Flood Forecasting Using Artificial Neural Networks
Date Issued
2020
Author(s)
Fidani, D.,
Gjeshovska, V.,
Petrusheva, S.
Abstract
Floods, as natural disasters, cause huge material damages and
often result in loss of human lives. Their early prediction is necessary in
order to take appropriate actions to reduce economic losses and risks for
people. Albeit there is no universal method for flood modeling, significant
advances in the technology of flood modeling techniques provide
opportunities for flood prediction. Their modeling usually requires a large
amount of data. In cases where only a specific part of the river basin is
explored for more accurate modeling, the time and the effort to implement
such complicated models is not justified. Therefore, the use of intelligent
techniques such as Artificial Neural Networks (ANN) can be a practical
alternative. The purpose of the investigations presented in this paper has
been to make flood forecast for part of the Polog region using ANN. The
forecast has been based on a model developed for this purpose. Modeling
has been performed by use of four artificial neural networks in time series:
Support Vector Machine (SVM), Radial Basis Function Neuron Network
(RBFNN), General Regression Neural Network (GRNN) and Multilayer
Perception (MP). Data on maximum annual flows of Vardar river, recorded
at the Radusha measuring station throughout a period of 58 years, have
been used as an input for the models and the output of the ANN is the
maximum annual flow forecast for a 5-year (1951-2008) period. The results
presented show that the ANN method, in this case the GRNN, can be useful
and can provide sufficient accuracy in solving problems related to
hydrological extremes.
often result in loss of human lives. Their early prediction is necessary in
order to take appropriate actions to reduce economic losses and risks for
people. Albeit there is no universal method for flood modeling, significant
advances in the technology of flood modeling techniques provide
opportunities for flood prediction. Their modeling usually requires a large
amount of data. In cases where only a specific part of the river basin is
explored for more accurate modeling, the time and the effort to implement
such complicated models is not justified. Therefore, the use of intelligent
techniques such as Artificial Neural Networks (ANN) can be a practical
alternative. The purpose of the investigations presented in this paper has
been to make flood forecast for part of the Polog region using ANN. The
forecast has been based on a model developed for this purpose. Modeling
has been performed by use of four artificial neural networks in time series:
Support Vector Machine (SVM), Radial Basis Function Neuron Network
(RBFNN), General Regression Neural Network (GRNN) and Multilayer
Perception (MP). Data on maximum annual flows of Vardar river, recorded
at the Radusha measuring station throughout a period of 58 years, have
been used as an input for the models and the output of the ANN is the
maximum annual flow forecast for a 5-year (1951-2008) period. The results
presented show that the ANN method, in this case the GRNN, can be useful
and can provide sufficient accuracy in solving problems related to
hydrological extremes.
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