Search

Abstract Geomagnetic storms can cause variations in the ionization levels of the ionosphere, which is commonly studied using the total electron content (TEC). TEC is a crucial parameter to identify the possible effects of ionospheric variations on satellite communication and navigation. This paper assesses the performance of light gradient boosting machine (LGB) and deep neural network (DNN) machine learning algorithms in modeling ionospheric vertical TEC (VTEC) during geomagnetic disturbances. GPS VTEC data for years 2011–2016 from 13 dual-frequency receiver stations over Ethiopia was utilized. Input parameters for the models were derived from the factors that influence VTEC, such as time, location, geomagnetic activity, solar activity, solar wind, and the interplanetary magnetic field. The LGB model improved the predictions of the DNN model from root mean squared error (RMSE), mean absolute percentage error (MAPE), and R2 values of 5.45 TECU, 21%, and 0.93 to 4.98 TECU, 18%, and 0.94 on the testing data, respectively. The two machine learning models significantly outperformed the International Reference Ionosphere (IRI 2020) model during the selected geomagnetic storm periods. This study could provide insight into the impacts of ionosphere variations on satellite communication and navigation systems in the low-latitude ionospheric region.

Calcined clays as supplementary cementitious materials (SCMs) have the potential to answer the need for SCMs because they have many resources in comparison with other SCMs. Moreover, calcined clays can be combined with limestone, which makes limestone calcined clay cement (LC3). LC3 can reduce the clinker of cement, which leads to the reduction of carbon dioxide pollution. The application of calcined clays as SCMs can make concrete with lower cost and the same compressive strength in comparison with other SCMs such as fly ash. Therefore, in this research, a predictive model has been built by the application of an artificial neural network (ANN), which can encourage the industry to increase the clay application in concrete. On the other hand, for improving the performance of the ANN model, four different optimization algorithms, such as genetic algorithm (GA), Bayesian optimization with a Gaussian process (BO-GP), Bayesian optimization with tree parzen estimator (BO-TPE), and hyperband algorithm, have been applied to optimize the number of neurons, number of hidden layers, activation function, number of batches, and epochs to predict the compressive strength of concrete. The results performance of models showed the superiority of the hyperband algorithm in running time and accuracy. [ABSTRACT FROM AUTHOR]