The relation between sediment concentration (C) and discharge (Q), for a hydrological event, such as a flood, is studied by analysing timeseries plot (discharge and sediment concentration, time). Comparing C/Q ratios at a given discharge on the rising and recession limbs of the hydrograph provides a consistent, reliable method for categorising C-Q relations. Three common classes of such relations are clockwise loop, counterclockwise loop and figure eight. Temporalgraph mode and skewness influence the type of relation, whereas a timeseries plot spread affects the details of the particular C-Q (its graphical breadth, shape, orientation and plotted location). Field examples of the various types of relation are given, for the several floods in the Wadi Kebir Rhumel Basin in North-East of Algeria. The hysteresis pattern in the downstream Kebir Rhumel Basin is dominantly counterclockwise and the relationship between the discharge (Q) and sediment load (Qs = Q x C) rates of rising and recession limps show an excellent exponential tendency for several analysed floods.
Surface water of Kébir Rhumel basin is indispensable for domestic and industrial needs of this region. Industrial development, with water excessive use and chemical products, in production and industrial treatment, and not sustainable fertilizers in agriculture, constitutes a serious threat to maintain our resources of good water quality. The majority of domestic and industrial wastewaters of the region, discharged to the stream water of Kébir Rhumel basin, promote the water enrichment in nutritious elements, phosphorus and nitrogen and particularly, the resulting increase in the aquatic primary production, mainly the planktonic or benthic algae. As a result, the physical and chemical properties of water deteriorate.This basin allows construction of the largest dam in Algeria “Beni-Haroun dam”. The infrastructure that was one of the greatest challenges of Algeria is now a reality. Hydraulic complex of Beni-Haroun remains a strategic and major achievement in the development program of water resources sector. This enormous building was constructed in the territory of the Wilaya (province) of Mila, used to meet water needs, with four million inhabitants, of eastern Algeria and other neighbouring regions that have suffered from lack of water consumption, especially in summer. In addition, it will irrigate over 42 000 ha, going thus to the several plains.Integration of sociological and environmental concerns into dams design is a recent phenomenon. It is considered at the impact study level, during which the dam study project is accompanied by a survey to assess project impact on natural environment and socioeconomic development.
Developing trustworthy rainfall-runoff (R-R) models can offer serviceable information for planning and managing water resources. Use of artificial neural network (ANN) in adopting such models and predicting changes in runoff has become popular among many hydrologists from a long time. However, since the optimization is the most significant phase in ANN training, researchers' attentiveness has been attracted to the ANN's biggest problem, i.e. its susceptibility of being blocked in local minima. Consequently, use of genetic algorithms (GA), particle swarm optimization (PSO), firefly algorithm (FFA) and improved particle swarm optimization (IPSO) approaches to increase the performance of ANN, have gained remarkable interest among distinct modern heuristic optimization approaches. In this paper, the capability of four improved ANN methods, hybrid GA-based ANN, PSO-based ANN, FFA-based ANN and IPSO-based ANN in modeling rainfall-runoff (R-R) is investigated. IPSO has been used in order to increase the ability of PSO, where the new positions of particles are dynamically adjusted using two procedures which is given form the velocity obtained by PSO and proposed velocity in IPSO. The random normal grated number with a dynamical scale factor is used to compute the new position of the best particles in proposed velocity. Daily R-R data from six stations distributed in the Seybouse watershed located in semi-arid region in Algeria were used in models' development. The selection of the input data sets was carried out using the autocorrelation, partial autocorrelation and cross correlation functions. The results of the four hybrid models were compared via performance metrics, viz., Root Mean Square Error (RMSE), Pearson's correlation coefficient (R), Nash Sutcliffe Efficiency coefficient (NSE), and via graphical analysis (scatter plots, time series and Taylor diagram). Outcomes of the analysis at all study stations disclosed that all the ANN models enhanced with IPSO overachieved the GA-based ANN, PSObased ANN and FFA-based ANN models in estimating runoff for both training and testing periods. The outcomes of the study indicate that the IPSO hybrid metaheuristic algorithm is the best technique in improving ANN capability in modeling daily R-R.
The objective of this research is to arrive at a better assessment of the quality of surface water in the Constantine region. The focus is on the comparison of three classical indices WQINSF (National Sanitation Foundation Water Quality Index), WQICCME (Canadian Council of Ministers of the Environment Water Quality Index) and WQIAP (weighted arithmetical Water Quality Index), the development of a new index and the prediction by ANN (Artificial neural network) of WQI indices. The PCA allows to select 10 parameters to be used in the calculation of the classical WQI, and 8 principal components to be used as input for the new proposed index (regularized WQI). However the ANN is applied for the search of prediction models of classical WQI and developed WQI. The results show that the WQIAP index assesses water quality better, and that the regularized WQI further promotes the assessment of water quality. WQIR shows that after the pollution peak the water quality does not return to its initial state. The modeling approach by ANN offers an effective alternative to predict the WQI, it subsequently appears that the ANN predicts better the new index WQIRregularized (R2 = 0.999) than the classic model WQIAP (R2 = 0.99).
Suspended Sediment Concentrations (SSC) Prediction in arid and semi-arid areas has aroused increasing interest in recent years because of its primary role in water resources planning and management. Today, given its simplicity and reliability, SSC modeling by artificial neural networks (ANN) and adaptive neuro-fuzzy Interference (ANFIS) are the most developed and widely used methods. The main aim of this study is suspended sediment concentrations modeling using ANN) and ANFIS methods at the five largest basins in eastern Algeria: the Constantinois Coastal, Highlands, Kébir-Rhumel, Seybouse, and Soummam basin, which are characterized by high water erosion and a lack of SSC measurements. An application was given for historical time series: liquid flows Ql and solid flows Qs as inputs, and daily SSC as outputs, for the 14 hydrometric stations controlling the entire area. The best models were achieved using a Multi-Layer Perceptrons (MLP) Feed Forward Networks (FFN) trained with a Levenberg-Marquardt (LM) algorithm for ANN modeling and a First-order Takagi-Sugeno-Kang (TSK) Feed-Forward Network (FFN) with a hybrid learning method for Anfis modeling. The reliability of the created models was evaluated using five validation cretaria: determination coefficient R2, Nash-Sutcliffe coefficient NSE, mean square error MSE, root-mean-square error RMSE, and the mean absolute error MAE. The ANN and ANFIS models showed high accuracy, confirmed by excellent R2 values ranging from 0.77 to 0.98. The NSE ranged from 0.67 to 0.97. The error values were very good, the MAE varies from 0.004 g/L to 0.028 g/L for both models. The comparison of the ANN and ANFIS models revealed that ANN models slightly outperformed the ANFISs; both of them had high accuracy in SSC prediction.
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