Contribution of Random Forest and Deep Neural Network Algorithms with Environmental Covariates for the Spatial SOC Stock Modelling
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22 mai 2025
- ISIDORE Id: 10670/1.e85557...
- hal: hal-05088381
http://creativecommons.org/licenses/by-nc-nd/ , info:eu-repo/semantics/OpenAccess
Mots-clés
SOC stock Spatial modeling Random forest DNN algorithm Environmental covariates Land cover MNT Aix-Marseille Provence Métropole Urban planningSujets proches
Soil Mould, Vegetable Vegetable mold Earth (Soils) Mold, Vegetable Molding (Clay, plaster, etc.) Clay modeling ModellingCiter ce document
Mounir Oukhattar et al., « Contribution of Random Forest and Deep Neural Network Algorithms with Environmental Covariates for the Spatial SOC Stock Modelling », HAL SHS (Sciences de l’Homme et de la Société), ID : 10670/1.e85557...
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Résumé
This study aims to compare the effectiveness of random forest (RF) and deep neural network (DNN) algorithms in conjunction with variable selection methods for modelling soil organic carbon (SOC) stocks in the Aix-Marseille-Provence (AMP) Metropolis. Using a total of 51 soil samples and 29 different environmental factors, which include climate data, parent material, topographic details, land cover, human impact, remote sensing information, and soil characteristics, the examination has demonstrated that the Deep Neural Network (DNN) generally performs better than Random Forest (RF), particularly when all variables or those chosen through the Boruta technique or the variance inflation factor (VIF) are integrated. Nevertheless, RF excels over DNN when only non-redundant variables are considered. The resulting visual representations illustrate a higher SOC Stock in hilly forested regions and lower levels in maritime wetlands and farming areas. These conclusions offer a valuable tool for sustainable soil management and spatial organization, helping in achieving AMP Metropolis’s climate goals.
