Articles | Volume 386
https://doi.org/10.5194/piahs-386-41-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/piahs-386-41-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Post-conference publication
| 
19 Apr 2024
Post-conference publication |
| 19 Apr 2024
| 19 Apr 2024
Performance analysis of physically-based (HEC-RAS, CADDIES) and AI-based (LSTM) flood models for two case studies
Marina Batalini de Macedo
CORRESPONDING AUTHOR
Natural Resources Institute, Federal University of Itajuba, Itajubá, Brazil
Nikunj K. Mangukiya
Department of Hydrology, Indian Institute of Technology Roorkee, Roorke, India
Maria Clara Fava
Institute of Exact and Technological Sciences, Federal University of Viçosa, Rio Paranaíba, Brazil
Ashutosh Sharma
CORRESPONDING AUTHOR
Department of Hydrology, Indian Institute of Technology Roorkee, Roorke, India
Roberto Fray da Silva
Institute of Advanced Studies, University of Sao Paulo, São Carlos, Brazil
Ankit Agarwal
Department of Hydrology, Indian Institute of Technology Roorkee, Roorke, India
Maria Tereza Razzolini
School of Public Health, University of Sao Paulo, São Paulo, Brazil
Eduardo Mario Mendiondo
Sao Carlos School of Engineering, Department of Hydraulics and Sanitation, University of Sao Paulo, São Carlos, Brazil
Narendra K. Goel
Department of Hydrology, Indian Institute of Technology Roorkee, Roorke, India
Mathew Kurian
Water-Energy-Food Nexus, Pennsylvania State University, State College, USA
Adelaide Cássia Nardocci
School of Public Health, University of Sao Paulo, São Paulo, Brazil
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With climate change, societies increasingly need to adapt to deal with more severe droughts and the impacts they can have on food production. To make better adaptation decisions, drought resilience indicators can be used. To build these indicators, surveys with experts can be done. However, designing surveys is a costly process that can influence how experts respond. In this communication, we aim to deal with the challenges encountered in the development of the surveys, to help further research.
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Flow simulation using artificial neural networks is widely used in modeling, particularly in data-scarce areas. Our study utilized MLP neural networks to predict urban runoff in flood-prone basin. Motivated by the vulnerability to floods, we input rainfall and previous runoff data. The model effectively captured basin dynamics, highlighting the impact of urbanization. This research supports urban river basin planning and aids in flood mitigation and adaptation strategies.
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Cited articles
Barros, M. T., Conde, F., Andrioli, C. P., and Zambon, R. C.: Flood Forecasting System in a Mega City: Challenges and Results for the São Paulo Metropolitan Region, in: World Environmental and Water Resources Congress 2016, 10–19, https://doi.org/10.1061/9780784479889.002, 2016.
Batalini de Macedo, M., Fray da Silva, R., Fava, M. C., Sharma, A., K. Mangukiya, N., Sarmento Buarque, A. C., Razzolini, M. T., Mendiondo, E. M., Goel, N. K., Kurian, M., and Nardocci, A. C.: Modelling urban floods in megacities: a comparative bibliometric review of traditional physically based and artificial intelligence models, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 June 2022, IAHS2022-687, https://doi.org/10.5194/iahs2022-687, 2022.
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Guidolin, M., Chen, A. S., Ghimire, B., Keedwell, E. C., Djordjević, S., and Savić, D. A.: A weighted cellular automata 2D inundation model for rapid flood analysis, Environ. Model. Softw., 84, 378–394, https://doi.org/10.1016/j.envsoft.2016.07.008, 2016.
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Mangukiya, N. K., Sharma, A., and Shen, C.: How to enhance hydrological predictions in hydrologically distinct watersheds of the Indian subcontinent?, Hydrol. Process., 37, e14936, https://doi.org/10.1002/hyp.14936, 2023.
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Simas, I. T. H.: Análise retrospectiva de episódios de inundações na Bacia Hidrográfica do Rio Aricanduva – São Paulo [Retrospective analysis of flooding episodes in the Aricanduva River catchment – São Paulo], Thesis, Universidade de São Paulo, São Paulo, https://doi.org/10.11606/D.8.2017.tde-21072017-162915, 2017.
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Short summary
More and more extreme rainfall causes flooding problems in cities and communities, affecting the health and well-being of the population, as well as causing damage to the economy. To help design actions aiming at reducing the impacts of these floods, computational models can be used to simulate their extent. However, there are different types of models currently available. In this study, we evaluated three different models, for a city in Brazil and a region in India, to guide the best use of it.
More and more extreme rainfall causes flooding problems in cities and communities, affecting the...
