Annals of GIS, Journal Year: 2024, Volume and Issue: 30(2), P. 215 - 232

Published: Jan. 29, 2024

This research focuses on understanding the spatial variation of Soil Organic Matter (SOM) and pH levels in North Morocco. The study employs a comprehensive approach to enhance predictive modelling, incorporating Boruta algorithm for effective environmental covariates selection optimizing model parameters through hyperparameter optimization. Utilizing Random Forest (RF) with remote sensing indices topographic features, predicts SOM identify key contributors their variability. prediction saw significant success, notable correlation such as RVI, NDVI, TNDVI. These indices, indicative vegetation health productivity, emerged primary influencers SOM. In comparison, influence features like elevation, slope, aspect was found be less significant. Conversely, predicting challenging due minimal variability within dataset. Addressing this limitation could involve dataset expansion or alternative models low-correlated data handling. Despite RF model's limited efficacy prediction, an observable between identified, consistent prior research. Areas higher exhibited lower values, indicating relative soil acidification from organic matter decomposition. study's demonstrated potential using but enhancing is essential. Future may explore expansion, diverse sampling, testing better performance datasets. offers valuable insights advanced development enriches management practices.

Language: Английский