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Wyświetlanie 1-2 z 2
Tytuł:
Streamflow prediction using data-driven models: Case study of Wadi Hounet, northwestern Algeria
Autorzy:
Beddal, Dalila
Achite, Mohammed
Baahmed, Djelloul
Powiązania:
https://bibliotekanauki.pl/articles/1844406.pdf
Data publikacji:
2020
Wydawca:
Instytut Technologiczno-Przyrodniczy
Tematy:
Algeria
back propagation neural network
BPNN
multi linear regression
MLR
streamflow
Wadi Hounet
Opis:
Streamflow modelling is a very important process in the management and planning of water resources. However, complex processes associated with the hydro-meteorological variables, such as non-stationarity, non-linearity, and randomness, make the streamflow prediction chaotic. The study developed multi linear regression (MLR) and back propagation neural network (BPNN) models to predict the streamflow of Wadi Hounet sub-basin in north-western Algeria using monthly hydrometric data recorded between July 1983 and May 2016. The climatological inputs data are rainfall (P) and reference evapotranspiration (ETo) on a monthly scale. The outcomes for both BPNN and MLR models were evaluated using three statistical measurements: Nash–Sutcliffe efficiency coefficient (NSE), the coefficient of correlation (R) and root mean square error (RMSE). Predictive results revealed that the BPNN model exhibited good performance and accuracy in the prediction of streamflow over the MLR model during both training and validation phases. The outcomes demonstrated that BPNN-4 is the best performing model with the values of 0.885, 0.941 and 0.05 for NSE, R and RMSE, respectively. The highest NSE and R values and the lowest RMSE for both training and validation are an indication of the best network. Therefore, the BPNN model provides better prediction of the Hounet streamflow due to its capability to deal with complex nonlinearity procedures.
Źródło:
Journal of Water and Land Development; 2020, 47; 16-24
1429-7426
2083-4535
Pojawia się w:
Journal of Water and Land Development
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Development of a Sound Quality Evaluation Model Based on an Optimal Analytic Wavelet Transform and an Artificial Neural Network
Autorzy:
Pourseiedrezaei, Mehdi
Loghmani, Ali
Keshmiri, Mehdi
Powiązania:
https://bibliotekanauki.pl/articles/1953511.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
analytic wavelet transform
AWT
sound quality evaluation
SQE
psychoacoustic metrics
back propagation neural network
BPNN
Opis:
The purpose of this study was to develop a sound quality model for real time active sound quality control systems. The model is based on an optimal analytic wavelet transform (OAWT) used along with a back propagation neural network (BPNN) in which the initial weights and thresholds are determined by particle swarm optimisation (PSO). In the model the input signal is decomposed into 24 critical bands to extract a feature matrix, based on energy, mean, and standard deviation indices of the sub signal scalogram obtained by OAWT. The feature matrix is fed into the neural network input to determine the psychoacoustic parameters used for sound quality evaluation. The results of the study show that the present model is in good agreement with psychoacoustic models of sound quality metrics and enables evaluation of the quality of sound at a lower computational cost than the existing models.
Źródło:
Archives of Acoustics; 2021, 46, 1; 55-65
0137-5075
Pojawia się w:
Archives of Acoustics
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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