/10.1038/nature14539
Luan B, Yin RX, Xu P, Wang X, Yang XM, Zhang L, Tang XY (2019) Evaluating Green Stormwater Infrastructure Strategies Efficiencies in a Rapidly Urbanizing Catchment Using Swmm-Based Topsis. J Clean Prod 223:680–691. https://doi.org/10.1016/j.jclepro.2019.03.028
Rossman LA (2009) Storm water management model: User’s manual version S. O [EB/OL]. http://www.epa.Gov/ednnrmed/models/sum/epaswmm5_manual.pdf
Rui X, Chengyu J, Qingjin C, Xiaoyan D (2015) Principle Analysis and Application of Storm Water Management Model on Stimulating Rainfall-Runoff. Advances in Science and Technology of Water Resources 35:1–5
Salas J, Yepes V (2018) Urban Vulnerability Assessment: Advances from the Strategic Planning Outlook. J Clean Prod 179:544–558. https://doi.org/10.1016/j.jclepro.2018.01.088
Schmidhuber J (2015) Deep Learning in Neural Networks: An Overview. Neural Networks 61: 85–117. https://doi.org/10.1016/j.neunet.2014.09.003
She L, You XY (2019) A Dynamic Flow Forecast Model for Urban Drainage Using the Coupled Artificial Neural Network. Water Resour Manage 33:3143–3153. https://doi.org/10.1007/s11269-019-02294-9
Sivapalan M, Beven K, Wood EF (1987) On Hydrologic Similarity. 2. A Scaled Model of Storm Runoff Production. Water Resour Res 23:2266–2278. https://doi.org/10.1029/WR023i012p02266
Stevens M (2012) Cities and Flooding: A Guide to Integrated Urban Flood Risk Management for the 21st Century. J Reg Sci 52:885–887. https://doi.org/10.1111/jors.12006_6
Sun N, Hong BG, Hall M (2014) Assessment of the Swmm Model Uncertainties within the Generalized Likelihood Uncertainty Estimation (Glue) Framework for a High- Resolution Urban Sewershed. Hydrol Process 28:3018–3034. https://doi.org/10.1002/hyp.9869
Tsai LY, Chen CF, Fan CH, Lin JY (2017) Using the Hspf and Swmm Models in a High Pervious Watershed and Estimating Their Parameter Sensitivity. Water 9:1–16. https://doi.org/10.3390/w9100780
Wu ZN, Zhou YH, Wang HL, Jiang ZH (2020) Depth Prediction of Urban Flood under Different Rainfall Return Periods Based on Deep Learning and Data Warehouse. Sci Total Environ 716:1–11. https://doi.org/10.1016/j.scitotenv.2020.137077
Wu Z, Lv H, Meng Y, Guan X, Zang Y (2021) The determination of flood damage curve in areas lacking disaster data based on the optimization principle of variation coefficient and beta distribution. Sci Total Environ 750:142277. https://doi.org/10.1016/j.scitotenv.2020.142277
Yao X (1999) Evolving artificial neural networks. Proc IEEE 87:1423–1447. https://doi.org/10.1109/5.784219
Xu ZX, Xiong LJ, Li HZ, Xu J, Cai X, Chen KL, Wu J (2019) Runoff Simulation of Two Typical Urban Green Land Types with the Stormwater Management Model (Swmm): Sensitivity Analysis and Calibration of Runoff Parameters. Environ Monit Assess 191:16343. https://doi.org/10.1007/s10661-019-7445-9
Yu DP, Coulthard TJ (2015) Evaluating the Importance of Catchment Hydrological Parameters for Urban Surface Water Flood Modelling Using a Simple Hydro-Inundation Model. J Hydrol 524:385–400. https://doi.org/10.1016/j.jhydrol.2015.02.040
Zaghloul NA, Abu Kiefa MA (2001) Neural Network Solution of Inverse Parameters Used in the Sensitivity-Calibration Analyses of the Swmm Model Simulations. Adv Eng Softw 32:587–595. https://doi.org/10.1016/s0965-9978(00)00072-7
Zhang D, Lindholm G, Ratnaweera H (2018) Use Long Short-Term Memory to Enhance Internet of Things for Combined Sewer Overflow Monitoring. J Hydrol 556:409–418. https://doi.org/10.1016/j.jhydrol.2017.11.018
Zhang W, Li T (2015) The Influence of Objective Function and Acceptability Threshold on Uncertainty Assessment of an Urban Drainage Hydraulic Model with Generalized Likelihood Uncertainty Estimation Methodology. Water Resour Manage 29:2059–2072. https://doi.org/10.1007/s11269-015-0928-8
Zhao G, Pang B, Xu ZX, Peng DZ, Xu LY (2019) Assessment of Urban Flood Susceptibility Using Semi-Supervised Machine Learning Model. Sci Total Environ 659:940–949. https://doi.org/10.1016/j.scitotenv.2018.12.217
Zhu ZH, Chen ZH, Chen XH, He PY (2016) Approach for Evaluating Inundation Risks in Urban Drainage Systems. Sci Total Environ 553:1–12. https://doi.org/10.1016/j.scitotenv.2016.02.025
Funding
This research was funded by the Key Program of National Natural Science Foundation of China (Grant No: 51739009) and the National Natural Science Foundation for Young Scientists of China (Grant No. 51909240). The authors thank the anonymous reviewers for their valuable comments.
Author information
Authors and Affiliations
Contributions
Ma, Methodology, Validation, Writing original draft, Visualization. Z Wu, Conceptualization, Project administration, Supervision. H Wang & H Lv, Writing original draft, Visualization. C Hu & X Zhang, Conceptualization, Writing review & editing, Funding acquisition. All authors have read and approved the final manuscript.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that there is no conflict of interest regarding the publication of this paper.
Ethical Approval
Animal experiments were performed in accordance with protocol approved by the Service de la Consommation et des Affaires vtrinaires of Canton de Vaud (VD 1541.4 and VD 1865.3). NOD.Cg-Prkdcscid Il2rgtm1Wjl/SzJ mice (NSG) breeders were purchased from Jackson Laboratories
Consent to participate
The cantonal ethics committee approved the study on patient samples (183/10). Informed consent was obtained from all subjects.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Wu, Z., Ma, B., Wang, H. et al. Identification of Sensitive Parameters of Urban Flood Model Based on Artificial Neural Network. Water Resour Manage 35, 2115–2128 (2021). https://doi.org/10.1007/s11269-021-02825-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11269-021-02825-3