http://depot.umc.edu.dz/handle/123456789/13473 Tue, 02 Jun 2026 21:15:51 GMT 2026-06-02T21:15:51Z http://depot.umc.edu.dz/handle/123456789/13595 GROUNDWATER RECHARGE OF AQUIFERS IN SEMI-ARID ZONE: EXAMPLE FROM THE KSOUR RIDGE, NW ALGERIA YOUSFI, Somia; KERZABI, Rachid; MANSOUR, Hamidi; MUDRY, Jacques In a recharge area characterized by a semiarid or an arid climate, therefore a low annual rainfall, effective rainfall is the hardiest item to estimate, as it was demonstrated by classical monthly hydrological balance computations. Tue, 24 Nov 2015 00:00:00 GMT http://depot.umc.edu.dz/handle/123456789/13595 2015-11-24T00:00:00Z http://depot.umc.edu.dz/handle/123456789/13593 EVALUATION DE LA QUALITE PHYSICO CHIMIQUE DES EAUX DE L'OUED EL KEBIR ET DE SES PRINCIPAUX affluents YALLES SATHA, A; SAMRAOUI, B En Algérie, Le problème de l’eau est aggravé ces dernières années par une sécheresse qui a territoire de notre pays, et qui a montré (REMINI, 2010). Tue, 24 Nov 2015 00:00:00 GMT http://depot.umc.edu.dz/handle/123456789/13593 2015-11-24T00:00:00Z http://depot.umc.edu.dz/handle/123456789/13591 HYDRODYNAMISME ET HYDROCHIMIE DES EAUX DE LA NAPPE ALLUVIALE D’OUED NIL (WILAYA DE JIJEL, NE ALGERIEN) MAHDID, S; DEBIECHE, T.H; CHABOUR, N La plaine alluviale d’oued Nil présente un grand potentiel hydrique, vu sa pluviométrie élevée (1200 mm/an) et l’épaisseur important (70 m) de son aquifère. Une quarantaine de forages sont implantés au niveau de la plaine pour répondre aux besoins en eaux potable de plusieurs agglomérations limitrophe (Chekfa, Taher, Bazoul,...) et en eaux d’irrigation. Le débit de pompage de ses stations de pompagr varie entre 14 et 50 l/s. Tue, 24 Nov 2015 00:00:00 GMT http://depot.umc.edu.dz/handle/123456789/13591 2015-11-24T00:00:00Z http://depot.umc.edu.dz/handle/123456789/13589 MODELLING BIOCHEMICAL OXYGEN DEMAND (BOD5) USING ARTIFICIAL NEURAL NETWORK BASED APPROACH: CASE STUDY OF SIDI MAROUANE WASTEWATER TREATMENT PLANT (WWTP) AT BENI HAROUN DAM RESERVOIR HEDDAM, Salim; LAMDA, Hilal; FILALI, Samir In this study, Multi-Layer Perceptron Neural Networks (MLPNN) and multiple linear regression (MLR) models were employed to predict the effluent Biochemical Oxygen Demand (BOD5) from a wastewater treatment plant (WWTP) of Sidi Marouane WWTP at Beni Haroun Dam Reservoir, Algeria. The two models are built using many variables, namely temperature of water (TE), conductivity (EC), water pH, suspended solids (SS), chemical oxygen demand (COD) and Biochemical oxygen demand (BOD5). Tue, 24 Nov 2015 00:00:00 GMT http://depot.umc.edu.dz/handle/123456789/13589 2015-11-24T00:00:00Z