- Tytuł:
- Weather drought index prediction using the support vector regression in the Ansegmir Watershed, Upper Moulouya, Morocco
- Autorzy:
-
Bekri, My Hachem
El Hmaidi, Abdellah
Ousmana, Habiba
El Faleh, El Mati
Berrada, Mohamed
El Aissaoui, Kamal
Essahlaoui, Ali
El Ouali, Abdelhadi - Powiązania:
- https://bibliotekanauki.pl/articles/1841959.pdf
- Data publikacji:
- 2021
- Wydawca:
- Instytut Technologiczno-Przyrodniczy
- Tematy:
-
Ansgemir watershed
drought
forecast
modelling
standardized precipitation index
SPI
standardized precipitation-evapotranspiration index
SPEI
support vectors regression
SVR - Opis:
- The purpose of this study is to develop mathematical models based on artificial intelligence: Models based on the support vectors regression (SVR) for drought forecast in the Ansegmir watershed (Upper Moulouya, Morocco). This study focuses on the prediction of the temporal aspect of the two drought indices (standardized precipitation index – SPI and standardized precipitation-evapotranspiration index – SPEI) using six hydro-climatic variables relating to the period 1979–2013. The model SVR3-SPI: RBF, ε = 0.004, C = 20 and γ = 1.7 for the index SPI, and the model SVR3-SPEI: RBF ε = 0.004, C = 40 and γ = 0.167 for the SPEI index are significantly better in comparison to other models SVR1, SVR2 and SVR4. The SVR model for the SPI index gave a correlation coefficient of R = 0.92, MSE = 0.17 and MAE = 0.329 for the learning phase and R = 0.90, MSE = 0.18 and MAE = 0.313 for the testing phase. As for the SPEI index, the overlay is slightly poorer only in the case of the SPI index between the observed values and the predicted ones by the SVR model. It shows a very small gap between the observed and predicted values. The correlation coefficients R = 0.88 for the learning, R = 0.86 for testing remain higher and corresponding to a quadratic error average MSE = 0.21 and MAE = 0.351 for the learning and MSE = 0.21 and MAE = 0.350 for the testing phase. The prediction of drought by SVR model remain useful and would be extremely important for drought risk management.
- Źródło:
-
Journal of Water and Land Development; 2021, 50; 187-194
1429-7426
2083-4535 - Pojawia się w:
- Journal of Water and Land Development
- Dostawca treści:
- Biblioteka Nauki
Artykuł