Predicting SnO₂ Sensor Response for Hydrogen Detection Using Artificial Intelligence Techniques

SnO2-based nanocomposites are reliable sensors to detect hydrogen leakage and satisfy safety protocols. Although the hydrogen detection response (HDR) of these sensors has been deeply studied in the laboratory, there are no models to estimate this parameter. Consequently, this study uses three machine learning classes (i.e., gene expression programming, support vector regression, and artificial neural network) to calculate the HDR of pure and Ag-, Co-, Pd-, Pt-, and Ru-decorated SnO2 nanostructures. These models only need nanocomposite chemistry and operating conditions to estimate the HDR of SnO2-based sensors. Comparing these models’ performance by the ranking analysis and spider-graph indicates the multilayer perceptron neural network is superior to the other models. This model shows the highest accuracy (regression coefficient = 0.9882, average absolute deviation = 2.74, and root mean squared errors = 8.05) for estimating the HDR of SnO2-based sensors. This model also anticipates that Pd and Ru are the best and worst dopants to decorate the SnO2-based sensors.