ORIGINAL RESEARCH article
Front. Environ. Sci.
Sec. Big Data, AI, and the Environment
Volume 13 - 2025 | doi: 10.3389/fenvs.2025.1630762
Sustainable Soil Organic Carbon Prediction Using Machine Learning and the Ninja Optimization Algorithm
Provisionally accepted
- 1Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia, Riyadh, Saudi Arabia
- 2Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 11152, Egypt, Mansoura, Egypt
- 3Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, Egypt
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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 7.52 × 10 -7 , 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.
Keywords: Soil organic carbon prediction, Machine learning optimization, Ninja Optimization Algorithm (NiOA), Feature Selection and Hyperparameter Tuning, Precision Environmental Modeling
Received: 18 May 2025; Accepted: 31 Jul 2025.
Copyright: © 2025 BEN GHORBAL, Grine, Eid and El-kenawy. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Anis BEN GHORBAL, Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia, Riyadh, Saudi Arabia
El-Sayed M. El-kenawy, Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, Egypt
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