Comparison of Explainable Machine Learning Algorithms for Optimization of Virtual Gas Sensor Arrays

Metal oxide semiconductor (MOS) gas sensors operated in temperature cycled operation (TCO) and calibrated with machine learning algorithms are increasingly promising for indoor air quality (IAQ) assessments. This can be attributed to the cost-efficient sensors, with a broad sensitivity spectrum and the possibility of continuous measurements. However, with the ever-increasing complexity of data-driven models used to calibrate the MOS gas sensors, understanding the connection between the raw input and the predicted gas concentration is especially important. In this work, two methods from the field of explainable AI are applied to our custom neural network (TCOCNN) and compared regarding their capability to identify essential parts of the raw input signal. For this purpose, a validation scheme is introduced to rate the explanation methods. Finally, it is shown that with only 7 % of the original raw input, root-mean-squared error (RMSE) values for formaldehyde that are only 22 % worse compared to the absolute best (15.8 ppb vs. 19.3 ppb) can be achieved. This more profound understanding of the sensor can then be used to show differences between sensors, allow more accessible models to be built, and optimize the temperature-cycled operation regarding the number of temperature steps.