Ground Temperature Forecast for a Linear Extended Object


In the permafrost zones along a linear extended object the thawing halos arise leading to a change in its spatial position and deformation. It is advisable to divide the forecast of the ground temperature of the route of the linear extended object into four stages.

At the first stage, a statistical analysis of the ground temperature measurements by spatial and temporal sections is carried out, correlated measurements are revealed layer by layer, time series are checked for stationarity and single roots.

At the second stage, a multidimensional space of features is visualized relative to the identified target features, using, among other things, down sampling and the principal component analysis method.

At the third stage, the most important features that carry the maximum information about the dataset are selected based on the dispersion that uncorrelated features cause in the target variable. Synthetic features are created, for example, by line-by-line construction of a linear regression model.

At the fourth stage, the predictive model of ground temperatures along the route is determined, for which the models are trained considering the time by highlighting the segment from the general data set, for which forecasting is performed. The temperature forecast is made using models considering the trend and (or) seasonal fluctuation.

Based on the experimental data obtained over a long period in the process of field measurements of ground temperatures along the route of a linear extended object, the following was determined: the actual depth of the zone of zero annual temperature fluctuations; monthly average ground temperatures at different depths; a family of models predicting cyclic changes in the ground temperature.

The results of the study allow to increase the accuracy of forecasting changes in the temperature field of ground along the linear extended object by 11 %.

  1. Bakhtiy N.S., Aristov A.A., Khodanovich D.A., Misharin M.V., Tupitsin M.S. Hydrodynamic modeling of the main fields of OAO «Surgutneftegas» using supercomputer technologies. Neftyanoe khozyaystvo = Oil Industry. 2017. № 5. pp. 64–67. (In Russ.). DOI: 10.24887/0028-2448-2017-5-64-67
  2. Reutskikh N.V., Berezhnoy M.A., Dudenko I.A. Geotechnical monitoring for the main pipelines in various types of the permafrost formations. Nauchnyy zhurnal Rossiyskogo gazovogo obshchestva = Scientific Journal of the Russian Gas Society. 2016. № 2. pp. 22–26. (In Russ.).
  3. Vladova A.Yu. Algorithmic support of the information system for geotechnical monitoring of hydrocarbon transport in the permafrost conditions. Informatsionnye tekhnologii = Information Technologies. 2017. Vol. 23. № 3. pp. 205–212. (In Russ.).
  4. Gishkelyuk I.A., Stanilovskaya Yu.V., Evlanov D.V. Prediction of the permafrost soil thawing around a long underground pipeline. Nauka i tekhnologii truboprovodnogo transporta nefti i nefteproduktov = Science & Technologies: Oil and Oil Products Pipeline Transportation. 2015. Vol. 1. № 17. pp. 20–25. (In Russ.).
  5. Bogdanov A.I. The system of temperature monitoring of the perennial soils condition in a roadbed of the Northern Latitudinal Railway. Proektirovanie razvitiya regionalnoy seti zheleznykh dorog: sb. nauch. tr. (Design of the development of the regional railway network: Proceedings). Khabarovsk: Izd-vo DVGUPS, 2014. Iss. 2. pp. 155–164. (In Russ.).
  6. Zhao X., Li W., Zhou L., Song G., Ba Q., Ho S.C.M., Ou J. Application of support vector machine for pattern classification of active thermometry‐based pipeline scour monitoring. Structural Control and Health Monitoring. 2015. Vol. 22. Iss. 6. pp. 903–918.
  7. Ni P., Mangalathu S., Liu K. Enhanced fragility analysis of buried pipelines through Lasso regression. Acta Geotechnica. 2020. Vol. 15. Iss. 2. pp. 471–487.
  8. Shi F., Peng X., Liu Z., Li E., Hu Y. A data-driven approach for pipe deformation prediction based on soil properties and weather conditions. Sustainable Cities and Society. 2020. Vol. 55. DOI: 10.1016/j.scs.2019.102012
  9. Vladova A.Yu., Vladov Yu.R. Machine Classification of Pore Space for Hydrocarbon Reservoir Characterization. IEEE 21st Conference on Business Informatics (CBI). 2019. Vol. 1. pp. 391–396.
  10. Flakh P. Computer-assisted learning. The science and art of building algorithms that extract knowledge from data. Moscow: DMK Press, 2015. 400 p.
  11. Vladova A.Yu. Clustering Analysis of Changes in the Spatial Position of the Trunk Oil Pipeline Sections Based on the In-line Inspection Datasets. Bezopasnost truda v promyshlennosti = Occupational Safety in Industry. 2018. № 1. pp. 22–25. (In Russ.). DOI: 10.24000/0409-2961-2018-1-22-25
  12. Skiena S.S. The data science design manual. Springer, 2017. 445 p.
DOI: 10.24000/0409-2961-2020-6-14-20
Year: 2020
Issue num: June
Keywords : pipeline machine learning linear extended object geotechnical monitoring heat transfer Holt — Winters model trend seasonal fluctuation time series
  • Vladova A.Yu.
    Dr. Sci. (Eng.), Lead Researcher ICS RAS, Moscow, Russia Prof. Financial University under the Government of the Russian Federation, Moscow, Russia
  • Vladov Yu.R., Dr. Sci. (Eng.), Prof., Laboratory Head OFRC UrO RAN, Orenburg, Russia Lead Researcher NOTs, Orenburg State University, Orenburg, Russia