Exploring Advanced Statistical Data Analysis Techniques for Interpolating Missing Observations and Detecting Anomalies in Mining Influenced Water Data
Author:
Affiliation:
1. SARChI Chair for Mine Water Management, Department of Environmental, Water and Earth Sciences, Tshwane University of Technology, Private Bag X680, Pretoria 0001, South Africa
Funder
Tshwane University of Technology
National Research Foundation
Publisher
American Chemical Society (ACS)
Subject
Water Science and Technology,Environmental Chemistry,Chemistry (miscellaneous),Chemical Engineering (miscellaneous)
Link
https://pubs.acs.org/doi/pdf/10.1021/acsestwater.3c00163
Reference58 articles.
1. ACID MINE DRAINAGE AND OTHER MINING-INFLUENCED WATERS (MIW)
2. Big data management in the mining industry
3. Automated measurement systems in mine water management and mine workings – A review of potential methods
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