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.

The design of flexible road pavements is a complex process as a result of the multiple variables that influence and interact in the models that allow the design of each layer. In recent years, a particular interest has been raised to ensure that climate is considered in pavement design due to temperature and precipitation that influence the deterioration of pavements, impacting their service life. This paper presents a critical review of flexible pavement design methods, from the first ones based on experience, such as empirical methods, to the most recent ones on mechanical–empirical methodologies, where, based on different principles, they determine the thicknesses of the layers that integrate the structure of a pavement to identify how these methods have included climate variables within their methodology. Through this review, it was identified that temperature is incorporated in the dynamic modulus of the asphalt mix, and precipitation and moisture are incorporated through the resilient modulus in the granular layers (base, subbase, and foundation soil or subgrade courses). As a result, it was identified that the most holistic way of integrating climate is through the Enhanced Integrated Climatic Model (EICM) from the Mechanistic–Empirical Pavement Design Guide (MEPDG). In many cases, climate is incorporated through parameters whose behavior is associated with temperature and precipitation but does not use the data of these climate variables directly from the project site. The practical incorporation of climate into design methods allows an increase in the certainty of results, ensuring additional climate-resilient pavement structures and increasing their durability and sustainability during their service life.

Soil electrical resistivity is a fundamental parameter in various geotechnical, agricultural, environmental, and engineering applications, as it directly depends on the soil’s moisture content and physical properties. Understanding this relationship is crucial for improving the safety and efficiency of electrical installations. This study analyzes the relationship between soil electrical resistivity and gravimetric moisture content in three soil types, sandy, clayey, and silty, with the aim of comparing the performance of different predictive models under controlled laboratory conditions. Seven fitting models were evaluated, Logarithmic Spline, Radial Basis Function (RBF), Locally Estimated Scatterplot Smoothing (LOESS), Least Absolute Shrinkage and Selection Operator (LASSO), Ridge Regression (RIDGE), Power Law and a segmented equation, using metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and coefficient of determination $R^2$ . The Spline and RBF models showed excellent accuracy and near-zero errors in all soils, although their applicability is limited by the lack of an explicit formulation and by the fact that, as interpolation methods, they do not guarantee predictive capacity outside the experimental dataset. Therefore, their use should be restricted to controlled laboratory conditions, as field variability factors can significantly alter soil resistivity responses. Among the explicit models, the Segmented Equation obtained a MAPE of 6.14% (sandy), 15.1% (clayey), and 13.16% (silty), with $R^2$ values of 0.91, 0.93, and 0.89, respectively, demonstrating good performance and functionality. The Power Law model, although showing an $R^2$ close to 0.96, presented significant overestimations, especially in silty soils (MAPE > 187%). The LASSO model yielded inconsistent predictions with percentage errors exceeding 120% in silty soils. In conclusion, nonparametric models provide excellent accuracy, while the segmented equation stands out as the best explicit alternative for estimating resistivity with reasonable precision.