Geo-spatial Estimation and Forecasting of LULC Vulnerability Assessment of Mining Activity: A Case Study of Jharia Coal Field, India

Kumar, Ajay; Gorai, Amit Kumar

doi:10.4172/2469-4134.1000253

Cited by 3 publications

(1 citation statement)

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“…But, grade monitoring is a significant effect on most variable factors for operation in ore planning. Elsewhere, remote sensing technology development is a growing change in the mining industry, automation of machinery, and fast computation [21,22]. The machine learning scenario is vital in ore resources intimation, a type of reinforcement learning with the internet of things (IoT) for ore resource service of data transformation from field to working station [23].…”

Section: Introductionmentioning

confidence: 99%

Computational Cut-Off Grade Modeling: A Review

Sandeep¹,

Ajay²

2021

Int J Earth Sci Geophys

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An entry into the mining profession is associated with many sectors that affect the environment, economy, and livelihood. Here, we have concentrated on the ore grade obtained from the extraction of the mining region. These ore-grade materials have limited on the mining region. Still, demand increases day by dayalso affects the ore-grade level of materials as the supply situation. So, we have reviewed computationalbased research on cut-off grade modeling techniques. We have to know the grade reserver boundary range defined levels of obtained grade qualities. Eventually, the conventional method has been done complex, and this worked very tediously. Many experts have done soft computing techniques over cut-off grade modeling using ordinary kriging, nearest neighbor, and inverse distance. Here, we have reviewed these techniques on three-parameter values like assay, collar, and grade value on the leading role for cutoff grade modeling. Also, the estimated reserved is represented as a graphical for open-pit models like borehole, section model, surface model, block model, etc., and computed results. Therefore, this review paper has expressed an interest in cut-off grade monitoring and emphasized advanced performance-based soft computing techniques for the future perspective.

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Section: Introductionmentioning

confidence: 99%