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.

酸性硫酸盐土壤是自然界中对环境危害最大的土壤之一。这是因为它们会产生硫酸并释放金属，这可能会造成多种生态破坏。在芬兰，沿海地区出现这种类型的土壤是该国的主要环境问题之一。为了解决这个问题，精确定位酸性硫酸盐土壤是必不可少的。因此，需要为这些土壤创建发生图。如今，可以按照数字土壤制图方法使用不同的机器学习方法。本研究的主要目标是评估酸性硫酸盐土壤测绘的不同监督机器学习技术。分析的方法是随机森林、梯度提升和支持向量机。我们表明梯度提升和随机森林是酸性硫酸盐土壤分类的合适方法，所得概率图具有较高的精度。然而，使用支持向量机创建的概率图的准确性较低，因为这种方法高估了非 AS 土壤的出现。我们还将这些建模概率图与常规生成的发生图进行了比较。总的来说，建模地图比传统地图更客观、更准确。此外，使用机器学习技术的映射过程更快且成本更低。我们还将这些建模概率图与常规生成的发生图进行了比较。总的来说，建模地图比传统地图更客观、更准确。此外，使用机器学习技术的映射过程更快且成本更低。我们还将这些建模概率图与常规生成的发生图进行了比较。总的来说，建模地图比传统地图更客观、更准确。此外，使用机器学习技术的映射过程更快且成本更低。