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Missing Precipitation Data Estimation Using Long Short-Term Memory Deep Neural Networks
 
 
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CEHSD Laboratory, Hydraulics Department, University of M’sila, Ichebila, P.O. Box 166, 28000 M’sila, Algeria
CORRESPONDING AUTHOR
Salim Djerbouai   

CEHSD Laboratory, Hydraulics Department, University of M’sila, Ichebila, P.O. Box 166, 28000 M’sila, Algeria
Publication date: 2022-05-01
 
J. Ecol. Eng. 2022; 23(5):216–225
 
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ABSTRACT
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. Keywords: Hodna, K’sob basin, Missing precipitation data, Long Short-Term Memory, CCWM, IDWM.