Authors :
Gill Ammara; Xiaojun NIE; Chang-hua LIU
Volume/Issue :
Volume 9 - 2024, Issue 5 - May
Google Scholar :
https://tinyurl.com/y4xjnzd7
Scribd :
https://tinyurl.com/3ak6zdvy
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24MAY2382
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
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.
Keywords :
Near Infrared and Visible Spectroscopy; Principal Component Analysis; Three-Dimensional Slice Map; Optimal Band Combination Algorithm; Random Forest.
References :
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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.
Keywords :
Near Infrared and Visible Spectroscopy; Principal Component Analysis; Three-Dimensional Slice Map; Optimal Band Combination Algorithm; Random Forest.