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The present study addresses the possible effects of soil moisture changes on the simulated daily maximum and minimum air temperatures of Australia for a duration of 13 years. Therefore, the community land model version 4.5 (CLM4.5; coupled to the RegCM4) was used to represent the soil moisture and processes associated with it. The CLM4.5 has two land-surface hydrology schemes: TOPMODEL (TOP) and Variable Infiltration Capacity (VIC) and two simulations were conducted, namely: TOP and VIC. The results showed that VIC has lower soil moisture than TOP, leading to a decrease in vegetation transpiration, evaporation, and an increase in soil evaporation relative to TOP. However, there is no considerable difference between the two simulations compared with reanalysis products. In comparison to in-situ measurements, the RegCM4 can reasonably model the climatological annual cycle of mean air temperature (TMP) and its performance varies with the study site (e.g., RegCM4 overestimates TMP by 2.76 and 5.46 °C at Yanco and Tumbarumba, respectively). In summary, the simulated maximum and minimum air temperatures are sensitive to the physical parameterization of RegCM4 rather than variations in soil moisture. Likewise, improvements to the land-surface hydrology schemes TOP/VIC are required to better model Australia's daily maximum and minimum air temperatures.

In this research, the spatiotemporal meteorological drought pattern was assessed in the Oued Sebaou basin (northern central Algeria) based on data from 23 rain gauges from 1972 to 2010 analyzed at seasonal and annual scales using the standardized precipitation index (SPI). A geographic information system was used to determine the cartographic precipitation concentration index and modified Fourier index (MFI) as well as the drought and rainfall characteristics of the stations. The analysis revealed moderate precipitation concentrations for all stations in the basin, for which longitude explains approximately 60% of the variance. The MFI results show three main aggressiveness distributions: weak, moderate and strong, which are similar to the spatial distribution of rainfall. The results show a prolonged drought that began in the late 1980s. In addition, more than 50% of the stations were affected by moderate and severe dry events during the period from 1986 to 2001. The comparison of the SPI values among the decades indicates that more than 20% of the cases were dominated by wet conditions from 1972 to 1981 and from 2002 to 2010, with near-normal and -normal events exceeding 70%. During the 2002–2010 decade, extreme wet events occurred in 7% of the cases, while in the 1972–1981 decade, only 1.75% of the cases were of this type. These results can provide watershed managers with more information to understand past drought and improve future water resource management.

The study of the effects of mixing potable water with wastewater is a complex and difficult research area. This difficulty is because water and sewage networks are subject to various physical, environmental, and operational factors. The main objective of the study was to propose a new comprehensive framework for analyzing and assessing water quality based on Bayesian networks. An intervention plan was proposed to reduce the consequences of water quality and networks failure. The proposed framework was applied to water distribution network of Mdaourouch city (Souk Ahras, Algeria) to demonstrate its effectiveness. The results indicated that the water contamination rate has reached 33.9 %, which caused severe consequences. The effectiveness of the proposed plan has been verified theoretically using simulations, and the results have proven to be very satisfactory. The proposed model is a decision support tool, which is expected to assist decision-makers and engineers in reviewing their plans and making the right decision. PRACTITIONER POINTS: This paper proposes a novel comprehensive framework for analyzing and assessing water quality and failure consequences based on Bayesian networks. This paper revisits the failure consequences. An intervention plan is proposed to reduce failure consequences. Results demonstrate that the proposed plan leads to fewer consequences probabilities. The proposed method can give the probability of failure of water and sewer network.

