Machine learning assisted prediction of land surface temperature (LST) based on major air pollutants over the Annamayya District of India

Abstract

Remote sensing (RS), Geographic information systems (GIS), and Machine learning can be integrated to predict land surface temperatures (LST) based on the data related to carbon monoxide (CO), Formaldehyde (HCHO), Nitrogen dioxide (NO2), Sulphur dioxide (SO2), absorbing aerosol index (AAI), and Aerosol optical depth (AOD). In this study, LST was predicted using machine learning classifiers, i.e., Extra trees classifier (ET), Logistic regressors (LR), and Random Forests (RF). The accuracy of the LR classifier (0.89 or 89%) is higher than ET (82%) and RF (82%) classifiers. Evaluation metrics for each classifier are presented in the form of accuracy, Area under the curve (AUC), Recall, Precision, F1 score, Kappa, and MCC (Matthew’s correlation coefficient). Based on the relative performance of the ML classifiers, it was concluded that the LR classifier performed better. Geographic information systems and RS tools were used to extract the data across spatial and temporal scales (2019 to 2022). In order to evaluate the model graphically, ROC (Receiver operating characteristic) curve, Confusion matrix, Validation curve, Classification report, Feature importance plot, and t- SNE (t-distributed stochastic neighbour embedding) plot were used. On validation of each ML classifier, it was observed that the RF classifier returned model complexity due to limited data availability and other factors yet to be studied post data availability. Sentinel-5-P and MODIS data are used in this study.

Keywords

• Machine learning
• Geographic information systems
• Sentinel-5-P
• MODIS
• Land surface temperature

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