In recent times, there has been a growing interest in understanding precipitation variability and its predictability for periods of a few months to several years. Our work consisted of studying climatic changes in the northeastern region of Algeria based on multivariate analysis of the annual rainfall. Variability of annual rainfall was analyzed using principal component analysis (PCA) and non-hierarchical classification (cluster method). For spatial rainfall variability, due to the complexity of the region, we used the method inverse distance weighted cartography modeling. Results indicate PCA represented the accumulated yearly rainfall of correlated fields on the annual scale, only the years 1971, 1985, 1995, and 2002 had a rather high degree of correlation, translating the homogeneity of annual distribution of precipitation. Cluster method demonstrated the certainty of three groups. The first group was characterized by regions of distinguishable climatic types, such as Mediterranean climate. The second group was characterized by the Tellian Atlas, while the third group was characterized by high plateaus. Spatial analysis of decade averages rainfall show that the curve isohyets of 750 mm in the center of the region study are shifting to the south, and that the Mediterranean regime rainfall is affecting all the northern region.
The objective of this work is studying the transformation of the rainfall into rain off in area scale catchment of North East of Algeria by artificial neural networks (ANNs). In this paper, we used simulation and the forecast per ANN and we adopted model conceptual GR2M to validate the results obtained per ANN. In this case, it is necessary to bring a sample of hydro meteorological data to knowing the rains, the evapotranspiration and the flows of the station to be modeled. Results obtained per ANN show superior result compared to the traditional modeling approaches (GR2M). Indeed, the coefficient of correlation is very significant (R ² exceeds 0.95) and the very weak quadratic error.
Treatment performances of two wastewater treatment plants (WWTPs), located in North-Eastern Algeria (Souk-Ahras and Sedrata) were tested using ASM1 model.The model, to be considered as a decision tool for the appropriate management of activated sludge systems, served for the prediction of both WWTP behaviours under different operating conditions. In Sedrata WWTP the first management scenarios is based on an increase of inflow rate, taking into account a new transfer volume from a nearby zone. In a second scenerio, the ultimate flow of 40 000 m 3 ·d -1 is estimated. Regarding Souk-Ahras WWTP, three scenarios were tested. The first tested the impact of an increase of the extraction flow rate and yielded a reduction by 37% of sludge production. The second dealt with the management of the mass budget of substrata and biomass. Finally, the third application was devoted to the estimation of the plant ultimate capacity, estimated to be 60 000 m 3 ·d -1.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.