Model for predicting the occurrence of soil compaction

Abstract

Solving the problem of preserving and increasing soil fertility, timely determination of the causes of its deformation require improved environmental forecasting. The development of quantitative approaches in ecology is facilitated by the availability of data on soil types and properties, understanding of ongoing processes and existing information technologies. In this article, we give an example of designing and training a deep neural network for ecological forecasting of the date of occurrence of soil compaction, as one of the relevant parameters in further research. Using the properties of existing compacted soils, we show that neural networks are able to predict both the short-term risk of soil compaction and the long-term dynamics. Against the background of existing methods, neural networks have better performance and the potential to create integrated soil monitoring systems based on them.