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Wastewater reuse and mapping of irrigable soils: Case of Sidi Bel Abbes City, Algeria

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Mahfoud Z. , Khaldi A. , Korichi K.

Journal of Water and Land Development

2020

tom no. 46

153--161

This work aims to evaluate the treated wastewater from the activated sludge treatment plant in the City of Sidi Bel Abbes (North-Western Algeria) which is required for reuse in irrigation. The control of irrigated areas downstream is done based on a pedological study. Physico-chemical analysis such as (pH, BOD5, COD and SS) indicate results in Algerian and international standards required by the WHO. The Sodium Adsorption Ratio and Electrical Conductivity values of the treated wastewater belong to the C3-S1 class. The treated wastewater has a fairly good microbiological quality that meets Algerian standards. The helminth eggs are practically absent. The concentrations of heavy metals are much lower than the limits prescribed in the Algerian decrees. Therefore, the overall processing plant efficiency is satisfactory and has the characteristics of a good treated water quality for reuse in the field of irrigation while protecting the environment. The pedological study of the soil samples shows that the most dominant fraction is undeveloped calcimagnetic. The planned irrigation plain covers an area of about two thousand hectares. Depending on the crops to irrigate; the development and nature of the necessary or recommended improvements, the proposed irrigation perimeter could be classified into five categories in which only three categories are irrigable. Water projects have been proposed to ensure the irrigation of three subdivided sectors.

Artificial Neural Networks vs Long Short-Term Memory Prediction of Solid Flow in Tafna Basin (North-West Algeria)

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Medfouni M. N. , Korichi K. , Marouf N.

2024

tom Vol. 25, iss. 3

275--286

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.