Using Machine Learning Algorithms for Spatial Prediction of Soil Organic Carbon Based on Environmental Variables and Soil Physicochemical Parameters in the Mediterranean Region
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The three-model screen
all 1,000 screened works →All three models called this out of scope.
Machine learning spatial prediction of soil organic carbon in a Moroccan catchment; the object is soil carbon mapping.
The work predicts soil organic carbon using machine learning, not research practice.
ML prediction of soil organic carbon; environmental domain application, not metaresearch.
Abstract
Soil plays a key role in storing organic carbon, which is a critical indicator of soil fertility and overall quality. Understanding the spatial distribution of soil organic carbon stock (SOCS) and its influencing factors is essential for promoting sustainable land management. This study applied four machine learning models such as Random Forest (RF), k-nearest neighbors (kNN), Support Vector Machine (SVM), and Cubist to enhance SOCS prediction in the Srou catchment, part of the Upper Oum Er-Rbia watershed in Morocco. A dataset of 120 samples was collected, with 80% used for model training and 20% for validation. Boruta’s feature selection and multicollinearity tests identified nine key factors influencing SOCS. Spatial maps generated from the models were validated using statistical indicators. The RF model showed the highest predictive accuracy (R² = 0.76, RMSE = 0.52 Mg C/ha), followed by SVM and Cubist, while kNN had the lowest performance (R² = 0.31, RMSE = 0.94 Mg C/ha). Key predictors for SOCS included bulk density, pH, electrical conductivity, and calcium carbonate. The proposed machine learning approach demonstrates significant potential for mapping SOCS in similar semi-arid environments.
Stored with the screening record, where it is evidence for the labels above.
The record
- Venue
- Topic
- Soil Geostatistics and Mapping
- Field
- Environmental Science
- Canadian institutions
- Dalhousie University
- Funders
- —
- Keywords
- Mediterranean climateSoil carbonEnvironmental scienceSoil scienceCarbon fibersAlgorithmComputer scienceSoil waterGeography
- Has abstract in OpenAlex
- yes