Artificial neural networks applied for flood forecasting in ungauged basin – the Paranaíba river study case

Flow simulation using artificial neural networks (ANNs) in the modelling has been widely applied and has gained prominence in regions lacking data. The hydrological variables are subject to the influence of morphological characteristics and urbanization in the watershed. Statistical models, such as...

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Published in:Proceedings of the International Association of Hydrological Sciences Vol. 386; pp. 81 - 86
Main Authors: Brandão, Abderraman R. A., de Menezes Filho, Frederico C. M., Oliveira, Paulo T. S., Fava, Maria C.
Format: Journal Article Conference Proceeding
Language:English
Published: Gottingen Copernicus GmbH 19.04.2024
Copernicus Publications
Subjects:
Artificial neural networks
Case studies
Correlation analysis
Flood forecasting
Flood predictions
Floods
Flow simulation
Hydrologic data
Hydrology
Machine learning
Multilayer perceptrons
Neural networks
Performance evaluation
Physical characteristics
Precipitation
Software
Statistical models
Urbanization
Variables
United States > US
ISSN:2199-899X, 2199-8981, 2199-899X
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Abstract Flow simulation using artificial neural networks (ANNs) in the modelling has been widely applied and has gained prominence in regions lacking data. The hydrological variables are subject to the influence of morphological characteristics and urbanization in the watershed. Statistical models, such as ANNs, need to be able to identify the relationship between the hydrological inputs and outputs of the model, without explicitly considering the other relationships involved in physical processes. This work aimed to apply a Multilayer Perceptron (MLP) neural network for predicting flows in an urban basin subject to recurrent floods, using precipitation and flow data from previous periods as inputs. After model calibration and validation for the current state of the basin (2018–2019), its responses were analysed using input data before the basin urbanization (1985–1986) to identify the error behaviour at the output as a proxy for the basin changes effect. Its efficiency was evaluated using hydrographs, showing satisfactory results in both periods. In the urbanization period, there is more dispersion for maximum flows. For the day 4 steps back in the current forecast, NSE = 0.59 was observed, whereas in the other period, NSE = 0.70. The evaluation of the models for the current period of basin urbanization showed that the model could capture the basin's physical dynamics within the established static relationship. Also, the result found in the statistical relationships for the inputs showed once again the impact of urbanization on the basin.
AbstractList Flow simulation using artificial neural networks (ANNs) in the modelling has been widely applied and has gained prominence in regions lacking data. The hydrological variables are subject to the influence of morphological characteristics and urbanization in the watershed. Statistical models, such as ANNs, need to be able to identify the relationship between the hydrological inputs and outputs of the model, without explicitly considering the other relationships involved in physical processes. This work aimed to apply a Multilayer Perceptron (MLP) neural network for predicting flows in an urban basin subject to recurrent floods, using precipitation and flow data from previous periods as inputs. After model calibration and validation for the current state of the basin (2018–2019), its responses were analysed using input data before the basin urbanization (1985–1986) to identify the error behaviour at the output as a proxy for the basin changes effect. Its efficiency was evaluated using hydrographs, showing satisfactory results in both periods. In the urbanization period, there is more dispersion for maximum flows. For the day 4 steps back in the current forecast, NSE = 0.59 was observed, whereas in the other period, NSE = 0.70. The evaluation of the models for the current period of basin urbanization showed that the model could capture the basin's physical dynamics within the established static relationship. Also, the result found in the statistical relationships for the inputs showed once again the impact of urbanization on the basin.
Author de Menezes Filho, Frederico C. M.
Fava, Maria C.
Brandão, Abderraman R. A.
Oliveira, Paulo T. S.
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StartPage 81
SubjectTerms Artificial neural networks
Case studies
Correlation analysis
Flood forecasting
Flood predictions
Floods
Flow simulation
Hydrologic data
Hydrology
Machine learning
Multilayer perceptrons
Neural networks
Performance evaluation
Physical characteristics
Precipitation
Software
Statistical models
Urbanization
Variables
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Title Artificial neural networks applied for flood forecasting in ungauged basin – the Paranaíba river study case
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