Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.
Geological analysis with open-source software:
case Cikapundung River
Event: Sarasehan Geologi Populer, Badan Geologi Indo...
Pre-talk quiz
Pls visit www.govote.at enter code 88 74 35, and answer
three yes/no questions
Part one: Introduction
Slide license
need attribution (BY)
for non-commercial purposes only (NC)
can be copied, modified (SA, share-alike)
A bit about me
Personal data
Name: Dasapta Erwin Irawan
Job: Lecturer/researcher at Groundwater Engineering Program,
ITB
E...
A bit about me
Visiting program
Nov-Dec 2009
Center for Environmental Remote Sensing (CeRES), Chiba
University (2009)
Supe...
A bit about me
Research
hydrogeology
hydrochemistry
multivariate [statistical] analysis
A bit about me
Media social
Website: - R and Linux - Writing - SlideShare
Twitter: @dasaptaerwin
Email: d_erwin_irawan[at]...
Skills in Geology
Essential skills for geologist
Software skills (not free version)
office: Microsoft Office (Word, Excel, ppt) -> annual
subscription (from USD 10 per month)
...
Software skills (free equivalent)
office: OpenOffice or LibreOffice
citation and referencing: Zotero, Mendeley, etc
statistical:...
Why open source?
free as breathing
mostly cross-platform (Linux, Mac, Win)
strong community, hence rapid development
suppo...
What is reproducibility in science?
Every step can be:
re-do
re-analysed and re-evaluate
re-developed
What is reproducibility in science?
Those principles are applied to:
data (items and locations)
software used in the analy...
End of part one
Part two: Cikapundung case
Slide license
need attribution (BY)
for non-commercial purposes only (NC)
can be copied, modified (SA, share-alike)
Background
Background
Cikapundung has important roles:
is one of the major water source for Bandung Basin: WTP
Dago Pakar = 40 L/sec
...
Background
Background
Vast growth of settlements + landuse change -> declining water
quality (both river and groundwater).
Background
What do we know so far?
There types of groundwater and river water interactions (Lubis
and Puradimaja 2006)
isolated strea...
What do we know so far?
What do we know so far?
Conceptual model to mimic the interactions (Darul et.al
2014ab)
It confirms the Lubis Model
What do we know so far?
What do we know so far?
Our question
Does water quality reflect the interactions?
Our tools
R
Let’s do some [simple] analyses
Showing pairs analysis (bivariate analysis)
Data format
variables or measurements in columns
cases or samples in rows
no merged columns or rows
read also Data is the ...
Why pairs analysis
equivalent to correlation matrix
the fastest way to see correlations between variables
pls bear in mind...
Our data
Our data
295 samples
From five years periode (1997, 1998, 2007, 2011, 2012)
Load
# load data
data <- as.data.frame(read.csv("BandungData.csv",
header = TRUE))
attach(data)
## The following object is...
Data structure
# data structure
str(data)
## 'data.frame': 295 obs. of 33 variables:
## $ no : int 16 22 263 17 12 18 13 1...
pairs plot 1
pairs(data)
no
0 1.0 10740000 700 1.0 5 0 −50 0 0.0 0 0 0 0 0 20
0
0
code
year
2000
1.0
type
x
790000
y
distx...
pairs plot 1
ugly, too small
no legend and axis
we need to tweak it: group the variables and change plot code
pairs plot 1
pairs(group1,labels=colnames(group1),
main="Physical parameter",
pch=21, bg=c("red", "blue")
[unclass(data$ty...
Grouping variables
# group data
# Data group 1: Physical parameters
group1 <- data[,c("x", "y", "elv", "aq", "ec", "ph",
"...
Pairs plot group 1 (physical parameters)
770000
770000
x
10735000
y
700
elv
1.0
aq
200
ec
58
ph
080
hard
0
tds
1535
temp
−...
Pairs plot group 2 (cations )
770000
770000
x
10735000
y
700
elv
0100
Ca
030
Mg
0.01.0
Fe
0.00
Mn
040
K
770000
0150
107350...
Pairs plot group 3 (anions )
770000
770000
x
10735000
y
025
CO3
0500
HCO3
0150
CO2
0150
Cl
0250
SO4
0.02.0
NO2
080
NO3
770...
