Classifying hot water chemistry: Application of multivariate statistics
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Transcript of Classifying hot water chemistry: Application of multivariate statistics
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Classifying hot water chemistry: Application of multivariate statistics
Prihadi Sumintadireja[1], Dasapta Erwin Irawan*[1], Yuano Rezky[2], Prana Ugiana Gio[3], Anggita Agustin[1]
[1] Faculty of Earth Sciences and Technology, Institut Teknologi Bandung, Jalan Ganesa No. 10, Bandung 40132
[2] Ministry of Energy and Mineral Resources
[3] Faculty of Math and Natural Sciences, Universitas Sumatera Utara, Jl. dr. T. Mansur No. 9, Medan 20155
* email: dasaptaerwin[@]outlook[.]co[.]id
* twitter: @dasaptaerwin ORCID (0000-0002-1526-0863)
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versioning
The following slides use versioning system on “Keynote”. This version has some improvements based on the presentation and some discussions
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license
I claim no right other than attribution
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Background
Classification is very important
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Background
Classification leads to characterisation of geothermal system
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Background
Classification: Direct method
Indirect method
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Background
Classification: Direct method (geology, drilling, etc) → strongly qualitative
Indirect method
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Background
Classification: Direct method (geology, drilling, etc) → strongly qualitative Indirect method → supports direct method, more quantitative
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Method
Here we used: Hydrochemistry,
Multivariate analysis, R programming
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Method
Here we used: Hydrochemistry (relatively cheap),
Multivariate analysis, R programming
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Method
Here we used: Hydrochemistry (relatively cheap),
Multivariate analysis (robust and powerful), R programming
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Method
Here we used: Hydrochemistry (relatively cheap),
Multivariate analysis (robust and powerful), R programming (robust, powerful, and free)
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Method
Samples = 11 Locations = Gorontalo
Sources = EBTKE Dataset and code =
available at zenodo.org (free repo)
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Method
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We combine:
linear regression from multi-regression technique with
axis rotation from PCA and CA.
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Method
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Results and discussions
|atemp>=27.1
B< 3.27
elv>=78.5
elv>=209.5
4887n=11
2276n=9
1204n=6
195n=2
1708n=4
4420n=3
1.664e+04n=2
1.529e+04n=1
1.799e+04n=1
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Results and discussions
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Variables (elements) extracted from the dataset.
We detect some strong correlations among the following elements.
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Results and discussions
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Results and discussions
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Our preliminary remarks is that we have three system running in the area (see the dots).
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Results and discussions
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Results and discussions
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Closing remarks
Regression tree technique has failed to read the data structure.
Collinearity effect is major problem
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Closing remarks
PCA and cluster analysis have successfully classify the samples.
Need more samples to validate training model.
Hopefully we can make a useable model to classify other samples.
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Closing remarks
Proposed work around:
manual variable selection then re-run the regression
use other technique without regression principles, in this case: PCA and CA
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Acknowledgment
ITB for funding this research under 2016 ITB Research Grant Scheme.
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Thank you
Classifying hot water chemistry: Application of multivariate statistics
for more communications with the authors please:
- send your email to dasaptaerwin[@]outlook[.]co[.]id
- mention on twitter: @dasaptaerwin
- connect ORCID (0000-0002-1526-0863)
Dataset and R code are stored in RG: http://dx.doi.org/10.13140/RG.2.1.3510.1205
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Thank you
R for all
@dasaptaerwin
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