The evaluation of groundwater quality is crucial for irrigation purposes; however, due to financial constraints in developing countries, such evaluations suffer from insufficient sampling frequency, hindering comprehensive assessments. Therefore, associated with machine learning approaches and the irrigation water quality index (IWQI), this research aims to evaluate the groundwater quality in Naama, a region in southwest Algeria. Hydrochemical parameters (cations, anions, pH, and EC), qualitative indices ($\mathrm{S}\mathrm{A}\mathrm{R},\mathrm{R}\mathrm{S}\mathrm{C},\mathrm{N}\mathrm{a}\mathrm{\%},\mathrm{M}\mathrm{H},\mathrm{a}\mathrm{n}\mathrm{d} \mathrm{P}\mathrm{I}),$ as well as geospatial representations were used to determine the groundwater’s suitability for irrigation in the study area. In addition, efficient machine learning approaches for forecasting IWQI utilizing Extreme Gradient Boosting (XGBoost), Support vector regression (SVR), and K-Nearest Neighbours (KNN) models were implemented. In this research, 166 groundwater samples were used to calculate the irrigation index. The results showed that 42.18% of them were of excellent quality, 34.34% were of very good quality, 6.63% were good quality, 9.64% were satisfactory, and 4.21% were considered unsuitable for irrigation. On the other hand, results indicate that XGBoost excels in accuracy and stability, with a low RMSE (of 2.8272 and a high R of 0.9834. SVR with only four inputs (Ca²⁺, Mg²⁺, Na⁺, and K) demonstrates a notable predictive capability with a low RMSE of 2.6925 and a high R of 0.98738, while KNN showcases robust performance. The distinctions between these models have important implications for making informed decisions in agricultural water management and resource allocation within the region.

Reinforced concrete (RC) constructions are seriously threatened by chloride-induced corrosion (CIC) and carbonation, which can result in structural degradation, safety issues, and financial losses. Electrochemical methods and microstructural analysis tests are some of the laboratory techniques used to examine key elements of CIC, such as the impact of different variables and the efficacy of mitigation solutions. In situ studies that make use of non-destructive testing, chloride profiling, and half-cell potential measurements offer important new insights into the long-term performance and causes of RC structure deterioration in real-world circumstances. Non-destructive approaches for CIC detection are emerging these days and provide fruitful results. Studies have focused on the use of these approaches for CIC detection on small specimens in the lab as well as on full-scale experiments in the field. This review covers both in situ monitoring and laboratory studies to provide a thorough analysis of CIC.

The increase in population growth and demand is rapidly depleting natural resources. Irrigation plays a vital role in the productivity and growth of agriculture, consuming no less than 75% of fresh water utilization globally. Irrigation, being the largest consumer of water across the globe, needs refinements in its process, and because it is implemented by individuals (farmers), the use of water for irrigation is not effective. To enhance irrigation management, farmers need to keep track of information such as soil type, climatic conditions, available water resources, soil pH, soil nutrients, and soil moisture to make decisions that resolve or prevent agricultural complexity. Irrigation, a data-driven technology, requires the integration of emerging technologies and modern methodologies to provide solutions to the complex problems faced by agriculture. The paper is an overview of IoT-enabled modern technologies through which irrigation management can be elevated. This paper presents the evolution of irrigation and IoT, factors to be considered for effective irrigation, the need for effective irrigation optimization, and how dynamic irrigation optimization would help reduce water use. The paper also discusses the different IoT architecture and deployment models, sensors, and controllers used in the agriculture field, available cloud platforms for IoT, prominent tools or software used for irrigation scheduling and water need prediction, and machine learning and neural network models for irrigation. Convergence of the tools, technologies and approaches helps in the development of better irrigation management applications. Access to real-time data, such as weather, plant and soil data, must be enhanced for the development of effective irrigation management applications.

Wind energy is one of the most attractive sustainable energy resources since it has low operation, maintenance, and production costs and a relatively low impact on the environment. Identifying the optimal sites for installing wind power plants (WPPs) is considered an important challenge of wind energy development which requires careful and combined analyses of numerous criteria. This study introduces a high-resolution wind farms suitability mapping based on Fuzzy Analytical Hierarchy Process (FAHP) and Geographic Information System (GIS) approaches considering technical, environmental, social, and spatial aspects, representing eight different criteria. First, a multi-criteria decision-making analysis based on the FAHP method is employed to assign appropriate weights for the addressed criteria with respect to their relative importance. Since the traditional AHP method, which was found employed in the majority of the relative case-studies, is not efficient in dealing with uncertainty when experts use a basic scale (0 to 1) for their assessments, the FAHP provides more flexible scales through the utilized fuzzy membership functions and the natural linguistic variables. Consequently, this helps to facilitate the assessments made by experts and increases the precision of the obtained results (weights). Next, the high-resolution GIS is used to carry out a spatial analysis and integrate various factors/criteria throughout the proposed index to produce the final suitability map and identify the unsuitable areas. The presented study emphasizes investigating the lightning strike flash rate due to its significant influences on the wind turbine’s safety and operation, yet this crucial factor has been seldomly investigated in previous studies. The obtained findings revealed that the wind speed, the land slope, and the elevation had the highest weighted criteria with 33.1%, 24.8%, and 12.2%, respectively. Besides, the final-developed suitability map revealed that 23.22% and 8.31% of the Sudanese territory are of high and very high suitability, respectively, for wind farms installation which are considered sufficient to cover the electricity needs. The difficulty of acquiring real data and resources for the addressed location was the main challenge of the presented work. The work outlook addresses the suitability mapping of hybrid photovoltaic-wind turbine energy systems, which will require addressing new and significant criteria in the applied methodology.

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.