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Water quality is an important criterion for evaluating the suitability of water for drinking and domestic purpose. The main objective of this study was to investigate the physicochemical characterization of groundwater for drinking water consumption. Ten captured sources were selected from three aquifers including the Guelma Mio-Plio-Quaternary alluvial basin; the Senonian Heliopolis Neritic limestone aquifer, and the Eocene limestones of Ras El Agba-Sellaoua aquifer. The analyses concerned the periods of high water in May 2017 and low water in August 2017. Twelve parameters were determined for the water samples: pH, T(°C), EC, Ca2+, Mg2+, Na+, K+, Cl−, HCO3−, SO42−, NO3−, TH(hydrotimetric degree), TAT(total alkalinity titration). The interpretation of the various analytical results allowed the determination of the chemical facies and the classification of the groundwater aquifers as follows: (i) in the alluvial layer, the gypsiferous marl substratum and the clays of the three terraces (high, medium and low) have given the water a chlorinated calcium chemical facies in the east part of the study area and travertines feeding partly alluvial layer, and have given a bicarbonated calcium water facies in the west, (ii) in the Senonian of Heliopolis limestone and Eocene carbonate formations of Ras El Agba-Sellaoua, the chemical facies are calcium bicarbonate. Water isotopes (δ18O and δD) helped to determine the origin of groundwater. Overall, the groundwater in the area is hard and has significant to excessive mineralization. It is progressively degraded in the direction of flow, especially in the Guelma alluvial aquifer.

The focus of this research was to identify potential equivalences between artificial neural networks and statistical regression and to verify these equivalences when applied to modelling sediment loads in the Kebir river. The use of feed-forward backpropagation neural networks such as MLP and LM were studied by applying relationship stream flow-sediment discharge data and geomorphology watershed parameters. Daily based water and sediment discharge were used as inputs for sediment rating curve and ANNs. In the present study the models were adopted by changing numbers of neuron in hidden layers and epoch. Results have shown that the ANN models were superior in reproducing sediment discharge compared to SRC. The findings further have suggested that LM could provide the most accurate estimates of sediment discharge, (R2and EFof 0.94) compared to SRC had lower values of R2and EF(0.89, 0.88), and resulted in underestimations of sediment discharge (−15%).

Erosion is a major phenomenon that causes damage not only to soil and agriculture, but also to the quality of the water amounting to tonnes of matter annually transported on the earth's surface. This fact has attracted the interest of researchers to understand its mechanism and explain its causes and consequences. This work is a comparative study of water erosion in the two semi-arid catchments of Wadi Soultez and Wadi Reboa; located in the North-East of Algeria. The approach adopted for the quantification of sediment transport consists on researching the best regressive model to represent the statistical relation between the sediment yield and the measured water discharge at different scales: annual, seasonal and monthly. The available data cover 27 years from 1985-2012. The results show that the power model has given the best correlation coefficient. Results have indicated that Wadi Reboa transported an average of 14.66 hm3 of water and 0.25 million tonnes of sediments annually. While Wadi Soultez has transported 4.2 hm3 of water and 0.11 million tonnes of sediments annually. At a seasonal scale, sediment amounts have showed significant water erosion in autumn with around 44% and secondarily in the spring with 29% in Wadi Soultez. Unlike Wadi Reboa, sediment transport represents 32% and 46% in autumn and spring respectively. Based on the obtained sediment amounts; it is found that the physical factors: such as steep reliefs, vulnerable lithological nature of rocks and poor vegetal cover, have significantly contributed in accelerating soil erosion.

In this study, we present the performances of the best training algorithm in Multilayer Perceptron (MLP) neural networks for prediction of suspended sediment discharges in Mellah catchment. Time series data of daily suspended sediment discharge and water discharge from the gauging station of Bouchegouf were used for training and testing the networks. A number of statistical parameters, i.e. root mean square error (RMSE), mean absolute error (MAE), coefficient of efficiency (CE) and coefficient of determination (R2) were used for performance evaluation of the model. The model produced satisfactory results and showed a very good agreement between the predicted and observed data. The results also showed that the performance of the MLP model was capable to capture the exact pattern of the sediment discharge data in the Mellah catchment.

