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<p>Microbial biomass carbon (MBC) is a key indicator of soil quality, and comprehensive knowledge of MBC is essential for sustainable environmental management and effective monitoring of soil quality changes. However, directly measuring MBC is time-consuming and costly. To overcome these limitations, artificial intelligence (AI) models offer a promising alternative by analyzing easily measurable soil properties. In this study, soil parameters such as organic carbon (OC), clay content, bicarbonate anion (HCO<sub>3</sub><sup>−</sup>), and total nitrogen (TN) concentrations were used as input features for AI models to predict MBC. A novel hybrid model combining a multilayer perceptron artificial neural network (MLP) with the Hunger Games Search (HGS) optimization algorithm, referred to as the Hybrid HGS–MLP, was developed. The predictive performance of this model was evaluated against several conventional models, including standalone MLP, Gene Expression Programming (GEP), Adaptive Neuro-Fuzzy Inference System (ANFIS), Multivariate Adaptive Regression Splines (MARS), and Multiple Linear Regression (MLR). Model accuracy and reliability were assessed using the coefficient of determination (R<sup>2</sup>), root mean square error (RMSE), and mean absolute error (MAE). Results showed that all AI-based models (Hybrid HGS–MLP, standalone MLP, and GEP) achieved an average relative error of less than 1%, demonstrating strong accuracy for MBC estimation. Notably, the hybrid HGS–MLP model achieved R<sup>2</sup>, RMSE, and MAE values of 0.943, 0.009, and 0.007&#xa0;mg CO<sub>2</sub>-C kg<sup>−1</sup> soil, respectively. Compared to the standalone MLP, GEP, ANFIS, MARS, and MLR models, the hybrid HGS–MLP exhibited RMSE reductions of 14.02%, 27.25%, 32.45%, 45.55%, and 51.23%, respectively.</p> Graphical abstract <p></p>

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Estimation of the microbial biomass carbon of soil using a hybrid multilayer perceptron-hunger games search algorithm

  • 期刊论文

  • 发表日期：

    2026年4月29日

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  • Samad Emamgholizadeh,
  • Changhyun Jun,
  • Sayed M. Bateni,
  • Mohammad Reza S. Nobariyan,
  • Toraj A. Badrloo,
  • Christopher M. U. Neale,
  • Hamid Reza Asghari,
  • Shahab S. Band,
  • Meghdad Biyari,
  • Rasoul Ameri

摘要

Microbial biomass carbon (MBC) is a key indicator of soil quality, and comprehensive knowledge of MBC is essential for sustainable environmental management and effective monitoring of soil quality changes. However, directly measuring MBC is time-consuming and costly. To overcome these limitations, artificial intelligence (AI) models offer a promising alternative by analyzing easily measurable soil properties. In this study, soil parameters such as organic carbon (OC), clay content, bicarbonate anion (HCO3−), and total nitrogen (TN) concentrations were used as input features for AI models to predict MBC. A novel hybrid model combining a multilayer perceptron artificial neural network (MLP) with the Hunger Games Search (HGS) optimization algorithm, referred to as the Hybrid HGS–MLP, was developed. The predictive performance of this model was evaluated against several conventional models, including standalone MLP, Gene Expression Programming (GEP), Adaptive Neuro-Fuzzy Inference System (ANFIS), Multivariate Adaptive Regression Splines (MARS), and Multiple Linear Regression (MLR). Model accuracy and reliability were assessed using the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). Results showed that all AI-based models (Hybrid HGS–MLP, standalone MLP, and GEP) achieved an average relative error of less than 1%, demonstrating strong accuracy for MBC estimation. Notably, the hybrid HGS–MLP model achieved R2, RMSE, and MAE values of 0.943, 0.009, and 0.007 mg CO2-C kg−1 soil, respectively. Compared to the standalone MLP, GEP, ANFIS, MARS, and MLR models, the hybrid HGS–MLP exhibited RMSE reductions of 14.02%, 27.25%, 32.45%, 45.55%, and 51.23%, respectively.

Graphical abstract