Showing PCA analysis (multivariate analysis)
Why PCA (Principle Component Analysis)?
nature embeds multivariable process
has been widely used and developed since the 6...
[Simple] codes
# install library
install.packages("pcaMethods") # for PCA
install.packages("gridExtra") # for plot lay out...
[Simple] codes
# evaluate results
summary(pca1) # result summary
sDev(pca1) # extracting eigenvalues
plot(sDev(pca1)) # pl...
Results: summary PCA1
## svdImpute calculated PCA
## Importance of component(s):
## PC1 PC2 PC3
## R2 0.258 0.1672 0.1257
...
Results: summary PCA2
## svdImpute calculated PCA
## Importance of component(s):
## PC1 PC2 PC3
## R2 0.2891 0.1392 0.1064...
Results: summary PCA3
## svdImpute calculated PCA
## Importance of component(s):
## PC1 PC2 PC3
## R2 0.1991 0.1337 0.0992...
Results: Extract Eigenvalues PCA1
1.0 2.0 3.0
1.21.31.41.51.6
Principal Component
Variance
1.0 2.0 3.0
1.11.21.31.41.51.61...
Results: plot PCA Group 1
−2 0 2 4
−2024
PC 1
R^2 = 0.26
−2024
PC 2
R^2 = 0.17
−2 0 2 4
−3−2−1012
−2 0 2 4 −3 −2 −1 0 1 2
...
Results: plot PCA Group 2
−2 0 2 4 6
−20246
PC 1
R^2 = 0.29
−20123
PC 2
R^2 = 0.14
−2 0 2 4 6
−4−2012
−2 0 1 2 3 −4 −2 0 1...
Results: plot PCA Group 3
−2 0 2 4 6
−20246
PC 1
R^2 = 0.2
−20246
PC 2
R^2 = 0.13
−2 0 2 4 6
−3−2−1012
−2 0 2 4 6 −3 −2 −1...
Results: loadings and scores Group1
−0.4 0.0 0.4
−0.4−0.20.00.20.4
Variable loadings
Group1
PC1
PC2
distx
ec
elv
ph
hard
t...
Results: loadings and scores Group2
−0.2 0.2
−0.6−0.4−0.20.00.20.4
Variable loadings
Group2
PC1
PC2
distx
ec
elv
Ca
Mg
Fe
...
Results: loadings and scores Group3
−0.2 0.2
−0.4−0.20.00.20.4
Variable loadings
Group3
PC1
PC2
distx
ec
elv
CO3
HCO3
CO2
...
spatial analysis (bubble plot)
why bubble plot?
shows spatial variation as well as values distribution
simple and straigtforward visualisation
[simple] codes
# load library (assuming all libraries are installed)
library(gstat)
library(sp)
library(rgdal)
library(lat...
[simple] codes
# make bubble plot
bubbleCa <- bubble(data, zcol="Ca",
xlab="X coord", ylab="Y coord",
main="Bubble plot Ca...
Process
## rgdal: version: 0.9-1, (SVN revision 518)
## Geospatial Data Abstraction Library extensions to R succ
## Loaded...
Results: groundwater end member (Ca)
Bubble plot Ca
X coord
Ycoord
10740000
10750000
10760000
770000 780000 790000 800000
...
Results: groundwater end member (Mg)
Bubble plot Mg
X coord
Ycoord
10740000
10750000
10760000
770000 780000 790000 800000
...
Results: river end member (Na)
Bubble plot Na
X coord
Ycoord
10740000
10750000
10760000
770000 780000 790000 800000
2.65
6...
Results: river end member (K)
Bubble plot K
X coord
Ycoord
10740000
10750000
10760000
770000 780000 790000 800000
0.2
2.1
...
Results: contamination signature (NO3)
Bubble plot NO3
X coord
Ycoord
10740000
10750000
10760000
770000 780000 790000 8000...
Results: contamination signature (NO2)
Bubble plot NO2
X coord
Ycoord
10740000
10750000
10760000
770000 780000 790000 8000...
Results: contamination signature (SO4)
Bubble plot SO4
X coord
Ycoord
10740000
10750000
10760000
770000 780000 790000 8000...
Results: contamination signature (Cl)
Bubble plot Cl
X coord
Ycoord
10740000
10750000
10760000
770000 780000 790000 800000...
Remarks
higher mineral concentration in river water than groundwater
should have occured in effluent flow.
higher mineral con...
Remarks
the anomaly is due to dilution effect.
dilution overides enrichment effect.
the opposite would happen if sampling is...
Closing
Future research opportunities:
to add more data in different locations along river bank, taken
in both rain and dry...
Main references
Lubis, RF and Puradimaja, DJ, 2006, Hydrodynamic
relationsships between groundwater and river water:
CIkap...
These slides were made using open-source tools
Ubuntu Linux (14.04)
R
Dia flowcharter
Gimp image editor
More resources
Me on Wordpress
Me on Blogger
Upcoming SlideShare
Loading in …5
×

