Comparison of four learning-based methods for predicting groundwater redox status-Reference-Cited by-同舟云学术

Comparison of four learning-based methods for predicting groundwater redox status

Published:2020-01 Issue: Volume:580 Page:124200
ISSN:0022-1694
Container-title:Journal of Hydrology
language:en
Short-container-title:Journal of Hydrology

Author:

Friedel M.J.,Wilson S.R.,Close M.E.,Buscema M.,Abraham P.,Banasiak L.

Publisher

Elsevier BV

Subject

Water Science and Technology

Reference59 articles.

1. Spatial and temporal patterns in the frequency of events exceeding three times the median flow (FRE3) across New Zealand;Booker;J. Hydrol. (NZ),2013

2. Booker, D.J, 2015. Hydrological Indices for National Environmental Reporting. NIWA report prepared for Ministry for the Environment, 39p.

3. Random forests;Breiman;Mach. Learn.,2001

4. Genetic doping algorithm (GenD): theory and applications;Buscema;Expert Syst.,2004

5. Training with input selection and testing (TWIST) algorithm: a significant advance in pattern recognition performance of machine learning;Buscema;J. Intell. Learn. Syst.,2013

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