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- Title
Artificial Neural Networks vs Long Short-Term Memory Prediction of Solid Flow in Tafna Basin (North-West Algeria).
- Authors
Medfouni, Mohamed Nadjib; Korichi, Khaled; Marouf, Nadir
- Abstract
The main objective of this work is to select the most reliable machine learning model to predict the generated solid flow in the Tafna basin (North-West of Algeria). It is about the artificial neural networks (ANN) and long short-term memory (LSTM). The sediment load is recorded through three hydrometric stations. The efficiency and performance of the two models is verified using the correlation coefficient (R²), the Nash-Sutcliffe coefficient (NSC) and the root mean square error (RMSE). The obtained simulated solids load shows a very good correlation in terms of precision although the ANN model gave relatively better results compared to the LSTM model where low RMSE values were recorded, which confirms that the artificial intelligence models remain also effective for the treatment and the prediction of hydrological phenomena such as the estimation of the solid load in a such watershed.
- Subjects
ALGERIA; ARTIFICIAL neural networks; MACHINE learning; SEDIMENTS; HYDRAULIC measurements
- Publication
Ecological Engineering & Environmental Technology (EEET), 2024, Vol 25, Issue 3, p275
- ISSN
2719-7050
- Publication type
Article
- DOI
10.12912/27197050/181208