[Hydro]geological analysis using open source app: case Cikapundung River

881 views

Published on

My talk on Sarasehan Geologi Populer, 16th March 2015, at Badan Geologi. This talk covers various open source tools for geological and hydrogeological analysis with focus on Cikapundung river case. Some examples of R code to extract hidden pattern in the data set, in order to explain natural phenomenon.

Published in: Education
  • Get the best essay, research papers or dissertations. from ⇒ www.HelpWriting.net ⇐ A team of professional authors with huge experience will give u a result that will overcome your expectations.
       Reply 
    Are you sure you want to  Yes  No
    Your message goes here

[Hydro]geological analysis using open source app: case Cikapundung River

  1. 1. Geological analysis with open-source software: case Cikapundung River Event: Sarasehan Geologi Populer, Badan Geologi Indonesia Dasapta Erwin Irawan 16th March 2015
  2. 2. Pre-talk quiz Pls visit www.govote.at enter code 88 74 35, and answer three yes/no questions
  3. 3. Part one: Introduction
  4. 4. Slide license need attribution (BY) for non-commercial purposes only (NC) can be copied, modified (SA, share-alike)
  5. 5. A bit about me Personal data Name: Dasapta Erwin Irawan Job: Lecturer/researcher at Groundwater Engineering Program, ITB Education (Geology, ITB): 1994-1998 (undergrad), 1999-2001 (Master), 2005-2009 (PhD)
  6. 6. A bit about me Visiting program Nov-Dec 2009 Center for Environmental Remote Sensing (CeRES), Chiba University (2009) Supervisor: Prof. Josaphat T.S. Sumantyo Research: remote sensing for hydrological purposes Feb 2014-Feb 2015 Faculty of Agriculture and Environment, University of Sydney Supervisor: Dr. Willem Vervoort Research: hydrological modeling in R
  7. 7. A bit about me Research hydrogeology hydrochemistry multivariate [statistical] analysis
  8. 8. A bit about me Media social Website: - R and Linux - Writing - SlideShare Twitter: @dasaptaerwin Email: d_erwin_irawan[at]yahoo[dot]com
  9. 9. Skills in Geology
  10. 10. Essential skills for geologist
  11. 11. Software skills (not free version) office: Microsoft Office (Word, Excel, ppt) -> annual subscription (from USD 10 per month) citation and referencing: EndNote -> from USD 250 statistical: Minitab, SPSS, Statistica, Stata -> basic version from USD 700 (2012) spatial / GIS: ArcGIS, Mapinfo, etc -> annual subscription (basic version from USD 100 per year) Sources: Openwetware ESRI
  12. 12. Software skills (free equivalent) office: OpenOffice or LibreOffice citation and referencing: Zotero, Mendeley, etc statistical: R and R Studio, Orange Data Mining, PSPP, etc GIS: QGIS, GRASSGIS, R
  13. 13. Why open source? free as breathing mostly cross-platform (Linux, Mac, Win) strong community, hence rapid development supporting reproducibility
  14. 14. What is reproducibility in science? Every step can be: re-do re-analysed and re-evaluate re-developed
  15. 15. What is reproducibility in science? Those principles are applied to: data (items and locations) software used in the analyses: each software has distinct feature and algorithm what would happen if not everyone could purchase the software?
  16. 16. End of part one
  17. 17. Part two: Cikapundung case
  18. 18. Slide license need attribution (BY) for non-commercial purposes only (NC) can be copied, modified (SA, share-alike)
  19. 19. Background
  20. 20. Background Cikapundung has important roles: is one of the major water source for Bandung Basin: WTP Dago Pakar = 40 L/sec electrical generator (since 1923): PLTA Bengkok = 3 MW PLTA Dago = 0.7 MW Drainase kota
