Boreholes Data Analysis Architecture Based on Clustering and Prediction Models for Enhancing Underground Safety Verification

During the last decade, substantial resources have been invested to exploit massive amounts of boreholes data collected through groundwater extraction. Furthermore, boreholes depth can be considered one of the crucial factors in digging borehole efficiency. Therefore, a new solution is needed to pro...

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Vydáno v:	IEEE access Ročník 9; s. 78428 - 78451
Hlavní autoři:	Iqbal, Naeem, Rizwan, Atif, Khan, Anam Nawaz, Ahmad, Rashid, Kim, Bong Wan, Kim, Kwangsoo, Kim, Do-Hyeun
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Analytical models Boreholes boreholes data Cluster analysis Clustering Data analysis data and predictive analytics Data mining Data models deep learning Efficiency Groundwater Hydrogeology Long short term memory Machine learning Modules Performance evaluation Prediction models Predictive analytics Predictive models Regression models Risk assessment ROP Safety management Verification Water resources
ISSN:	2169-3536, 2169-3536
On-line přístup:	Získat plný text
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