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

Author

Listed:
  • Ainagul Abzhanova
  • Zhazira Taszhurekova
  • Bauyrzhan Berlikozha
  • Mira Kaldarova
  • Ardak Batyrkhanov

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).

Suggested Citation

  • Ainagul Abzhanova & Zhazira Taszhurekova & Bauyrzhan Berlikozha & Mira Kaldarova & Ardak Batyrkhanov, 2025. "Development of a hybrid machine learning model for classification of soil types based on geophysical parameters," International Journal of Innovative Research and Scientific Studies, Innovative Research Publishing, vol. 8(3), pages 2173-2181.
    • Handle: RePEc:aac:ijirss:v:8:y:2025:i:3:p:2173-2181:id:6966
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