Soil erosion by water is a major problem that the Northern part of Algeria witnesses nowadays; it reduces: the productivity of agricultural areas due to the loss of lands, and leads to the loss of storage capacity in reservoirs, the deterioration of water quality etc. The aim of this study is to evaluate the soil losses due to water erosion, and to identify the sectors which are potentially sensitive to water erosion in the Bouhamdane watershed, that is located in the northeastern part of Algeria. To this end, the Revised Universal Soil Loss Equation (RUSLE) was used. The application of this equation takes into account five parameters, namely the rainfall erosivity, topography, soil erodibility, vegetative cover and erosion control practices. The product of these parameters under GIS using the RUSLE mathematical equation has enabled evaluating an annual average erosion rate for the Bouhamdane watershed of 11.18 t·ha-1·y-1. Based on the estimates of soil loss in each grid cell, a soil erosion risk map with five risk classes was elaborated. The spatial distribution of risk classes was 16% very low, 41% low, 28% moderate, 12% high and 3% very high. Most areas showing high and very high erosion risk occurred in the lower Bouhamdane watershed around Hammam Debagh dam. These areas require adequate erosion control practices to be implemented on a priority basis in order to conserve soil resources and reduce siltation in the reservoir.

Soil erosion by water and the impact of sediment transport on lakes and streams, can seriously degrade soil and create problems for both agricultural land and water quality. The present study has been carried out to assess suspended sediment yield in Mellegue catchment, northeast of Algeria. Regression analysis was used to establish a relationship between the instantaneous water discharge (Q) and the instantaneous suspended sediment concentration (C) based on all recorded data and seasonal ratings for the period 1970–2003. The regression technique used in this paper involved a division of data into discharge – based classes, the mean concentrations and discharges of which are used to develop power regressions, according to single and season ratings, through log-transformation. Sediment loads estimated by stratified rating curves reduced underestimations to a range from 2 to 4%. The mean annual sediment yield during the 34 years of the study period was 589.23 t·km−2·y−1. Sediment transport is dominated by fall rainstorms accounting for 41% of the annual load. The big supply of sediment during this season confirms the intense geomorphic work by fall storms caused by high intensity rainfall and low vegetation cover.

Hierbei wurden die Schwebstofffrachten von einzelnen beprobten Ereignissen über ereignisbezogene Regressionsbeziehungen bestimmt. Bei Ereignissen mit fehlenden oder nur vereinzelten Schwebstoffproben wurden Schwebstoff-Abfluss-Beziehungen verwendet, die mit der Abflussklassen-Methode ermittelt wurden. Die durchschnittliche jährliche Schwebstofffracht, die durch 22 Jahre von Abflussereignissen mobilisiert wurde, betrug 461 t km−2 in Saf Saf und 247 t km−2 in Kebir West. Obwohl im Saf Saf-Einzugsgebiet sowohl der Niederschlag als auch der Abfluss geringer waren, ergaben sich höhere Erosionsraten. Jene können durch einen hohen Anteil von Ackerland auf schluffig-tonigen Böden erklärt werden, deren Hangneigung oft 12% übersteigt. Im Einzugsgebiet von Kebir West ist der Anteil von Ackerfläche geringer, auch werden flachere Hänge mit tiefgründigeren Böden bewirtschaftet. Die negativen Folgen dieser verstärkten Sedimentanlieferung aus dem Saf Saf-Einzugsgebiet sind direkt im Zardézas-Staudamm zu spüren. Die großen Abflussereignisse im Saf Saf-Einzugsgebiet im Winter und Frühjahr zeigen extreme Abflussspitzen mit hohen Schwebstoffkonzentrationen, die auf Oberflächenabfluss mit hohen Erosionsraten von gepflügten Ackerflächen hinweisen. Im Kebir West Einzugsgebiet hingegen waren die Ereignisse länger anhaltend und zeigten vergleichsweise niedrige Schwebstoffraten.