Extensive, but remote oil and gas fields of the United States, Canada, and Russia require
the construction and operation of extremely long pipelines. Global warming and local heating effects
lead to rising soil temperatures and thus a reduction in the sub-grade capacity of the soils; this
causes changes in the spatial positions and forms of the pipelines, consequently increasing the number
of accidents. Oil operators are compelled to monitor the soil temperature along the routes of the
remoted pipelines to be able to perform remedial measures in time. They are therefore seeking methods
for the analysis of volumetric diagnostic information. To forecast soil temperatures at the different
depths we propose compiling a multidimensional dataset, defining descriptive statistics; selecting
uncorrelated time series; generating synthetic features; robust scaling temperature series,
tuning the additive regression model to forecast soil temperatures.