  A methodology is proposed, which addresses the caveat that line-of-sight
emission spectroscopy presents in that it cannot provide spatially resolved
temperature measurements in nonhomogeneous temperature fields. The aim of this
research is to explore the use of data-driven models in measuring temperature
distributions in a spatially resolved manner using emission spectroscopy data.
Two categories of data-driven methods are analyzed: (i) Feature engineering and
classical machine learning algorithms, and (ii) end-to-end convolutional neural
networks (CNN). In total, combinations of fifteen feature groups and fifteen
classical machine learning models, and eleven CNN models are considered and
their performances explored. The results indicate that the combination of
feature engineering and machine learning provides better performance than the
direct use of CNN. Notably, feature engineering which is comprised of
physics-guided transformation, signal representation-based feature extraction
and Principal Component Analysis is found to be the most effective. Moreover,
it is shown that when using the extracted features, the ensemble-based, light
blender learning model offers the best performance with RMSE, RE, RRMSE and R
values of 64.3, 0.017, 0.025 and 0.994, respectively. The proposed method,
based on feature engineering and the light blender model, is capable of
measuring nonuniform temperature distributions from low-resolution spectra,
even when the species concentration distribution in the gas mixtures is
unknown.