The aim of this research is to assess relatively new hybrid methods for changes points and trends detection on rainfall series: Dynamic Programming Bayesian Change Point Approach (BA), Sen's innovative trend method (ITM) and its double (D-ITM) and triple (T-ITM) version using the multi-scale analysis of the discrete wavelet transform (DWT) as a coupling method. Three representatives rainfall stations of northern Algeria were analysed at annual scale during the period 1920–2011. Moreover, correlation and spectral analysis (CSA) was applied for periodicity analysis. The CSA indicates the dominance of interannual to multidecadal rainfall periodicity fluctuations (2-years, 5-years and 20-years) characterising long term structured processes. Moreover, an abrupt downward trend with significant probability was detected from the 1970s with a relatively wet period between the periods 1950–1970 and 2001–2011. The latter is observed in particular in the central and eastern stations, well-explained by the BA-DWT. The results showed that the comparison results from different modelling approaches found that the hybrid models (BA-DWT, ITM-DWT, D-ITM-DWT, T-ITM-DWT) often perform better than the conventional approach (BA, ITM, D-ITM, T-ITM), where the computation time is very reasonable. The analysis revealed that information stemming from discrete wavelet spectrums significantly increased the accuracy of the methods for detecting hidden change points and trends.

Heavy rainfall over a short period or slowly during long periods can significantly increase the amount of water. Where it results in floods that can pose a direct threat, capable of causing human and material losses. Over the past two decades, artificial intelligence has been widely applied in the field of hydrology as well as in many other areas of hydraulic engineering. The wavelet transform is a very popular technique in the analysis of non-stationary time series and particularly effective for hydrological series. Currently, the application of intelligent hybrid systems in different fields has shown good performance and unparalleled efficiency. As such, in this work, we propose a hybrid neuro-fuzzy-wavelet model to modelling the rainfall-runoff transformation for forecasting daily flow rates in the Sebaou basin located in Tizi Ouzou region. The results obtained are very encouraging and better than those obtained by the models used for comparison in this research. According to the results, the hybrid neuro-fuzzy-wavelet model with mother wavelet db7 gave us better performance for daily flow rates forecasting.

Evapotranspiration is an important quantity required in many applications, such as hydrology and agricultural and irrigation planning. Reference evapotranspiration is particularly important, and the prediction of its variations is beneficial for analyzing the needs and management of water resources. In this paper, we explore the predictive ability of hybrid ensemble learning to predict daily reference evapotranspiration (RET) under the semi-arid climate by using meteorological datasets at 12 locations in the Andalusia province in southern Spain. The datasets comprise mean, maximum, and minimum air temperatures and mean relative humidity and mean wind speed. A new modified variant of the grey wolf optimizer, named the PRSFGWO algorithm, is proposed to maximize the ensemble learning's prediction accuracy through optimal weight tuning and evaluate the proposed model's capacity when the climate data is limited. The performance of the proposed approach, based on weighted ensemble learning, is compared with various algorithms commonly adopted in relevant studies. A diverse set of statistical measurements alongside ANOVA tests was used to evaluate the predictive performance of the prediction models. The proposed model showed high-accuracy statistics, with relative root mean errors lower than 0.999% and a minimum R

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.

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.

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.

Due to its geographical location, Algeria is characterized by high spatiotemporal rainfall variability. In this study, data from 69 rain gauges located in representative humid, semiarid and arid Mediterranean basins in northeastern Algeria were analyzed from 1970–2007 on a monthly scale using continuous wavelet analysis and hierarchical cluster analysis with the aim of regionalizing the rainfall patterns. The analysis shows that northern Algeria (cluster #1), which has a humid climate, is dominated by periodic annual fluctuations in the 8–16-month band. This mode explains most of the total variance, with a contribution between 25 and 60%. In the cluster #2 and cluster #3 regions, the climate varies towards aridity (humid to arid from north to south), and the climate is dominated by long-term periodic phenomena characterizing multiannual fluctuations of 64–128 months to decadal periods greater than 128 months, which explains why the total cumulative contribution exceeds 50% of the total variance. In addition, the regional analysis of the isolated spectral bands of the 3–6-month (3 clusters), 8–16-month (3 clusters), and 1–3-year (4 clusters) scale-average variance revealed, globally and for the different regions, a long period of drought that was most pronounced during the 1970s, 1980s, and 1990s, whereas the wet years were marked by fluctuations that exceeded the 95% confidence level during the study period, with a very remarkable tendency towards wet conditions, particularly since the late 1990s. The obtained results can assist decision-makers in better sustainable development practices, especially in the fields of water resources, agriculture, and energy.

