# Predicting and Mapping of Soil Organic Carbon Using Machine Learning Algorithms in Northern Iran

arXiv: https://arxiv.org/abs/2007.12475 · HTML: https://mlsanity.com/paper/2007.12475/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jul 2020.

## Built on by (most cited first, 10 of 10)

- [SSL-SoilNet: A Hybrid Transformer-based Framework with Self-Supervised Learning for Large-scale Soil Organic Carbon Prediction](https://mlsanity.com/paper/2308.03586/) (2023)
- [Knowledge Guided Representation Learning and Causal Structure Learning in Soil Science](https://mlsanity.com/paper/2306.09302/) (2023)
- [Causal Modeling of Soil Processes for Improved Generalization](https://mlsanity.com/paper/2211.05675/) (2022)
- [Domain Adaptation for Sustainable Soil Management using Causal and Contrastive Constraint Minimization](https://mlsanity.com/paper/2401.07175/) (2024)
- [Integration of neural network and fuzzy logic decision making compared with bilayered neural network in the simulation of daily dew point temperature](https://mlsanity.com/paper/2202.12256/) (2022)
- [Accurate Discharge Coefficient Prediction of Streamlined Weirs by Coupling Linear Regression and Deep Convolutional Gated Recurrent Unit](https://mlsanity.com/paper/2204.05476/) (2022)
- [Comparative analysis of machine learning and numerical modeling for combined heat transfer in Polymethylmethacrylate](https://mlsanity.com/paper/2204.08459/) (2022)
- [Joint Study of Above Ground Biomass and Soil Organic Carbon for Total Carbon Estimation using Satellite Imagery in Scotland](https://mlsanity.com/paper/2205.04870/) (2022)
- [A study of Universal ODE approaches to predicting soil organic carbon](https://mlsanity.com/paper/2509.24306/) (2025)
- [Spatial Prediction of Soil Microplastics and Organic Matter Using Graph Attention Networks](https://mlsanity.com/paper/2607.22875/) (2026)

## Abstract (arXiv)

Estimation of the soil organic carbon content is of utmost importance in understanding the chemical, physical, and biological functions of the soil. This study proposes machine learning algorithms of support vector machines, artificial neural networks, regression tree, random forest, extreme gradient boosting, and conventional deep neural network for advancing prediction models of SOC. Models are trained with 1879 composite surface soil samples, and 105 auxiliary data as predictors. The genetic algorithm is used as a feature selection approach to identify effective variables. The results indicate that precipitation is the most important predictor driving 15 percent of SOC spatial variability followed by the normalized difference vegetation index, day temperature index of moderate resolution imaging spectroradiometer, multiresolution valley bottom flatness and land use, respectively. Based on 10 fold cross validation, the DNN model reported as a superior algorithm with the lowest prediction error and uncertainty. In terms of accuracy, DNN yielded a mean absolute error of 59 percent, a root mean squared error of 75 percent, a coefficient of determination of 0.65, and Lins concordance correlation coefficient of 0.83. The SOC content was the highest in udic soil moisture regime class with mean values of 4 percent, followed by the aquic and xeric classes, respectively. Soils in dense forestlands had the highest SOC contents, whereas soils of younger geological age and alluvial fans had lower SOC. The proposed DNN is a promising algorithm for handling large numbers of auxiliary data at a province scale, and due to its flexible structure and the ability to extract more information from the auxiliary data surrounding the sampled observations, it had high accuracy for the prediction of the SOC baseline map and minimal uncertainty.