- Tytuł:
- Missing Precipitation Data Estimation Using Long Short-Term Memory Deep Neural Networks
- Autorzy:
- Djerbouai, Salim
- Powiązania:
- https://bibliotekanauki.pl/articles/2086428.pdf
- Data publikacji:
- 2022
- Wydawca:
- Polskie Towarzystwo Inżynierii Ekologicznej
- Tematy:
-
Hodna
K'sob basin
missing precipitation data
long short-term memory
CCWM
coefficient of correlation weighting method
IDWM
inverse distance weighting method - Opis:
- Due to the spatiotemporal variability of precipitation and the complexity of physical processes involved, missing precipitation data estimation remains as a significant problem. Algeria, like other countries in the world, is affected by this problem. In the present paper, Long Short-Term Memory (LSTM) deep neural Networks model was tested to estimate missing monthly precipitation data. The application was presented for the K'sob basin, Algeria. In the present paper, the optimal architecture of LSTM model was adjusted by trial-and-error-procedure. The LSTM model was compared with the most widely used classical methods including inverse distance weighting method (IDWM) and the coefficient of correlation weighting method (CCWM). Finally, it was concluded that the LSTM model performed better than the other methods.
- Źródło:
-
Journal of Ecological Engineering; 2022, 23, 5; 216--225
2299-8993 - Pojawia się w:
- Journal of Ecological Engineering
- Dostawca treści:
- Biblioteka Nauki
Artykuł