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Machine learning insoil science forprediction andmanagement ofbiological activityfor sustainable landuse Kukartseva SvetlanaEvsyukov DmitryOrlov Vasiliy2024 год

Machine learning in soil science for prediction and management of biological activity for sustainable land use
статья из журнала

Дополнительные ссылки: eLibrary
База данных: Каталог библиотеки СФУ
Библиографическое описание: Machine learning in soil science for prediction and management of biological activity for sustainable land use : научное издание / S. S. Kukartseva [и др.]. - Текст : непосредственный // E3S Web of Conferences. - 2024. - Т. 583, № . - С. 01011. - ISSN 2555-0403. - ISSN 2267-1242, DOI 10.1051/e3sconf/202458301011.
Аннотация: <jats:p>The article discusses the use of machine learning methods for predicting and managing soil biological activity, which is a key aspect of sustainable land use. The development of a random forest model for predicting the Respiration parameter based on data on the physical and chemical characteristics of the soil collected in various areas of Baltimore, Maryland is shown. The model has demonstrated an accuracy of about 70%, which highlights its potential for application in the agricultural sector. The results of visualization of the distribution of actual and predicted values, as well as the analysis of prediction errors are presented. Prospects for further improvement of the model using a genetic algorithm to optimize hyperparameters and integrate additional data such as climatic conditions and historical land use data are discussed. The findings highlight the importance of using machine learning to improve agricultural production efficiency and minimize environmental impacts.</jats:p>
Год издания: 2024
Авторы: Kukartseva Svetlana, Evsyukov Dmitry, Orlov Vasiliy, Kukartsev Anatoly, Poddubny Andrey
Издательство: EDP Sciences - Web of Conferences, Les Ulis
Источник: E3S Web of Conferences
Выпуск: Т. 583
Номера страниц: 01011
Количество экземпляров:
Твёрдая копия издания отсутствует в фонде библиотеки

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