The use of intelligent algorithms for global solar prediction is an ideal tool for research focused on the use of solar energy. Forecasting solar radiation supports different applications focused on the generation and transport of energy in places where there are no meteorological stations. Different solar radiation prediction techniques have been applied in different time horizons, such as neural networks (ANN) or machine learning (ML), with the latter being the most important. The support vector machine (SVM) is a classification method of the ML that is used to predict solar radiation. To obtain a better accuracy of prediction data, search optimization algorithms (SOA) such as genetic algorithms (GA) and the particle swarm optimization algorithm (PSO) were used to optimize the prediction accuracy by searching the model parameters. This article presents a review of different hybrid SVM models with SOA applied to obtain the best parameters to reduce the prediction error of solar radiation using meteorological variables. Research articles from the last 5 years on solar radiation prediction using SVM models and hybrid SMV optimized models with SOA were studied. The results show that SVM with GA presents a better performance than the classical SVM models using the Radial basis kernel function for prediction parameters.

Grounding systems are essential components of electrical infrastructures, ensuring personnel safety, equipment protection, and operational reliability. In the context of smart cities, these systems have evolved from passive safety elements to critical assets within interconnected cyber-physical infrastructures. Simultaneously, advances in Artificial Intelligence (AI), the Internet of Things (IoT), digital twins, and predictive analytics have created new opportunities for improving the design, monitoring, maintenance, and resilience of electrical systems. This study presents a systematic review of AI applications in grounding systems and smart city electrical infrastructures following the PRISMA 2020 methodology. The literature search was conducted exclusively in the Scopus database, and 179 studies were included in the final synthesis. The review analyzes the scientific literature published between 2015–2026 and examines four major thematic classification dimensions: (i) the role of grounding systems in smart city infrastructures, (ii) AI-based design and optimization methods, (iii) intelligent monitoring, fault detection, predictive maintenance, and digital twins, and (iv) sustainability, resilience, and future implementation challenges. The thematic classification provides the organizational structure for the evidence synthesis, while a separate set of seven assessment axes is used to comparatively evaluate the technological capabilities and integration potential of the main AI technology families. The findings were synthesized by distinguishing between evidence directly addressing grounding systems and evidence from adjacent smart-grid and electrical-infrastructure domains. Direct grounding-system studies indicate that Machine Learning (ML), Deep Learning (DL), optimization algorithms, and intelligent sensing can support grounding design, condition assessment, fault diagnosis, and maintenance-related tasks, although the available evidence remains limited and heterogeneous. In adjacent electrical-infrastructure domains, AI-based approaches report high fault-detection performance, enable real-time condition monitoring, and support predictive maintenance strategies associated with reductions in downtime and maintenance costs. These findings provide transferable methodological insights for intelligent grounding-system management but should not be interpreted as direct evidence of performance improvements in grounding systems. The review also identifies critical research gaps, including the lack of standardized frameworks, limited large-scale deployments, interoperability challenges, cybersecurity concerns, and the need for explainable and trustworthy AI solutions. The study concludes that AI has the potential to become a foundational technology for the development of intelligent, adaptive, and resilient grounding systems that support the sustainability, reliability, and resilience objectives of future smart cities.

Artificial Intelligence (AI) has become a key enabler in soil science and agriculture, supporting advanced modeling, monitoring, and decision-making processes. This systematic review synthesizes recent developments in AI-based soil characterization and agricultural applications, with emphasis on soil physicochemical properties, digital soil mapping, irrigation management, and crop yield prediction. Following the PRISMA 2020 framework, a structured search of the Scopus database identified 196 eligible studies published between 2018 and 2026. The reviewed literature reveals a clear transition toward data-driven approaches, with machine learning and deep learning models dominating recent research. Random Forest, Support Vector Machines, gradient boosting methods, artificial neural networks, Convolutional Neural Networks, and Long Short-Term Memory architectures were the most frequently reported techniques. The primary data sources included in situ sensors, laboratory measurements, remote sensing imagery, and environmental covariates, often integrated through multi-source data fusion frameworks. The results indicate that tree-based ensemble models provide robust performance across diverse soil properties, whereas deep learning models are particularly effective for spatiotemporal prediction and remote sensing applications. AI-driven systems are increasingly used to support precision agriculture through irrigation optimization, crop yield forecasting, digital soil mapping, and soil health monitoring. However, challenges remain regarding data quality and availability, model transferability across regions, and the limited interpretability of complex models. The findings highlight current research trends, methodological challenges, and future opportunities for the development of reliable and scalable AI-driven soil and agricultural systems.