DYNAMIC DATASETS FOR MACHINE LEARNING IN THE SOIL EROSION FORECASTING IT PROJECT

Abstract

The article discusses the use of dynamic datasets for machine learning tools in an IT project for soil erosion forecasting. The essence of the approach is to use algorithms to analyze large volumes of dynamic data on soil, topographic, meteorological and vegetation parameters in order to identify patterns that allow predicting erosion risks. The use of machine learning methods for soil erosion forecasting is a promising direction, as it allows automating the processes of processing large data sets and obtaining forecasts that are much more accurate and faster compared to traditional methods. Thanks to the development of the latest technologies in the field of machine learning, in particular deep learning and classification algorithms, it is possible to create highly accurate forecasts that will allow more effectively preserving soils, preventing erosion and minimizing its negative consequences. The IT project for creating accurate models on sets of dynamic heterogeneous information provides prompt, informed and responsible decision-making for land resource management and preventing soil degradation. It also leads to a deterioration in the water balance, since soil erosion is often accompanied by pollution of water resources, which reduces their quality and suitability for use. In this regard, there is an urgent need to create accurate erosion forecasting models that will help identify areas of increased risk and promote effective management of natural resources. Thanks to the development of the latest technologies in the field of machine learning, in particular deep learning and classification algorithms, it is possible to create highly accurate forecasts that will allow you to more effectively preserve soils, prevent erosion and minimize its negative consequences. This approach allows for the creation of accurate models that help in making informed decisions for land management and preventing soil degradation.