Urban flood forecasting with multi-output neural networks: a physically-based and data-driven approach
ABSTRACT Physically based models for spatial flood prediction are time and computationally expensive. Data-driven models, while faster, require large amounts of data for adjustment. This study presents an original methodology combining these two approaches, using a physically-based model (HEC-RAS 2D...
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Associação Brasileira de Recursos Hídricos
2025-05-01
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| Series: | Revista Brasileira de Recursos Hídricos |
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| Online Access: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S2318-03312025000100216&lng=en&tlng=en |
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| author | Renata Bulling Magro Olavo Correa Pedrollo Silvio Luis Rafaeli Neto |
| author_facet | Renata Bulling Magro Olavo Correa Pedrollo Silvio Luis Rafaeli Neto |
| author_sort | Renata Bulling Magro |
| collection | DOAJ |
| description | ABSTRACT Physically based models for spatial flood prediction are time and computationally expensive. Data-driven models, while faster, require large amounts of data for adjustment. This study presents an original methodology combining these two approaches, using a physically-based model (HEC-RAS 2D), adjusted with known events, to generate water depth data at control points and multi-output artificial neural networks (ANNs) for flood forecasting at these points. The performance of the ANN in this research, with application to the urban area of Lages-SC, southern Brazil, resulted in average mean absolute errors of 3.9, 9.8, and 46 cm, with corresponding Nash-Sutcliffe coefficients of 0.99, 0.98, and 0.75 at lead times of 3 h, 8 h, and 20 h, respectively. Multi-output ANNs exhibited greater robustness compared to single-output ANNs for spatial flood prediction. The methodology is suitable for developing models for spatial predictions of urban flooding, with sufficient agility to take necessary measures. |
| format | Article |
| id | doaj-art-e9a7f0ca5e914364a65717b1d2d5f56b |
| institution | Kabale University |
| issn | 2318-0331 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Associação Brasileira de Recursos Hídricos |
| record_format | Article |
| series | Revista Brasileira de Recursos Hídricos |
| spelling | doaj-art-e9a7f0ca5e914364a65717b1d2d5f56b2025-08-20T03:48:19ZengAssociação Brasileira de Recursos HídricosRevista Brasileira de Recursos Hídricos2318-03312025-05-013010.1590/2318-0331.302520240089Urban flood forecasting with multi-output neural networks: a physically-based and data-driven approachRenata Bulling Magrohttps://orcid.org/0000-0003-2433-100XOlavo Correa Pedrollohttps://orcid.org/0000-0001-7264-0259Silvio Luis Rafaeli Netohttps://orcid.org/0000-0002-6640-1961ABSTRACT Physically based models for spatial flood prediction are time and computationally expensive. Data-driven models, while faster, require large amounts of data for adjustment. This study presents an original methodology combining these two approaches, using a physically-based model (HEC-RAS 2D), adjusted with known events, to generate water depth data at control points and multi-output artificial neural networks (ANNs) for flood forecasting at these points. The performance of the ANN in this research, with application to the urban area of Lages-SC, southern Brazil, resulted in average mean absolute errors of 3.9, 9.8, and 46 cm, with corresponding Nash-Sutcliffe coefficients of 0.99, 0.98, and 0.75 at lead times of 3 h, 8 h, and 20 h, respectively. Multi-output ANNs exhibited greater robustness compared to single-output ANNs for spatial flood prediction. The methodology is suitable for developing models for spatial predictions of urban flooding, with sufficient agility to take necessary measures.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S2318-03312025000100216&lng=en&tlng=enUrban floodingHydrodynamic modelingComputational intelligenceForecasting model |
| spellingShingle | Renata Bulling Magro Olavo Correa Pedrollo Silvio Luis Rafaeli Neto Urban flood forecasting with multi-output neural networks: a physically-based and data-driven approach Revista Brasileira de Recursos Hídricos Urban flooding Hydrodynamic modeling Computational intelligence Forecasting model |
| title | Urban flood forecasting with multi-output neural networks: a physically-based and data-driven approach |
| title_full | Urban flood forecasting with multi-output neural networks: a physically-based and data-driven approach |
| title_fullStr | Urban flood forecasting with multi-output neural networks: a physically-based and data-driven approach |
| title_full_unstemmed | Urban flood forecasting with multi-output neural networks: a physically-based and data-driven approach |
| title_short | Urban flood forecasting with multi-output neural networks: a physically-based and data-driven approach |
| title_sort | urban flood forecasting with multi output neural networks a physically based and data driven approach |
| topic | Urban flooding Hydrodynamic modeling Computational intelligence Forecasting model |
| url | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S2318-03312025000100216&lng=en&tlng=en |
| work_keys_str_mv | AT renatabullingmagro urbanfloodforecastingwithmultioutputneuralnetworksaphysicallybasedanddatadrivenapproach AT olavocorreapedrollo urbanfloodforecastingwithmultioutputneuralnetworksaphysicallybasedanddatadrivenapproach AT silvioluisrafaelineto urbanfloodforecastingwithmultioutputneuralnetworksaphysicallybasedanddatadrivenapproach |