首页|Machine learning techniques for acid sulfate soil mapping in southeastern Finland

Machine learning techniques for acid sulfate soil mapping in southeastern Finland

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  • NSTL
  • Elsevier
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.

Acid sulfate soilsSoil probability mappingMachine learningRandom forestGradient boostingSupport vector machine

Estevez, Virginia、Beucher, Amelie、Mattback, Stefan、Boman, Anton、Auri, Jaakko、Bjork, Kaj-Mikael、Osterholm, Peter

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Arcada Univ Appl Sci, Jan Magnus Janssonin Aukio 1, Helsinki 00550, Finland

Aarhus Univ, Dept Agroecol, Blichers Alle 20,POB 50, DK-8830 Tjele, Denmark

Abo Akad Univ, Geol & Mineral, Domkyrkotorget 1, Turku 20500, Finland

Geol Survey Finland, POB 97, Kokkola 67101, Finland

Geol Survey Finland, POB 96, Espoo 02151, Finland

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2022

10.1016/j.geoderma.2021.115446

Geoderma: An International Journal of Soil Science

Geoderma: An International Journal of Soil Science

ISSN：0016-7061
年,卷(期)：2022.406
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