This paper presents the energy audit of a water network, which is obtained from the energy equation in integral form, and its time integration extended over a given period (day, month, or year). The analysis allows accounting for all the energy in the system, showing that the energy balance is maintained. This balance can be used to obtain performance indicators to assess the system from the energetic point of view. From these indicators, it is possible to identify the improvement actions that will make the system more efficient. This energy audit requires a previous water balance and the mathematical model of the network, both of which are necessary to know the energy flows through the system’s boundaries.

Groundwater quality in arid areas is an issue that requires attention, as often the only source of irrigation and drinking. In this manuscript, a research area of 4590.2 Km², which depicts important problems of water scarcity and water-poor quality in an arid environment, was assessed. Therefore, GIS-based mapping of the region was carried out to examine the adequacy of groundwater quality. Forty-three (43) groundwater samples were gathered in the fieldwork in March 2022, and various physicochemical limits were analysed within Gibbs and Piper diagrams. Likewise, spatial distribution maps of twelve (12) main water quality parameters were performed using the Inverse Distance Weighted interpolation method (IDW) in the ArcGIS tool. The Water Quality Index (WQI) has been calculated considering Algerian drinking water quality standards and to know the suitability of water for human consumption. A Pearson’s test was carried out to explore the correlations among the different variables. Major results of the study showed that, among anions and cations, sulphates ( $$SO_{4}^{ - 2}$$S O 4 - 2 ) and calcium (Ca) were the most common ions. The WQI showed that 30.23% of the samples have excellent water quality, 62.79% belong to the good water category, and 6.97% fall into the poor water quality category. Thus, the correlation study between many physicochemical properties also showed significant positive relationships between WQI, TDS, EC, TH, $${\text{SO}}_{4}^{ - 2}$$SO 4 - 2 , $${\text{Ca}}^{ + + }$$Ca + + , and $${\text{Mg}}^{ + + }$$Mg + + . In agreement with the Gibbs diagram, most of the samples fall into the field of rock-water interaction dominance. Hence, this research shows that using GIS and WQI as joint approaches could be useful tools for the decision-making process of local and international authorities and water resources management.

Water quality and scarcity are key topics considered by the Sustainable Development Goals (SDGs), institutions, policymakers and stakeholders to guarantee human safety, but also vital to protect natural ecosystems. However, conventional approaches to deciding the suitability of water for drinking purposes are often costly because multiple characteristics are required, notably in low-income countries. As a result, building right and trustworthy models is mandatory to correctly manage available groundwater resources. In this research, we propose to check multiple classification techniques such as Decision Trees (DT), K-Nearest Neighbors (KNN), Discriminants Analysis (DA), Support Vector Machine (SVM), and Ensemble Trees (ET) to design the best strategy allowing the forecast a Water Quality Index (WQI). To achieve this goal, an extended dataset characterized by water samples collected in a total of twelve municipalities of the Wilaya of Naâma in Algeria was considered. Among them, 151 samples were examined as training samples, and 18 were used to test and confirm the prediction model. Later, data samples were classified based on the WQI into four states: excellent water quality, good water quality, poor water quality, and very poor or unsafe water. The main results revealed that the SVM classifier obtained the highest forecast accuracy, with 95.4% of prediction accuracy when the data are standardized and 88.9% for the accuracy of the test samples. The results confirmed that the use of machine learning models are powerful tools for forecasting drinking water as larger scales to promote the design of efficient and sustainable water quality control and support decision-plans.

Solar photovoltaic systems have become one of the most popular topics in the water management industry. Moreover, irrigation networks are water- and energy-hungry, and utility managers are likely to adapt water consumption (and consequently energy demand) to the hours in which there is energy availability. In countries such as Spain (with high irradiance values), solar energy is an available green alternative characterised by zero electricity costs and significantly lower environmental impact. In this work, several types of irrigation scheduled programmes (according to different irrigation sectors) that minimise the number of photovoltaic solar panels to be installed are studied; moreover, the effects of the variable costs linked to energy (energy and emissions costs) are presented. Finally, the effect of incorporating batteries for storing energy to protect the system against emergencies, such as unfavourable weather, is proposed. The irrigation hours available to satisfy water demands are limited by sunlight; they are also limited by the condition that the irrigation schedule type has to be rigid (predetermined rotation) and that the pressure at any node has to be above minimum pressure required by standards. A real case study is performed, and the results obtained demonstrate that there is no universal solution; this is because the portfolio of alternatives is based on investments for purchasing equipment at present and also on future energy savings (revenues). Apart from these two values, there is an economic value (equivalent discontinuous discount rate), which also influences the final results.

This study aimed to assess the water quality parameters in the wastewater treatment plant (WWTP) of Ain Sefra, southwestern Algeria. Various methods were employed to analyze the performance and suitability of the WWTP for irrigation. The results revealed effective removal of nitrates, with levels below the limit set for irrigation water. The dissolved oxygen content showed efficient biological processes and good degradation of organic matter. Phosphate levels were found to be within FAO and Algerian irrigation standards. However, elevated ammonia levels were observed, exceeding typical ranges for irrigation. The suitability of groundwater for irrigation was evaluated by calculating groundwater suitability indices. These indices categorized all samples as either excellent or good based on their Sodium Adsorption Ratio (SAR) and Kelly’s ratio. However, the sodium percentage values raised concerns about potential negative effects on the soil. Some samples were deemed unsuitable for irrigation because of high magnesium hazard and potential salinity values. These findings offer valuable insights into the performance and suitability of treated wastewater for irrigation in the Ain Sefra region. They can inform decision makers and stakeholders involved in agriculture and water management.