Abstract
One of the actions to mitigate the impacts of hydrological extremes is to issue warnings as far in advance as possible. This article reports the application of neural networks for water level forecast to three small watersheds in Brazil that are susceptible to flash floods. First, the physical characteristics and land use and cover maps of the watersheds were surveyed. Next, Multilayer Perceptrons were trained with observed water level and rainfall data covering the period 2014 to 2022 to make water level forecasts 1, 2 and 3 h in advance. To design the neural networks, different combinations of activation functions in the hidden and output layers were tested and also variations in the number of neurons in the hidden layer. The neural networks forecasts for the three watersheds test data were quite good for the three forecast horizons, highlighting the forecasts 3 h in advance that reached a Nash–Sutcliffe index greater than 0.9. In future work, neural networks will be trained with rainfall estimates obtained from numerical weather forecast models data and observed rainfall data, enabling their operational use in the National Center for Monitoring and Early Warning of Natural Disasters situation room. The operational neural models can semi-automate the flash flood warning process for the studied watersheds. As a result, the warnings effectiveness, concerning the advance-assertiveness trade-off, is expected to improve.
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Data availability
Free access to the data used in this study can be obtained by contacting corresponding author Glauston R. T. de Lima (glauston.lima@cemaden.gov.br).
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Acknowledgements
Author Luiz Ferreira de Aguiar Filho thanks CNPq (National Council for Scientific and Technological Development of Brazil) for grant 800108/2022-1.
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G. R. T. L. wrote and reviewed the various versions of the paper; set up the databases; supervised the implementation of the experiments carried out with the neural networks; did the literature review.
R. O. C. collaboratted in the elaboration of relief and land use and cover maps; database organization; collaborated in the revision of various versions of the paper.
L. F. A. F. collaboratted in the elaboration of relief and land use and cover maps; collaborated in the implementation of the experiments carried out with the neural networks.
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Communicated by: H. Babaie
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de Lima, G.R.T., de Oliveira Caran, R. & de Aguiar Filho, L.F. Use of neural network as a support tool in water level forecasting and issuing flash floods early warnings to three small Brazilian urban watersheds. Earth Sci Inform 16, 4313–4326 (2023). https://doi.org/10.1007/s12145-023-01159-5
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DOI: https://doi.org/10.1007/s12145-023-01159-5