Work

Soil Salinity Classification Using Machine Learning Algorithms and Radar Data in the Case from the South of Kazakhstan

Year: 2022

Type: article

Abstract: Soil salinity is one of the major impact factors on agriculture in the South of Kazakhstan. Prediction and estimation of soil salinity before planting a season usually helps to plan for the leaching of the salt. In the paper, satellite data, such as radar data and machine learning algorithms, were used to classify soil salinity. Numerical results were presented for the Turkestan region, which contains more than 102 points. The machine learning algorithms, including Gaussian Process, Decision Tree, and Random Forest, were compared. The evaluation of the model score was realized by using metrics such as accuracy, Recall, and f1. In addition, the influence of the dataset features on the classification was investigated using machine learning algorithms. The research results showed that the Gaussian Process model has the best score among considered algorithms. In addition, the results are consistent with the outcome of the Shapley Additive exPlanations (SHAP) framework.

Source: Journal of Ecological Engineering

DOI: 10.12911/22998993/152281

Authors: Timur Merembayev, Yedilkhan Amirgaliyev, Sultan Saurov, Waldemar Wójcik

Institutions: Institute of Information and Computational Technologies, S.Seifullin Kazakh Agro Technical University, Lublin University of Technology

Language: English

Topic: Soil Geostatistics and Mapping

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDGs: Life on land, Zero hunger

Open Access status: gold

Estimated APC paid: $330