Evaluation of Karst Spring Discharge Response Using Time-Scale-Based Methods for a Mediterranean Basin of Northern Algeria

Understanding of behavior, variability, and links between hydrological series is a key element for successful long-term water resources planning and management. In this study, various time-scale-based methods such as correlation and spectral analysis (CSA), cross wavelet (XWT), and wavelet coherence transform (WCT) were applied to assess the response of daily rainfall and karst spring discharge for the Sebaou River basin, which is located on Mediterranean basin in northern Algeria. The CSA revealed that the hydrogeological systems under study are characterized by various memory effect (small, poor, reduced, and extensive) with regularization times ranging from 5 to 50 day. XWT between rainfall and discharge time series indicates few marked disruptions in the spectra between the 1980s and 1990s corresponding to the dry period. The annual process is visible, dominant, and more amplified compared to the multi-annual fluctuations that characterize the 1-3- and 3–6-year modes, which explained the multi-annual regulation. The nonlinear relationship of the short-term components seems to be linked to the periods of storage (infiltration). Compared to the WCT components of 2–5, 26, and 52 weeks, there is a strong coherence for 102 weeks, which explains the long-term component, indicating a quasi-linearity of the rainfall-runoff relationship. According to the obtained results, the construction of more water resources structures is recommended to increase the water storage and improve the water supply due to the richness of the hydrographic network. On the other hand, the impacts of human activities on streamflow due to the looting of rocks and sands in the Sebaou River valleys have reached alarmingly high levels that require urgent intervention for the protection of water and ecological resources and their better rational use

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

Determining the Hydrological Behaviour of Catchment Based on Quantitative Morphometric Analysis in the Hard Rock Area of Nand Samand Catchment, Rajasthan, India

India’s water resources are under tremendous pressure due to elevated demand for various purposes. The over-exploitation of these valuable resources has resulted in an imbalance in the watershed ecology. The application of spatial analysis tools in studying the morphological behaviour of watersheds has increased in recent decades worldwide due to the accessibility of the geospatial database. A morphometric analysis of a river basin is vital to determine the hydrological behaviour to develop effective management. Under the current study, morphological behaviour of Nand Samand catchment in the hard rock region was evaluated employing remote sensing (RS) and geographical information system (GIS) tools. The Nand Samand catchment (Rajasthan State, India) has an area of 865.18 km2 with the highest and lowest elevations of 1318 m and 570 m above mean sea level, respectively. This study utilises a 30 m high-spatial-resolution ASTER imagery digital elevation model for delineating the catchment. The drainage network is assessed using a GIS method, and morphometric parameters like linear, areal, and relief aspects were calculated. Results were obtained for parameters viz., basin length of 82.66 km, constant channel maintenance equal to 0.68 km, stream frequency of 2.11 km−2, drainage density of 1.48 km−1, and length overflow of 0.34 km. Form factor of 0.13, and the circulatory ratio of 0.28 showed that an elongated shape characterises the study area. The results would help understand the relationship between hydrological variables and geomorphological parameters for better decision-making. The techniques used could effectively help to perform better drainage basin and channel network morphometric analyses. The found morphometric characteristics will be helpful in understanding the Nand Samand catchment and similar areas in India in order to better guide the decision-makers in providing adequate policy to the development of the regionValiderad;2022;Nivå 2;2022-02-11 (joosat)</p

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&rsquo; 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

Intrinsic Workforce Diversity and Construction Worker Productivity in Pakistan: Impact of Employee Age and Industry Experience

Worker productivity is critical within construction projects as it is the measure of the rate at which work is performed and, more importantly, helps to know how to motivate them to perform at high levels. This research aimed to examine the impact of employee age and industry experience on the intrinsic workforce diversity factors influencing construction worker productivity. Sieving through the previous research and models and theories of analysis, the intrinsic workforce diversity was modeled into the following set of factors, i.e., income, motivation, psychosocial factors, and technical skills. The data were collected by means of a questionnaire survey and examined for the employees having different ages and experiences using the Mann–Whitney U test through SPSS. The results show that employees of varied ages do not concur over motivation-, psychosocial, and technical skills-related workforce diversity factors, whereas employees of varied industrial experiences are in disagreement over some income and motivation related workforce diversity factors. In order to overcome intrinsic workforce diversity, firm support is direly needed for old and mature employees in terms of financial incentives leading to motivation, less supervised scheduling, opportunities for firm advancement, and reporting back every time work is completed. Furthermore, support is required for young employees who are more susceptible due to psychosocial stresses like unevenly distributed work, communication gaps, and technical skills like knowledge of technological equipment and advancement in construction technology which has reduced the skills of workers

A GIS-Based Groundwater Contamination Assessment Using Modified DRASTIC Geospatial Technique

Groundwater contamination along with anthropogenic actions and land use forms are increasing threats in urbanized zones around the world. Additionally, water quality and quantity are declining due to urbanization development. DRASTIC parameters (depth to the water table, net recharge, aquifer media, soil media, topography, impact of the vadose zone, hydraulic conductivity) were considered to investigate hydrological characteristics for assessment of contamination. Having a major effect of anthropogenic activities, various susceptibility zones were produced by modifying the DRASTIC model into DRASTICA, integrating anthropogenic effects as the “A” parameter in an alphabetic system. After the assessment, the research exposes that from the total area, 14% is under very high susceptibility, 44% is of high susceptibility, 39% is of moderate susceptibility, and 3% is of low susceptibility to groundwater pollution. The results in the built-up areas and based on the parameter of nitrate in quality of water show that the altered DRASTIC model or DRASTICA model proved to give better outcomes compared with the usual DRASTIC model. The policy advisers and management authorities must use the analysis data as precaution measures so that future calamities can be avoided

Investigating Relationships between Runoff–Erosion Processes and Land Use and Land Cover Using Remote Sensing Multiple Gridded Datasets

Climate variability, land use and land cover changes (LULCC) have a considerable impact on runoff–erosion processes. This study analyzed the relationships between climate variability and spatiotemporal LULCC on runoff–erosion processes in different scenarios of land use and land cover (LULC) for the Almas River basin, located in the Cerrado biome in Brazil. Landsat images from 1991, 2006, and 2017 were used to analyze changes and the LULC scenarios. Two simulations based on the Soil and Water Assessment Tool (SWAT) were compared: (1) default application using the standard model database (SWATd), and (2) application using remote sensing multiple gridded datasets (albedo and leaf area index) downloaded using the Google Earth Engine (SWATrs). In addition, the SWAT model was applied to analyze the impacts of streamflow and erosion in two hypothetical scenarios of LULC. The first scenario was the optimistic scenario (OS), which represents the sustainable use and preservation of natural vegetation, emphasizing the recovery of permanent preservation areas close to watercourses, hilltops, and mountains, based on the Brazilian forest code. The second scenario was the pessimistic scenario (PS), which presents increased deforestation and expansion of farming activities. The results of the LULC changes show that between 1991 and 2017, the area occupied by agriculture and livestock increased by 75.38%. These results confirmed an increase in the sugarcane plantation and the number of cattle in the basin. The SWAT results showed that the difference between the simulated streamflow for the PS was 26.42%, compared with the OS. The sediment yield average estimation in the PS was 0.035 ton/ha/year, whereas in the OS, it was 0.025 ton/ha/year (i.e., a decrease of 21.88%). The results demonstrated that the basin has a greater predisposition for increased streamflow and sediment yield due to the LULC changes. In addition, measures to contain the increase in agriculture should be analyzed by regional managers to reduce soil erosion in this biome.Validerad;2022;Nivå 2;2022-04-21 (joosat);</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