Solving the problem of classifying forest cover types based on soil characteristics

D. T. Muhamediyeva; L. U. Safarova; S. S. Nabiyeva

doi:10.1117/12.3024969

20 February 2024 Solving the problem of classifying forest cover types based on soil characteristics

D. T. Muhamediyeva, L. U. Safarova, S. S. Nabiyeva

Proceedings Volume 13065, Third International Conference on Optics, Computer Applications, and Materials Science (CMSD-III 2023); 130650I (2024) https://doi.org/10.1117/12.3024969
Event: Third International Conference on Optics, Computer Applications, and Materials Science (CMSD-III 2023), 2023, Dushanbe, Tajikistan

Abstract

The "covtype" dataset in scikit-learn represents forest cover information and includes a variety of soil characteristics for seven different forest cover types. The proposed work solves the classification problem, where the goal is to accurately determine the type of forest cover based on given soil characteristics. The study uses various machine learning methods such as decision trees and naive Bayes classifier. Models are trained on an extensive training set and then evaluated on test data to determine their ability to accurately predict forest cover types. The classification results are analyzed, including metrics of accuracy, recall, F1-measures, as well as ROC curves are constructed and the areas under them (AUC) are calculated. The results and metrics obtained allow us to compare the effectiveness of different models in solving a given classification problem. The knowledge gained can be useful for the application of machine learning algorithms in ecology and forest resource management.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

D. T. Muhamediyeva, L. U. Safarova, and S. S. Nabiyeva "Solving the problem of classifying forest cover types based on soil characteristics", Proc. SPIE 13065, Third International Conference on Optics, Computer Applications, and Materials Science (CMSD-III 2023), 130650I (20 February 2024); https://doi.org/10.1117/12.3024969

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KEYWORDS

Decision trees

Data modeling

Ecosystems

Education and training

Environmental monitoring

Library classification systems

Mathematical optimization

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