ANALYSE DE LA MEDIATION DANS LA RECHERCHE EN MANAGEMENT : L’IMPACT DE LA VEILLE DANS LA POSITION CONCURRENTIELLE DES PME

Cette étude discute une nouvelle Méthode d'analyser de la médiation dans un contexte du PLS. Elle facilite l'adoption de nouvelles procédures en PLS, par le défi de l'approche traditionnelle de l'analyse de la médiation en proposant des alternatives plus précises. De plus, l'objectif de cet article est de vérifier l'existence d'un rôle de la veille dans La position concurrentielle des PME. L'étude a trouvé la signification de l'effet indirect de la médiation.L’enquête a confirmé l’effet de L’influence comme un médiateur, il y a une partielle médiation de la relation entre la veille et La position concurrentielle

Towards a rational exploitation of the groundwater resources of the Algerian Sahara, Ghardaia region: actual situation and recommendations

peer reviewedLe Sahara septentrional renferme un des puissants systèmes aquifères dont le Continental Intercalaire (CI) qui constitue la principale réserve en eau pour les régions du Grand Erg occidental et la dorsale du Mzab (Sahara septentrional algérien). Afin de comprendre les différents processus qui contrôlent l’acquisition et la modification de la minéralisation des eaux du CI, différentes méthodes d’interprétation des données hydrochimiques ont été appliquées pour décrire les variations spatiales des concentrations en éléments majeurs et mineurs. Cette étude a montré que les eaux du CI sont, au nord de la zone d’étude (région de Laghouat), plutôt sulfatées calciques. Au sud de celui-ci (région de Ghardaïa), elles sont sulfatées à chlorurées sodiques avec un enrichissement en Cl par rapport au SO4 vers l’est en aval piézométrique. Par ailleurs, en s’appuyant sur le calcul des rapports caractéristiques Na/Cl et Br/Cl ainsi que sur l’indice de saturation, l’origine de la salinité évaporitique non marine a pu être identifiée. L’étude de cette variation spatiale a mis en évidence que les concentrations des ions sont contrôlées par des phénomènes naturels qui sont en relation avec les caractéristiques lithologiques de l’aquifère (dissolution du gypse et de la halite, échanges cationiques, …) mais également par l’intensité de l’exploitation qui a tendance à provoquer un mélange d’eau avec des niveaux aquifères plus profonds et plus salés. Ainsi, on conclut que le fonctionnement hydrogéologique, et a fortiori hydrochimique, est fortement influencé par la surexploitation qui augmente significativement la salinité

Suspended Sediment Load Simulation during Flood Events Using Intelligent Systems: A Case Study on Semiarid Regions of Mediterranean Basin

Sediment transport in rivers is a nonlinear natural phenomenon, which can harm the environment and hydraulic structures and is one of the main reasons for the dams’ siltation. In this paper, the following artificial intelligence approaches were used to simulate the suspended sediment load (SSL) during periods of flood events in the northeastern Algerian river basins: artificial neural network combined with particle swarm optimization (ANN-PSO), adaptive neuro-fuzzy inference system combined with particle swarm optimization (ANFIS-PSO), random forest (RF), and long short-term memory (LSTM). The comparison of the prediction accuracies of such different intelligent system approaches revealed that ANN-PSO, RF, and LSTM satisfactorily simulated the nonlinear process of SSL. Carefully comparing the results, the ANN-PSO model showed a slight superiority over the RF and LSTM models, with RMSE = 67.2990 kg/s in the Chemourah basin and RMSE = 55.8737 kg/s in the Gareat el tarf basin

Suspended Sediment Load Simulation during Flood Events Using Intelligent Systems: A Case Study on Semiarid Regions of Mediterranean Basin

Sediment transport in rivers is a nonlinear natural phenomenon, which can harm the environment and hydraulic structures and is one of the main reasons for the dams’ siltation. In this paper, the following artificial intelligence approaches were used to simulate the suspended sediment load (SSL) during periods of flood events in the northeastern Algerian river basins: artificial neural network combined with particle swarm optimization (ANN-PSO), adaptive neuro-fuzzy inference system combined with particle swarm optimization (ANFIS-PSO), random forest (RF), and long short-term memory (LSTM). The comparison of the prediction accuracies of such different intelligent system approaches revealed that ANN-PSO, RF, and LSTM satisfactorily simulated the nonlinear process of SSL. Carefully comparing the results, the ANN-PSO model showed a slight superiority over the RF and LSTM models, with RMSE = 67.2990 kg/s in the Chemourah basin and RMSE = 55.8737 kg/s in the Gareat el tarf basin

