classifying hot water chemistry: application of multivariate statistics
TRANSCRIPT
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)
1
versioning
The following slides use versioning system on “Keynote”. This version has some improvements based on the presentation and some discussions
2
license
I claim no right other than attribution
3
Background
Classification is very important
4
Background
Classification leads to characterisation of geothermal system
5
Background
Classification: Direct method
Indirect method
6
Background
Classification: Direct method (geology, drilling, etc) → strongly qualitative
Indirect method
7
Background
Classification: Direct method (geology, drilling, etc) → strongly qualitative Indirect method → supports direct method, more quantitative
8
Method
Here we used: Hydrochemistry,
Multivariate analysis, R programming
9
Method
Here we used: Hydrochemistry (relatively cheap),
Multivariate analysis, R programming
10
Method
Here we used: Hydrochemistry (relatively cheap),
Multivariate analysis (robust and powerful), R programming
11
Method
Here we used: Hydrochemistry (relatively cheap),
Multivariate analysis (robust and powerful), R programming (robust, powerful, and free)
12
Method
Samples = 11 Locations = Gorontalo
Sources = EBTKE Dataset and code =
available at zenodo.org (free repo)
13
Method
14
We combine:
linear regression from multi-regression technique with
axis rotation from PCA and CA.
Method
15
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
16
Results and discussions
17
Variables (elements) extracted from the dataset.
We detect some strong correlations among the following elements.
Results and discussions
18
Results and discussions
19
Our preliminary remarks is that we have three system running in the area (see the dots).
Results and discussions
20
Results and discussions
21
Closing remarks
Regression tree technique has failed to read the data structure.
Collinearity effect is major problem
22
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.
23
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
24
Acknowledgment
ITB for funding this research under 2016 ITB Research Grant Scheme.
25
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
26
Thank you
R for all
@dasaptaerwin
27