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

dc.contributor.author	HEDDAM, Salim
dc.contributor.author	LAMDA, Hilal
dc.contributor.author	FILALI, Samir
dc.date.accessioned	2022-12-18T13:46:27Z
dc.date.available	2022-12-18T13:46:27Z
dc.date.issued	2015-11-24
dc.identifier.uri	http://depot.umc.edu.dz/handle/123456789/13589
dc.description.abstract	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).	fr_FR
dc.language.iso	en	fr_FR
dc.publisher	Université Frères Mentouri - Constantine 1	fr_FR
dc.subject	MLR and MLPNN	fr_FR
dc.subject	high statistical quality	fr_FR
dc.subject	MLPNN	fr_FR
dc.title	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	fr_FR
dc.type	Article	fr_FR