Mapping of acid sulfate soil types in Laihianjoki River catchment: A multiclass classification

Mapping of acid sulfate soils (ASS) has in the past focused on ASS probability maps, which are very useful to avoid environmental damage caused by these soils. However, these maps do not indicate the ASS subtypes, which may have different environmental impacts depending on whether they are actively releasing acidity and metals (sulfuric soils) or have the potential to do so (hypersulfidic soils) if the sulfidic material within them is disturbed (oxidized). Additionally, there is a particular type of soil that is close to being classified as an ASS, but where the pH criterion is not fulfilled. This soil is referred as para-ASS and may have a similar negative environmental impact as ASS. In the risk assessment of ASS, it is therefore crucial to know the location of ASS subtypes as well as para-ASS. In this study, we have created for the first time a multiclass map of ASS subtypes. Furthermore, four probability maps have been generated, one for each class. For this, the suitability of two machine learning methods for multiclass classification of different ASS subtypes has been evaluated. The methods are Random Forest (RF) and Gradient Boosting (GB), which showed very high capabilities for the classification of ASS in binary classification [1-3]. RF has given the best results with F1-score values between 71% and 80% for the four classes. An accurate and realistic multiclass map of the ASS subtypes has been created using the RF model.

[1] V. Estévez et al. 2022.  “Machine learning techniques for acid sulfate soil mapping in southeastern Finland”. Geoderma 406 (2022) 115446.

[2] V. Estévez et al. 2023. “Improving prediction accuracy for acid sulfate soil mapping by means of variable selection”. Front. Environ. Sci. 11:1213069 (2023).

[3] V. Estévez et al. 2024.  “Acid sulfate soil mapping in western Finland: How to work with imbalanced datasets and machine learning”. Geoderma 447 (2024) 116916.