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Subsurface elevated temperatures (SETs) often occur in landfills and pose great threats to their structural and environmental integrity. Current landfill gas monitoring practices only recommend maintaining certain soil gases percentages, with no integrated strategy for predicting subsurface temperature. As a solution, this paper proposes a comprehensive risk assessment framework specific to SET mitigation. The risk model (R SET ) was constructed by incorporating independent gas variables (methane, carbon dioxide, oxygen, residual nitrogen, and temperature) identified in the existing literature as SET indicators, and analyzing gas-well data from the Bridgeton Landfill. Upon identifying these gas indictors and their safety thresholds, we found a significant association (p-value < 0.05) between safe-unsafe ranges of gas variables and subsurface temperature. Temperatures above 80 °C were found to be associated with 100%, 92.3%, and only 4% of the unsafe ranges of methane, residual nitrogen, and oxygen, respectively. As the correlation between gases and temperature seemed to vary for different gas combinations, we developed the R SET by incorporating into these correlation coefficients event intensities specific to certain gas combinations, and then normalizing the R SET scale over a 0-10 range. Over the study period, we identified 22.29% of cases as medium risk at the Bridgeton Landfill and 17.7% as high risk. SETs are governed by different combinations of safe-unsafe ranges of parameters rather than any individual parameters alone. Subsequently, we used a decision tree algorithm to assess the risk types associated with R SET values. The proposed R SET can serve as a monitoring and decision-making tool for landfill authorities for managing and preventing SET incidents., Competing Interests: Declaration of competing interest I am writing this letter to indicate that our submission is in compliance with the ethical requirements of the journal and the authors whose names are listed in the article certify that they have NO affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers' bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript., (Copyright © 2021. Published by Elsevier B.V.)