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Amélie Marie Beucher

Machine learning techniques for acid sulfate soil mapping in southeastern Finland

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  • Virginia Estévez, Arcada University of Applied Sciences, Finland
  • Amélie Marie Beucher
  • Stefan Mattbäck, Åbo Akademi University, Geological Survey of Finland, Finland
  • Anton Boman, Geological Survey of Finland, Finland
  • Jaakko Auri, Geological Survey of Finland
  • ,
  • Kaj-Mikael Björk, Arcada University of Applied Sciences, Finland
  • Peter Österholm, Åbo Akademi University
Acid sulfate soils are one of the most environmentally harmful soils existing in nature. This is because they
produce sulfuric acid and release metals, which may cause several ecological damages. In Finland, the occurrence
of this type of soil in the coastal areas constitutes one of the major environmental problems of the country. To
address this problem, it is essential to precisely locate acid sulfate soils. Thus, the creation of occurrence maps for
these soils is required. Nowadays, different machine learning methods can be used following the digital soil
mapping approach. The main goal of this study is the evaluation of different supervised machine learning
techniques for acid sulfate soil mapping. The methods analyzed are Random Forest, Gradient Boosting and
Support Vector Machine. We show that Gradient Boosting and Random Forest are suitable methods for the
classification of acid sulfate soils, the resulting probability maps have high precision. However, the accuracy of
the probability map created with Support Vector Machine is lower because this method overestimates the non-AS
soils occurrences. We also compare these modeled probability maps with the conventionally produced occurrence
map. In general, the modeled maps are more objective and accurate than the conventional maps. Moreover,
the mapping process using machine learning techniques is faster and less expensive.
Original languageEnglish
Article number115446
Number of pages11
Publication statusPublished - Jan 2022

    Research areas

  • Acid sulfate soils, Gradient boosting, Machine learning, Random forest, Soil probability mapping, Support vector machine

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