| Issue |
E3S Web Conf.
Volume 431, 2023
XI International Scientific and Practical Conference Innovative Technologies in Environmental Science and Education (ITSE-2023)
|
|
|---|---|---|
| Article Number | 03005 | |
| Number of page(s) | 6 | |
| Section | Mining, Geology and Geotechnology | |
| DOI | https://doi.org/10.1051/e3sconf/202343103005 | |
| Published online | 13 October 2023 | |
Investigation of the influence of geographical factors on soil suitability using a nonparametric controlled method of training and data analysis
1 Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia
2 Reshetnev Siberian State University of Science and Technology, 660037 Krasnoyarsk, Russia
3 Peter the Great St.Petersburg Polytechnic University, St.Petersburg, Russia
* Corresponding author: rhfdwjdr1@gmail.com
This paper analysed a dataset using a selected data analysis tool. The study found that decision tree was a suitable tool to analyse this data set. Special attention was given to the analysis of geographical factors including an assessment of the presence of water bodies in the county. The analysis showed that these factors have a significant impact on soil workability. Although the model based on these factors did not have absolute accuracy (14% error), it was still acceptable and cheaper to implement. One of the main advantages of using geographical factors to predict soil workability is their easy availability. Data on the presence of water bodies and other geographical indicators can be easily found and used in the analysis. The analysis thus confirms the effectiveness of using decision tree in combination with geographical factors to analyse datasets related to soil serviceability. Despite some inaccuracy of the model, its relative simplicity and accessibility make it an attractive tool for forecasting and decision making in this area.
© The Authors, published by EDP Sciences, 2023
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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