Predicting copper concentrations in acid mine drainage: a comparative analysis of five machine learning techniques-Reference-Cited by-同舟云学术

Predicting copper concentrations in acid mine drainage: a comparative analysis of five machine learning techniques

Published:2012-09-15 Issue:5 Volume:185 Page:4171-4182
ISSN:0167-6369
Container-title:Environmental Monitoring and Assessment
language:en
Short-container-title:Environ Monit Assess

Author:

Betrie Getnet D.,Tesfamariam Solomon,Morin Kevin A.,Sadiq Rehan

Publisher

Springer Science and Business Media LLC

Subject

Management, Monitoring, Policy and Law,Pollution,General Environmental Science,General Medicine

Link

http://link.springer.com/content/pdf/10.1007/s10661-012-2859-7.pdf

Reference34 articles.

1. Allison, J.D., Brown, D.S. & Novo-Gradac, K.J. (1991). MINTEQA2/PRODEFA2- user's manual (version 3.0). A geochemical assessment model for environmental systems. U.S. Environmental Protection Agency, Athens, GA. EPA/600/3-91/021.

2. Azapagic, A. (2004). Developing a framework for sustainable development indicators for the mining and minerals industry. Journal of Cleaner Production, 12, 639–662.

3. Bishop, C.M. (1995). Neural networks for pattern recognition. Oxford: Clarendon.

4. Bouckaert, R.R., Frank, E., Hall, M., Kirkby, R., Reutemann, P., Seewald, A., et al. (2010). WEKA manual (version 3.6.4). Hamilton: University of Waikato.

5. Cherkassky, V.S., & Mulier, F. (2007). Learning from data: concepts, theory, and methods. New Jersey: Wiley.

Cited by 43 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Empowered machine learning algorithm to identify sustainable groundwater potential zone map in Jashore District, Bangladesh;Groundwater for Sustainable Development;2024-05

2. Integrated machine learning and remote sensing for groundwater potential mapping in the Mekong Delta in Vietnam;Acta Geophysica;2024-04-03

3. Comparison of individual and ensemble machine learning models for prediction of sulphate levels in untreated and treated Acid Mine Drainage;Environmental Monitoring and Assessment;2024-03-02

4. Exploring Advanced Statistical Data Analysis Techniques for Interpolating Missing Observations and Detecting Anomalies in Mining Influenced Water Data;ACS ES&T Water;2023-08-10

5. Conjunct application of machine learning and game theory in groundwater quality mapping;Environmental Earth Sciences;2023-08-09