Analysis of the use of discrete wavelet transforms coupled with ANN for short-term streamflow forecasting

Freire, Paula K. M. M.; Santos, Celso Augusto Guimarães; Silva, Gustavo Barbosa Lima da

doi:10.1016/j.asoc.2019.04.024

Cited by 98 publications

(26 citation statements)

References 27 publications

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“…number of layers, the neurons per layer, activation functions, and the network topology (Freire, Santos, & da Silva, 2019). In the design of an NN, its explicit features should be suitable for the particular problem at hand.…”

Section: Theorymentioning

confidence: 99%

“…where N is the total number of training patters, e i is the error for the training pattern n and t is the target. The architecture of the ANN is composed of the specified number of layers, the neurons per layer, activation functions, and the network topology (Freire, Santos, & da Silva, 2019). In the design of an NN, its explicit features should be suitable for the particular problem at hand.…”

Section: Introductionmentioning

confidence: 99%

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Comparative analysis of neural network training algorithms for the flood forecast modelling of an alluvial Himalayan river

Tabbussum

Dar

2020

J Flood Risk Management

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This study aimed to develop novel flood forecasting models using artificial neural network (ANN) algorithms. The models were assessed and validated for the case study of an alluvial river in the Kashmir region of the Indian Himalayas-River Jhelum. In September 2014, a major flood occurred in the Kashmir Valley with peak discharge exceeding 115,000 m 3 /s; brought about by the multifarious interaction among atmospheric disturbances. The present study targeted the development of ANN models for flood prognosis. Thereby five neural network models were developed: Bayesian regularisation neural network, the Levenberg-Marquardt neural network, conjugate gradient neural network, scaled conjugate gradient neural network, and resilient backpropagation neural network. Levenberg-Marquardt neural network model, with the mean squared error of 0.002128 (lowest of all models) and coefficient of determination of 95.839% (highest of all models), proved to be the best model based on the statistical validation parameters. All the simulations were evaluated and curves graphed for actual versus predicted discharges for 20% of the data. These models may be used to predict floods, and take advance precautionary measures by Irrigation and Flood Control Department of the State, rather than waiting for flood hydrograph to shoot (norm as of now).

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Section: Theorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparative analysis of neural network training algorithms for the flood forecast modelling of an alluvial Himalayan river

Tabbussum

Dar

2020

J Flood Risk Management

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“…Each of these connections receives a weight, which determines its impact on the cells it connects. Each cell thus has an input, which allows it to receive information from other cells, but also from what is called an activation function, which in the simplest cases is a simple identity of the result obtained by the input and finally an output (Santos & Silva, 2014;Freire et al, 2019;Santos et al, 2019;Honorato et al, 2019).…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Spatial modeling of soil salinity using multiple linear regression, ordinary kriging and artificial neural network methods in the Lower Cheliff plain, Algeria

Noureddine¹,

Achite²,

Santos³

et al. 2019

JUEE

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Soil salinity is one of the most damaging environmental issues worldwide, essentially in arid and semi-arid regions, caused by various factors. Spatial estimation and prediction of salinity is important to predict land evaluation in order to develop and determine leaching factor and the precise management for maximum production. The Lower Cheliff is characterized by the augmentation of rate of soil salinity in 80% of area. In this study, the relationship between both elevation and soil salinity was analysed, giving their role in understanding and estimating the spatial distribution of soil salinity in the Lower Cheliff plain. To conduct this work, 406 samples were taken and analysis of electric conductivity was performed as well as the measurement of the elevation using a GPS. The correlations of soil salinity with elevation were analysed as well. In this study, a great focus on the use of the multiple linear regressions, ordinary kriging and artificial neural network methods was given. The results showed that soil salinity had a good correlation with elevation, and according to the values of coefficient of determination (R 2), root mean square error (RMSE) and mean absolute error (MAE), the superiority of MLP model was implied with the value of R 2 = 0.994, RMSE = 0.63 and MAE = 0.33.

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“…However, in dealing with non-linear situations where data is not a linear function of time, Box-Jenkins methodology is inappropriate [ 3 , 7 , 23 , 33 ]. For accurate forecasting of non-linear data, wavelet analysis is a magnificent tool that is capable of diagnosing high-frequency components in time series data [14,15,17,29,34,36,42] . Discreet wavelet transformation involves decomposition of time series at different scales, and each component series can be treated for forecasting purpose [ 25 , 38 , 40 , 44 , 49-51 , 53 , 59 ].…”

Section: Introductionmentioning

confidence: 99%

Development of new hybrid model of discrete wavelet decomposition and autoregressive integrated moving average (ARIMA) models in application to one month forecast the casualties cases of COVID-19

Singh

Parmar

Kumar

et al. 2020

Chaos, Solitons & Fractals

136

103

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Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre-including this research content-immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

show abstract

Analysis of the use of discrete wavelet transforms coupled with ANN for short-term streamflow forecasting

Cited by 98 publications

References 27 publications

Comparative analysis of neural network training algorithms for the flood forecast modelling of an alluvial Himalayan river

Comparative analysis of neural network training algorithms for the flood forecast modelling of an alluvial Himalayan river

Spatial modeling of soil salinity using multiple linear regression, ordinary kriging and artificial neural network methods in the Lower Cheliff plain, Algeria

Development of new hybrid model of discrete wavelet decomposition and autoregressive integrated moving average (ARIMA) models in application to one month forecast the casualties cases of COVID-19

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