Tool for fast assessment of stormwater flood volumes for urban catchment: A machine learning approach

• Published in: Journal of environmental management Vol. 355; p. 120214
• Main Authors: Szeląg, Bartosz, Majerek, Dariusz, Eusebi, Anna Laura, Kiczko, Adam, de Paola, Francesco, McGarity, Arthur, Wałek, Grzegorz, Fatone, Francesco
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01-03-2024
• Subjects: Algorithms; Cities; City Planning; Floods; Global sensitivity analysis; Machine learning; Models, Theoretical; Neural Networks, Computer; Rain; Specific flood volume; SWMM; Water Movements; Global sensitivity analysis; SWMM; Specific flood volume; Machine learning
• ISSN: 0301-4797; 1095-8630
• DOI: 10.1016/j.jenvman.2024.120214

Specific flood volume is an important criterion for evaluating the performance of sewer networks. Currently, mechanistic models - MCMs (e.g., SWMM) are usually used for its prediction, but they require the collection of detailed information about the characteristics of the catchment and sewer network, which can be difficult to obtain, and the process of model calibration is a complex task. This paper presents a methodology for developing simulators to predict specific flood volume using machine learning methods (DNN - Deep Neural Network, GAM - Generalized Additive Model). The results of Sobol index calculations using the GSA method were used to select the ML model as an alternative to the MCM model. It was shown that the DNN model can be used for flood prediction, for which high agreement was obtained between the results of GSA calculations for rainfall data, catchment and sewer network characteristics, and calibrated SWMM parameters describing land use and sewer retention. Regression relationships (polynomials and exponential functions) were determined between Sobol indices (retention depth of impervious area, correction factor of impervious area, Manning's roughness coefficient of sewers) and sewer network characteristics (unit density of sewers, retention factor - the downstream and upstream of retention ratio) obtaining R2 = 0. 55–0.78. The feasibility of predicting sewer network flooding and modernization with the DNN model using a limited range of input data compared to the SWMM was shown. The developed model can be applied to the management of urban catchments with limited access to data and at the stage of urban planning.
•ML simulator of specific flood volume as an alternative to the SWMM model.•Multi-criteria selection of machine learning method for flooding simulation.•Sobol indices prediction in GSA via empirical models.•Correlation between Sobol indices and characteristic of the catchment and sewer.