Development of a hybrid machine learning model for classification of soil types based on geophysical parameters

Ainagul Abzhanova (), Zhazira Taszhurekova (), Bauyrzhan Berlikozha (), Mira Kaldarova () and Ardak Batyrkhanov ()

International Journal of Innovative Research and Scientific Studies, 2025, vol. 8, issue 3, 2173-2181

Abstract: In this paper, a hybrid model based on RandomForestClassifier and MLPClassifier is presented, achieving an accuracy of 96.07% in the task of soil classification based on geophysical parameters. The results demonstrate the advantages of the proposed approach over selected classical algorithms, indicating a high practical value for precision agriculture and environmental monitoring. A dataset containing key soil parameters such as electrical conductivity, density, P-wave velocity, and depth was utilized. Prior to training, the data were preprocessed: the target variable was converted to numeric format using LabelEncoder, and the features were standardized using StandardScaler to bring them to a common scale. Data were divided into training and test samples using the train_test_split method (80% training, 20% test).

Keywords: Data preprocessing; Electrical conductivity; Geophysical data; Hybrid model; Information systems; Land classification; Machine learning; Multilayer perceptron; Neural networks; Random forest. (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:aac:ijirss:v:8:y:2025:i:3:p:2173-2181:id:6966

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