An Enhanced Innovative Triangular Trend Analysis of Rainfall Based on a Spectral Approach

The world is currently witnessing high rainfall variability at the spatiotemporal level. In this paper, data from three representative rain gauges in northern Algeria, from 1920 to 2011, at an annual scale, were used to assess a relatively new hybrid method, which combines the innovative triangular trend analysis (ITTA) with the orthogonal discrete wavelet transform (DWT) for partial trend identification. The analysis revealed that the period from 1950 to 1975 transported the wettest periods, followed by a long-term dry period beginning in 1973. The analysis also revealed a rainfall increase during the latter decade. The combined method (ITTA–DWT) showed a good efficiency for extreme rainfall event detection. In addition, the analysis indicated the inter- to multiannual phenomena that explained the short to medium processes that dominated the high rainfall variability, masking the partial trend components existing in the rainfall time series and making the identification of such trends a challenging task. The results indicate that the approaches—combining ITTA and selected input combination models resulting from the DWT—are auspicious compared to those found using the original rainfall observations. This analysis revealed that the ITTA–DWT method outperformed the ITTA method for partial trend identification, which proved DWT’s efficiency as a coupling method.Validerad;2021;Nivå 2;2021-03-08 (alebob)</p

Artificial intelligent systems optimized by metaheuristic algorithms and teleconnection indices for rainfall modeling: The case of a humid region in the mediterranean basin

Characterized by their high spatiotemporal variability, rainfalls are difficult to predict, especially under climate change. This study proposes a multilayer perceptron (MLP) network optimized by Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Firefly Algorithm (FFA), and Teleconnection Pattern Indices - such as North Atlantic Oscillation (NAO), Southern Oscillations (SOI), Western Mediterranean Oscillation (WeMO), and Mediterranean Oscillation (MO) - to model monthly rainfalls at the Sebaou River basin (Northern Algeria). Afterward, we compared the best-optimized MLP to the application of the Extreme Learning Machine optimized by the Bat algorithm (Bat-ELM). Assessment of the various input combinations revealed that the NAO index was the most influential parameter in improving the modeling accuracy. The results indicated that the MLP-FFA model was superior to MLP-GA and MLP-PSO for the testing phase, presenting RMSE values equal to 33.36, 30.50, and 29.92 mm, respectively. The comparison between the best MLP model and Bat-ELM revealed the high performance of Bat-ELM for rainfall modeling at the Sebaou River basin, with RMSE reducing from 29.92 to 11.89 mm and NSE value from 0.902 to 0.985 during the testing phase. This study shows that incorporating the North Atlantic Oscillation (NAO) as a predictor improved the accuracy of artificial intelligence systems optimized by metaheuristic algorithms, specifically Bat-ELM, for rainfall modeling tasks such as filling in missing data of rainfall time series

The history of rainfall data time-resolution in a wide variety of geographical areas

Collected rainfall records by gauges lead to key forcings in most hydrological studies. Depending on sensor type and recording systems, such data are characterized by different time-resolutions (or temporal aggregations), ta. We present an historical analysis of the time-evolution of ta based on a large database of rain gauge networks operative in many study areas. Globally, ta data were collected for 25,423 rain gauge stations across 32 geographic areas, with larger contributions from Australia, USA, Italy and Spain. For very old networks early recordings were manual with coarse time-resolution, typically daily or sometimes monthly. With a few exceptions, mechanical recordings on paper rolls began in the first half of the 20th century, typically with ta of 1 h or 30 min. Digital registrations started only during the last three decades of the 20th century. This short period limits investigations that require long time-series of sub-daily rainfall data, e.g, analyses of the effects of climate change on short-duration (sub-hourly) heavy rainfall. In addition, in the areas with rainfall data characterized for many years by coarse time-resolutions, annual maximum rainfall depths of short duration can be potentially underestimated and their use would produce errors in the results of successive applications. Currently, only 50% of the stations provide useful data at any time-resolution, that practically means ta = 1 min. However, a significant reduction of these issues can be obtained through the information content of the present database. Finally, we suggest an integration of the database by including additional rain gauge networks to enhance its usefulness particularly in a comparative analysis of the effects of climate change on extreme rainfalls of short duration available in different locations