In the rapidly developing field of smart cities, accurately predicting weather conditions plays a vital role in various sectors, including industry, tourism, agriculture, social planning, architecture, and economic development. Unfortunately, the instruments used (such as pyranometers, barometers, and thermometers) often suffer from low accuracy, high computational costs, and a lack of robustness. This limitation affects the reliability of weather predictions and their application across these critical areas. This study proposes artificial neural network-multilayer perceptrons (ANN-MLPs). A dataset of 480 data points was used, with 80% allocated for the training phase, 10% for the validation phase, and 10% for the testing phase. The best results were obtained with the structure 6-17-1 (6 inputs, 17 hidden neurons, and 1 output neuron) to predict weather condition data in the Ghardaïa district. Weather conditions parameters include air temperature, relative humidity, wind speed, and cumulative precipitation. Results showed that the most relevant input factors are, in order of importance: earth-sun distance (DT-S) with a relative importance (RI) of 31.10%, factor conversion (d) with an RI of 26.05%, and solar radiation (SR) with an RI of 16.26%. The contribution of the elevation of the sun (HI) has an RI of 13.29%. The optimal configuration includes seventeen neurons in the hidden layer with a logistic sigmoid activation function and a Levenberg–Marquardt learning algorithm, resulting in a root mean square error (RMSE) of 3.3043% and a correlation coefficient (R) of 0.9683. The proposed model can predict both short- and long-term climate factors such as solar radiation, air temperature, and wind energy in areas with similar conditions.

In this study, an artificial neural network (ANN) is used to develop predictive models for estimating molecular diffusion coefficients of various gases at multiple pressures over a large field of temperatures. Two feed-forward neural networks NN1 and NN2 are trained using six physicochemical properties: molecular weight, critical volume, critical temperature, dipole moment, temperature, and pressure for NN1 and molecular weight, critical pressure, critical temperature, dipole moment, temperature, and pressure for NN2. The diffusion coefficients are regarded as the output. A set of 1252 gases (941 non-polar gases and 311 polar gases) is used for training and testing the ANN performance, and good correlations are found (R = 0.986 for NN1 and R = 0.988 for NN2). The result of the sensitivity analysis shows the importance of the six input parameters selected for modeling the diffusion coefficient. Moreover, the present ANN model provides more accurate predictions than other models.

This study addresses the pervasive challenge of membrane filtration clogging across various industries. Eight new empirical fractional models are proposed based on the volume accumulation change curve. The effectiveness of these models in predicting material accumulation and characterizing clogging patterns is evaluated. The models are validated against experimental data, achieving impressive coefficients of determination (R²) between 0.9896 and 0.9997 and relative root mean squared errors (nRMSE) ranging from 0.8674% to 2.9548%. Furthermore, comparing the results with theoretical models of Hermia allows us to relate the empirical models to clogging mechanisms.