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Abstract <p>This study focused on modeling specific soil properties in the Bingöl Solhan Plain using soil samples collected from 85 coordinates at 0–30 cm depth, with 300 × 300 m intervals. Artificial intelligence techniques, implemented with ArcGIS 10.8 software, were employed to estimate soil characteristics efficiently and cost-effectively. The input data for the artificial neural network models consisted of electrical conductivity (EC) and clay content, while the output parameters included exchangeable cation percentage, cation exchange capacity, field capacity, and wilting point. Soil characteristics were determined through physical, chemical, and biological laboratory tests. The training process involved three algorithms: Levenberg-Marquardt (LM), scaled conjugate gradient (SCG), and bayesian regularization (BR). To enhance model performance, hyperparameter optimization was applied, with the number of iterations and network structures (comprising double hidden layers with 0–30 neurons per layer) adjusted. The training dataset was divided into 70% training, 15% testing, and 15% validation. Among the algorithms tested, the Levenberg-Marquardt algorithm yielded the best performance across all estimations. It achieved 94% accuracy with mean absolute error (MAE) and root mean squared error (RMSE) values of 0.04 and 0.046, respectively, for exchangeable cation percentage prediction. For cation exchange capacity estimation, it attained 84% accuracy, with MAE and RMSE values of 0.081 and 0.091, respectively. In field capacity estimation, it achieved 79% accuracy, with MAE and RMSE values of 0.095 and 0.118, respectively. For wilting point estimation, it reached 86%&#xa0;accuracy, with MAE and RMSE values of 0.057 and 0.067 respectively. The results demonstrate that the Levenberg-Marquardt algorithm consistently produced the highest <i>R</i><sup>2</sup> values and the lowest MAE and RMSE across all estimation models. This study highlights the potential of artificial intelligence techniques, particularly the Levenberg-Marquardt algorithm, to accurately model complex soil parameters that are traditionally time-consuming and costly to analyze.</p>

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Modeling Soil Quality Parameters in Solhan Plain Using Neural Networks and Comparing Different Algorithms for Prediction Accuracy

  • 期刊论文

  • 发表日期：

    2025年7月15日

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  • V. Alp,
  • S. Aydemir,
  • A. V. Bilgili,
  • A. R. Demirkıran,
  • M. Kılıc

摘要

Abstract

This study focused on modeling specific soil properties in the Bingöl Solhan Plain using soil samples collected from 85 coordinates at 0–30 cm depth, with 300 × 300 m intervals. Artificial intelligence techniques, implemented with ArcGIS 10.8 software, were employed to estimate soil characteristics efficiently and cost-effectively. The input data for the artificial neural network models consisted of electrical conductivity (EC) and clay content, while the output parameters included exchangeable cation percentage, cation exchange capacity, field capacity, and wilting point. Soil characteristics were determined through physical, chemical, and biological laboratory tests. The training process involved three algorithms: Levenberg-Marquardt (LM), scaled conjugate gradient (SCG), and bayesian regularization (BR). To enhance model performance, hyperparameter optimization was applied, with the number of iterations and network structures (comprising double hidden layers with 0–30 neurons per layer) adjusted. The training dataset was divided into 70% training, 15% testing, and 15% validation. Among the algorithms tested, the Levenberg-Marquardt algorithm yielded the best performance across all estimations. It achieved 94% accuracy with mean absolute error (MAE) and root mean squared error (RMSE) values of 0.04 and 0.046, respectively, for exchangeable cation percentage prediction. For cation exchange capacity estimation, it attained 84% accuracy, with MAE and RMSE values of 0.081 and 0.091, respectively. In field capacity estimation, it achieved 79% accuracy, with MAE and RMSE values of 0.095 and 0.118, respectively. For wilting point estimation, it reached 86% accuracy, with MAE and RMSE values of 0.057 and 0.067 respectively. The results demonstrate that the Levenberg-Marquardt algorithm consistently produced the highest R2 values and the lowest MAE and RMSE across all estimation models. This study highlights the potential of artificial intelligence techniques, particularly the Levenberg-Marquardt algorithm, to accurately model complex soil parameters that are traditionally time-consuming and costly to analyze.