Advanced Modelling of Soil Organic Carbon Content in Coal Mining Areas Using Integrated Spectral Analysis: A Dengcao Coal Mine Case Study

Abstract

Effective modelling and integrated spectral analysis approaches can advance modelling precision. To develop an integrated spectral forecast modelling of soil organic carbon (SOC), this research investigated a mining coal in Dengcao Coal Mine Area, Zhengzhou. The study utilizes the Lasso and Ranger algorithms were utilized in spectral band analysis. Four primary models employed during this process include Artificial Neural Network (ANN), Support Vector Machine, Random Forest (RF), and Partial Least Squares Regression (PLSR). The ideal model was chosen. The results showed that, in contrast to when band collection was based on Lasso algorithm modelling, model precision was higher when it was based on the Ranger algorithm. ANN model had an ideal goodness acceptance, and the modelling developed by RF showed the steadiest modelling consequences. Based on the results, a distinct method is proposed in this study for band assortment at the earlier stage of integrated spectral modelling of SOC. The Ranger method can be used to check the spectral particles, and RF or ANN can be chosen to develop the prediction modelling based on different statistics sets, which is appropriate to create the prediction modelling of SOC content in Dengcao Coal Mine Area. This research avails a position for the integrated spectral of Analysis for Advanced Modelling of Soil Organic Carbon Content in Coal Sources alongside a theoretical foundation for innovating portable device for the integrated spectral assessment of SOC content in coal mining habitats. This study might be significant for the changing modelling and monitoring of SOC in mining and environmental areas.