Accurate Estimation for Stability of Slope and Partition Over Old Underground Coal Workings Using Regression-Based Algorithms

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dc.contributor.authorDorthi, K.
dc.contributor.authorKumar, A.
dc.contributor.authorRam Chandar, K.R.
dc.date.accessioned2026-02-06T06:35:37Z
dc.date.issued2022
dc.description.abstractNumerical modeling simulation has found to be best solution for predicting slope and partition stability over old underground coal workings. But it has taken huge time to complete a single simulation model. In this regard, machine learning-based framework is used to predict the stability of old galleries. A case study is taken up in opencast mine and simulation is carried out using numerical model and machine learning-based framework. Framework has shown an overall accuracy of 94–95% for different slope and partition stability. Framework shows a speedup of 2366 × against numerical simulator. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
dc.identifier.citationLecture Notes in Electrical Engineering, 2022, Vol.862, , p. 573-582
dc.identifier.issn18761100
dc.identifier.urihttps://doi.org/10.1007/978-981-19-0252-9_52
dc.identifier.urihttps://idr.nitk.ac.in/handle/123456789/29958
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.subjectMachine learning
dc.subjectNumerical modeling
dc.subjectOpencast mine
dc.subjectPartition
dc.subjectSlope
dc.subjectSupport vector regression
dc.titleAccurate Estimation for Stability of Slope and Partition Over Old Underground Coal Workings Using Regression-Based Algorithms

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