| Issue |
BIO Web Conf.
Volume 130, 2024
International Scientific Conference on Biotechnology and Food Technology (BFT-2024)
|
|
|---|---|---|
| Article Number | 03003 | |
| Number of page(s) | 7 | |
| Section | Water Environmental Biotechnology | |
| DOI | https://doi.org/10.1051/bioconf/202413003003 | |
| Published online | 09 October 2024 | |
Development of a model for assessing water quality and its impact on agro-industry using the random forest method
1 Bauman Moscow State Technical University (BMSTU), 105005 Moscow, Russia
2 Reshetnev Siberian State University of Science and Technology, 660037 Krasnoyarsk, Russia
3 Russian State Agrarian University - Timiryazev Moscow Agricultural Academy (RSAU-MAA Named after K.A. Timiryazev), 127550 Moscow, Russia
* Corresponding author: anna_glinskaja@rambler.ru
This article considers the application of the random forest algorithm to build a model designed to assess water quality and analyze its impact on agro-industrial complex. The main objective of the study is to identify the key factors affecting water quality and their interaction with indicators important for agricultural production. The random forest algorithm was chosen for its ability to process large amounts of data and identify complex non-linear dependencies. The random forest model was trained on historical data and tested on new samples to assess its accuracy and reliability. The study analyzed various physical and chemical parameters of water such as pH, organic and inorganic content, mineralization and their impact on agro-industrial indicators including crop yield, soil health and crop health. The results showed that the random forest algorithm is able to effectively classify water quality and identify its impact on agro-industrial complex. Analyzing the importance of attributes allowed us to identify the key parameters that most strongly affect water quality and agricultural land health.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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