Sustainable soil organic carbon prediction using machine learning and the ninja optimization algorithm

Soil organic carbon (SOC) plays a critical role in global carbon cycling, influencing climate regulation, soil fertility, and sustainable land management. However, accurate SOC prediction remains a challenging task due to the complex, high-dimensional, and nonlinear nature of soil data. Recent advances in machine learning (ML) have improved SOC estimation, yet these models often suffer from overfitting and computational inefficiency when effective feature selection and hyperparameter tuning are not applied. To address these challenges, we propose a novel integration of the Ninja Optimization Algorithm (NiOA) for simultaneous feature selection and hyperparameter optimization, aimed at enhancing both predictive accuracy and computational efficiency. In our experimental setup, 80% of the dataset was allocated for training and 20% for testing. The baseline Support Vector Machine (SVR) model achieved a mean squared error (MSE) of 0.00513, which was reduced to 0.00011 after applying binary NiOA (bNiOA) for feature selection. After full NiOA-based hyperparameter tuning, the MSE improved further to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m1"><mml:mrow><mml:mn>7.52</mml:mn><mml:mo>×</mml:mo><mml:mn>1</mml:mn><mml:msup><mml:mrow><mml:mn>0</mml:mn></mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:mn>7</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math> , corresponding to a 99.98% reduction in prediction error. Thus, the proposed NiOA-enhanced framework demonstrates considerable potential in advancing SOC modeling. It offers a scalable, interpretable, and high-precision solution that can be effectively applied in data-scarce environments, particularly in support of sustainable land management and climate change adaptation strategies.