Neural Network Structure for Tracking the Climate Temperature Change

Irina Hristova Topalova; Pavlinka Goranova Radoyska

doi:10.20944/preprints202309.0101.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 31 August 2023 / Approved: 1 September 2023 / Online: 4 September 2023 (07:09:01 CEST)

Tracking temperature changes in certain geographic regions is a current task in modern research on Earth's climate changes. One of the global problems in solving this task is related to the large volume of measured data and the search for appropriate methods for effective determination of changes. The purpose of this research is to track climate temperature changes using a machine learning-based automated change detection method. The presented method includes training of a two-level structure of neural networks, with measured temperatures for a ten-year period of time for a certain geographical region. In the testing phase, the neural structure classifies measured temperatures for two three-year periods, before and after the ten-year time period, respectively, for the same geographic region. An algorithm was developed to visualize the studied regions by creating a map with their geographic coordinates. The classification results in the neural structure outputs are presented and analyzed as possible temperature changes. Suggestions for continuing and expanding the research in the future are discussed.

machine learning; neural networks; temperature changes, geografical coordinates

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Neural Network Structure for Tracking the Climate Temperature Change

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