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)

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.

In this paper, the time and frequency-based methods as cross wavelet (XWT) and wavelet coherence transform (WCT) are used to search the links between rainfall and runoff of the hydrogeological systems of the Sebaou river watershed (northern central Algeria). The analysis shows a discontinuity in the linearity between rainfall and discharge that could correspond to the dry periods, storage during floods and the capacitance of the aquifer to store reserves, with the exception of some significant amplifications in some time intervals. Furthermore, human activities such as the dam construction and the smuggling of sands and rocks of the Oued (Wadi) since the year 1998 have had a significant impact on rainfall-runoff relationship, the flows decrease and the changes in the flow regime, which is more pronounced with a rupture in the annual component of 256–512 day of WCT spectrum.

Flooding is a natural phenomenon, which constitutes a threat that could lead to loss of human life and material property. It constitutes the first major risk. During the last years, artificial intelligence has been widely applied in the field of hydrology and in many other fields of hydraulic engineering. The Hilbert-Huang Transform (HHT) is a new signal processing technique in the analysis of non-stationary time series, particularly effective for hydrological series. Currently, the application of intelligent hybrid systems in different areas has shown a good performance and an unequalled efficiency. As such, the hybrid technique of an adaptive neuro-fuzzy inference system (ANFIS) coupled to the Hilbert-Huang transform (HHT-ANFIS), was used in this study to estimate daily flow rates in Algiers’ coastal basin. The results obtained are very encouraging and more efficient than those obtained by the neuro-fuzzy inference model and the classical multiple linear regression (MLR) model.

This paper is proposed for the investigation of possible relationships between the large-scale atmospheric circulation phenomena such as the North Atlantic Oscillation (NAO), Southern Oscillation (SOI), Mediterranean Oscillation (MO), Western Mediterranean Oscillation (WeMO) and rainfall of Sebaou river watershed (Northern central Algeria), covering a period of 39 years at monthly scale. Several time and scale-based methods were used: correlation and spectral analysis (CSA), continuous wavelet transform (CWT), multiresolution wavelet analysis (MRWA), cross wavelet analysis (XWT), wavelet coherence transform (WCT) and cross multiresolution wavelet analysis (CMRWA). The rainfall analysis by CSA and CWT has been clearly demonstrating the dominance of 1 year and 1–3-year modes, which they explain 30 to 51% and 25 to 28% of the variance respectively. However, the indices have shown that inter-annual fluctuations up to long-term explain between 60 and 90%. CWT and MRWA indicated significant fluctuations materialising a dry period more marked between the 1980s and 1990s with strong trend towards drier conditions starting from the 1980s, explained by the decadal components D7 and the approximation A7. In addition to the annual component, the XWT spectrums reveal strong coefficients for the SOI between 1992–2005 and 1986–2000 for the modes of 5–10 years and higher than 10 years respectively and less intense for NAO. The WCT between NAO and rainfall indicated the most significant relationship for 1 year, 1–3 years and 3–5 years approximately from the early 1980s corresponding to the dry period. However, the SOI affects rainfall only locally and with significant values more or less localised in the time-frequency space between MO, WeMO and rainfall, but this influence could be significant for low-frequency events. CWMRA shows that the components of 5–10 years and higher than 10 years are the most effective to represent climate index-rainfall significant relationships, where change in Daubechies wavelet properties can improve the correlation across the scales. Furthermore, has indicated that the short-term processes dominate the relationship index-rainfall, which masks the long-term phenomena whose influence can sometimes be very distant. As such, the rainfall variability of the study area has shown fairly significant links, at least locally with large-scale atmospheric circulation phenomena.