dc.contributor.author |
van Zyl, TL
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|
dc.date.accessioned |
2015-12-18T12:44:39Z |
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dc.date.available |
2015-12-18T12:44:39Z |
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dc.date.issued |
2014-07 |
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dc.identifier.citation |
Van Zyl, T,L. 2014. A comparison of machine learning techniques for predicting downstream acid mine drainage. In: 35th Canadian Symposium on Remote Sensing (IGARSS) 2014, Quebec, Canada, 13-18 July 2014 |
en_US |
dc.identifier.uri |
ftp://ftp.legos.obs-mip.fr/pub/tmp3m/IGARSS2014/abstracts/3311.pdf
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|
dc.identifier.uri |
http://hdl.handle.net/10204/8326
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dc.description |
35th Canadian Symposium on Remote Sensing (IGARSS) 2014, Quebec, Canada, 13-18 July 2014. . Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, please consult the publisher's website |
en_US |
dc.description.abstract |
We consider the challenge of providing downstream predictions of water quality using a time-series of upstream insitu measurements and a time-series of remote sensed precipitation data from the Tropical Rainfall Measuring Mission (TRMM). We use a windowing approach over historical values to generate a prediction for the current value. We evaluate a number of Machine Learning techniques as regressors including Support Vector Regression, Random Forests, Stochastic Gradient Decent Regression, Linear Regression, Ridge Regression and Gaussian Processes. We show that overall we are able to attain R(sup2) values above 0:80 ( 0:16) for most target variables and that Random Forests are the most effective Machine Learning technique in this predictive task. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
IGARRS |
en_US |
dc.relation.ispartofseries |
Workflow;14556 |
|
dc.subject |
Tropical Rainfall Measuring Mission |
en_US |
dc.subject |
TRMM |
en_US |
dc.subject |
Machine learning techniques |
en_US |
dc.subject |
Water quality |
en_US |
dc.subject |
Acid mine drainage |
en_US |
dc.title |
A comparison of machine learning techniques for predicting downstream acid mine drainage |
en_US |
dc.type |
Conference Presentation |
en_US |
dc.identifier.apacitation |
van Zyl, T. (2014). A comparison of machine learning techniques for predicting downstream acid mine drainage. IGARRS. http://hdl.handle.net/10204/8326 |
en_ZA |
dc.identifier.chicagocitation |
van Zyl, TL. "A comparison of machine learning techniques for predicting downstream acid mine drainage." (2014): http://hdl.handle.net/10204/8326 |
en_ZA |
dc.identifier.vancouvercitation |
van Zyl T, A comparison of machine learning techniques for predicting downstream acid mine drainage; IGARRS; 2014. http://hdl.handle.net/10204/8326 . |
en_ZA |
dc.identifier.ris |
TY - Conference Presentation
AU - van Zyl, TL
AB - We consider the challenge of providing downstream predictions of water quality using a time-series of upstream insitu measurements and a time-series of remote sensed precipitation data from the Tropical Rainfall Measuring Mission (TRMM). We use a windowing approach over historical values to generate a prediction for the current value. We evaluate a number of Machine Learning techniques as regressors including Support Vector Regression, Random Forests, Stochastic Gradient Decent Regression, Linear Regression, Ridge Regression and Gaussian Processes. We show that overall we are able to attain R(sup2) values above 0:80 ( 0:16) for most target variables and that Random Forests are the most effective Machine Learning technique in this predictive task.
DA - 2014-07
DB - ResearchSpace
DP - CSIR
KW - Tropical Rainfall Measuring Mission
KW - TRMM
KW - Machine learning techniques
KW - Water quality
KW - Acid mine drainage
LK - https://researchspace.csir.co.za
PY - 2014
T1 - A comparison of machine learning techniques for predicting downstream acid mine drainage
TI - A comparison of machine learning techniques for predicting downstream acid mine drainage
UR - http://hdl.handle.net/10204/8326
ER -
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en_ZA |