Modeling air temperature in forested areas using machine learning

Massoud Emamy Forooshani, Alexander Gegov, Nick Pepin, Mo Adda

Research output: Contribution to journalArticlepeer-review

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Abstract

Air Temperature is a fundamental measure of the Earth’s climate but is only measured at fixed locations. Land surface temperature can be measured widely using satellites. To estimate air temperature (Ta) from the surface temperature (Ts) measured on the forested slopes of Kilimanjaro, four models with unique sets of inputs were tested using five machine learning algorithms. The RMSE for each model was compared with a benchmark model. Models and algorithms were ranked according to their RMSE (Root Mean Square Error) The models and algorithms reliability and consistency ranking were calculated. The best model and algorithm were determined. Novel models results were compared with the benchmark model. All models outperformed the benchmark model in the consistency ranking while three out of four models outperformed the benchmark model in the reliability ranking. Thus machine learning improves the estimation of air temperature in this forested environment.
Original languageEnglish
Pages (from-to)180-188
JournalWSEAS Transactions on Computers
Volume20
DOIs
Publication statusPublished - 3 Jun 2022

Keywords

  • Machine learning
  • air temperature
  • surface temperature
  • Kilimanjaro

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