Acid sulfate soil mapping using machine learning

Background and goals

Acid sulfate (AS) soils can cause environmental damage and geotechnical problems. For this, they are considered the most harmful soils in nature. In Europe, the highest concentrations of AS soils are located in the coastal areas of Finland. This represents one of the main environmental problems of the country. To mitigate the damage, it is essential to accurately locate the areas where these soils appear. The present study focuses on the AS soil mapping using machine learning techniques.

Objectives and benefits

The main objective of this project is to create accurate AS soil maps using different machine learning techniques. These maps will have different applications and will be used by territorial decision makers to mitigate the ecological damage that this type of soil can cause.
The results of this research will be published in several high-impact peer-reviewed scientific journals in the field of soil sciences. Furthermore, the results will be presented at international conferences on the subject.

Results

In this project, different machine learning methods will be used for AS soil mapping. Specifically, risk and AS probability maps will be created for several areas under risk of environmental damage. In this way, it is expected to prevent possible environmental hazards.

Societal impact

It is expected that the results obtained during this proposed project have a high impact not only on a scientific level but also on a social and economic level. The new knowledge of AS soils as well as the creation of AS soil maps will contribute to mitigate the environmental damage created by AS soils in watercourses.