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Constraining the thermal inertia of Mars utilizing machine learning techniques by Hongqing Song; Jie Zhang; Shuyi Du; Dongdong Ni; Yang Liu; Yueqiang Sun is a Physics and Astronomy article available to read on EtoBox.

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Abstract Machine learning techniques, showing high automation and efficiency in handling large amounts of observation data, have been applied to predict the thermal inertia of Mars from surface kinetic temperatures. We created a large data set from well-established thermal models. Using this data set, we trained random forest (RF) models using surface kinetic temperatures, time of day and other five accessible parameters as inputs to the model. The model performances for different local times were analyzed and the characteristics of derived thermal inertia in typical regions on Mars were discussed. It is found that it is feasible and reliable to predict the thermal inertia of Mars using the well-trained RF. The RF predictions reflect the thermal signatures of Mars and show good agreement with previous retrievals. When using the nighttime data to make predictions, the RF model shows the best performance compared with those at other times of day. We also classified thermal inertia into four units: low, intermediate, relatively large, and large thermal inertia, and the RF model works for all four units. The predictive ability of the RF is also demonstrated for five representative regi

Who reads Constraining the thermal inertia of Mars utilizing machine learning techniques?

Author

Hongqing Song; Jie Zhang; Shuyi Du; Dongdong Ni; Yang Liu; Yueqiang Sun

Publisher

Oxford University Press (OUP)

Published

2023

Language

EN

Field

Physics and Astronomy (Physical Sciences)