M. GUESRI Mourad

MCB

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Department

BASE COMMON ST Departement ST

Research Interests

Hydrology Water Science GIS Hydraulics Artificial intelligence

Contact Info

University of M'Sila, Algeria

Email : mourad.guesri@univ-msila.dz

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Recent Publications

2024-12-16

Evaluation de la qualité physico-chimique et bactériologique des eaux usées épurées de la STEP de M'sila

Les eaux usées dans les zones urbaines contiennent une quantité importante de polluants provenant des eaux usées domestiques, industrielle. Pour protéger simultanément la santé publique des habitants et préserver l’écosystème naturel ; l’installation d’une station d'épuration joue le rôle d'assainir ces eaux usées avant de les rejeter dans le milieu naturel et leur réutilisation dans l’irrigation des espaces verts et des terres agricoles. La STEP de M’sila mise en service en 2013 traite quotidiennement 48000 m3 d’eau usée provenant de l’agglomération de M’sila et sa zone industrielle. L’objectif de ce travail est de caractériser la qualité physico-chimique et bactériologique des eaux usées épurées de la Station de M’sila afin d'évaluer l'efficacité des processus utilisés dans cette station, y compris le processus de boues activées. Les résultats obtenus révèlent une efficacité de dégradation de la matière organique (MES de 03 mg/l, DBO5 de 07mg/l et DCO de 45 mg/l) par rapport aux normes Algériennes. Les résultats bactériologiques montrent l’absence des anaérobies sulfito-rédacteurs, et une charge très forte en germes totaux, en coliformes avec présence de Staphylococcus Aureus. De plus, on a noté de grandes quantités de métaux lourds. A cet effet, des traitements complémentaires doivent être envisagés pour réduire considérablement les métaux lourds et la charge bactériologique afin de protéger la santé publique contre les maladies à transmission hydrique.
Citation

M. GUESRI Mourad, (2024-12-16), "Evaluation de la qualité physico-chimique et bactériologique des eaux usées épurées de la STEP de M'sila", [international] natural risks, urban territory managment and analysis tools , msila university

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2024-10-14

Comparative soft computing approaches for discharge modeling in a semi-arid region: Case study of some wadis of the Macta basin (Northweste of Algeria)

In North Africa, water resources are precious and scarce due to the insufficiency of perpetual wadis and variability over time between periods of extreme rain and desiccation. As a result, The development of accurate discharge models can be helpful in the simulation and the prediction in several water resources fields such as management and planning of water resources. The usage of soft computing techniques has become necessary to solve problems associated with non linear methods. The main objective of this work is to simulate the discharge of some wadis of Macta basin by using two different data-driven models, namely BPNN and MLR widely applied for discharge simulation in hydrology. The Macta basin is located in northwestern of Algeria, It covers an area of approximately 14,389 km2.The Macta watershed is composed of sixteen (16) sub-basins drained by temporary wadis of total length equal to 8115 Km. The climate of the Macta basin is semi-arid. Different combinations were attempted to simulate the target variable (Q) which is monthly hydrometric data recorded between July 1983 and May 2016. The climatological inputs data are rainfall (P) and reference evapotranspiration (ETo) on a monthly scale including different lag time series of input data (P, Pt–1, ETo, ETot–1, Qt–1). The outcomes for BPNN and MLR were evaluated using three statistical measurements: Nash–Sutcliffe efficiency coefficient (NSE), the coefficient of correlation (R) and root mean square error (RMSE). The results revealed that the BPNN model exhibited good performance and accuracy in the simulation of discharge during both training and validation phases. The outcomes demonstrated that BPNN-4 is the best performing model with the values of 0.885, 0.941 and 0.05 for NSE, R and RMSE, respectively. The highest NSE and R values and the lowest RMSE for both training and validation are an indication of the best network. Therefore, the BPNN model provides better prediction due to its capability to deal with complex nonlinearity procedures. However, the MLR model presents a low predictive accuracy because of the linearity of the input–output relation.
Citation

M. GUESRI Mourad, BAAHMED Djelloul, , (2024-10-14), "Comparative soft computing approaches for discharge modeling in a semi-arid region: Case study of some wadis of the Macta basin (Northweste of Algeria)", [national] eau, environnement et énergie renouvelable , msila university

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