  21. 21. Background
  22. 22. Background Vast growth of settlements + landuse change -> declining water quality (both river and groundwater).
  23. 23. Background
  24. 24. What do we know so far? There types of groundwater and river water interactions (Lubis and Puradimaja 2006) isolated stream at Maribaya area (upstream) effluent stream (or gaining stream) at Maribaya to Viaduct segment (Bandung central) influent stream (or losing stream) from Viaduct to Dayeuhkolot some facts of springs and seepages at isolated segment (Tanuwijaya 2014).
  25. 25. What do we know so far?
  26. 26. What do we know so far? Conceptual model to mimic the interactions (Darul et.al 2014ab) It confirms the Lubis Model
  27. 27. What do we know so far?
  28. 28. What do we know so far?
  29. 29. Our question Does water quality reflect the interactions?
  30. 30. Our tools R Let’s do some [simple] analyses
  31. 31. Showing pairs analysis (bivariate analysis)
  32. 32. Data format variables or measurements in columns cases or samples in rows no merged columns or rows read also Data is the new soil
  33. 33. Why pairs analysis equivalent to correlation matrix the fastest way to see correlations between variables pls bear in mind correlation does not always mean causality
  34. 34. Our data
  35. 35. Our data 295 samples From five years periode (1997, 1998, 2007, 2011, 2012)
  36. 36. Load # load data data <- as.data.frame(read.csv("BandungData.csv", header = TRUE)) attach(data) ## The following object is masked from package:datasets: ## ## CO2
  37. 37. Data structure # data structure str(data) ## 'data.frame': 295 obs. of 33 variables: ## $ no : int 16 22 263 17 12 18 13 19 14 20 ... ## $ code : int 116 122 8 117 112 118 113 119 114 120 . ## $ year : int 1997 1997 1997 1997 1997 1997 1997 1997 ## $ type : Factor w/ 2 levels "groundwater",..: 1 1 2 1 ## $ x : num 785175 785168 799275 785175 785181 ... ## $ y : num 10752836 10752843 10753680 10752840 107 ## $ distx : num 6897 6904 0 6897 6891 ... ## $ elv : int 1338 1336 1336 1320 1300 1247 1240 1230 ## $ aq : Factor w/ 3 levels "breccias","clay",..: 3 3 ## $ zone : Factor w/ 2 levels "eff","inf": 1 1 1 1 1 1 ## $ ec : num 71.9 71.9 77 71.9 71.9 71.9 71.9 71.9 7 ## $ ph : num 6.89 6.89 6.39 6.89 6.89 ... ## $ hard : num 11 11 26.4 11 11 11 11 11 11 11 ... ## $ tds : num 58.7 58.7 50 58.7 58.7 ...
  38. 38. pairs plot 1 pairs(data) no 0 1.0 10740000 700 1.0 5 0 −50 0 0.0 0 0 0 0 0 20 0 0 code year 2000 1.0 type x 790000 y distx 0 700 elv aq 1.0 1.0 zone ec 200 5 ph hard 0 0 tds temp 15 −50 eh Q 0 0 Ca Mg 0 0.0 Fe Mn 0.00 0 K Na 0 0 CO3 HCO3 0 0 CO2 Cl 0 0 SO4 NO2 0.0 0 NO3 SiO2 10 20 cumrain 0 2000 770000 0 1.0 200 0 15 0 0 0.00 0 0 0 0.0 10 40 40 lag1
  39. 39. pairs plot 1 ugly, too small no legend and axis we need to tweak it: group the variables and change plot code
  40. 40. pairs plot 1 pairs(group1,labels=colnames(group1), main="Physical parameter", pch=21, bg=c("red", "blue") [unclass(data$type)], upper.panel=NULL) legend(x=0.6, y=0.8, levels(data$type), pt.bg=c("red", "blue"), pch=21, bty="n", ncol=2, horiz=F)
  41. 41. Grouping variables # group data # Data group 1: Physical parameters group1 <- data[,c("x", "y", "elv", "aq", "ec", "ph", "hard", "tds", "temp", "eh", "Q")] # Data group 2: Cation group2 <- data[,c("x", "y", "elv", "Ca", "Mg", "Fe", "Mn", "K", "Na")] ## Data group 3: Anions (unit = ppm) group3 = data[,c("x", "y", "CO3", "HCO3", "CO2", "Cl", "SO4", "NO2", "NO3", "SiO2")]
  42. 42. Pairs plot group 1 (physical parameters) 770000 770000 x 10735000 y 700 elv 1.0 aq 200 ec 58 ph 080 hard 0 tds 1535 temp −50 eh 770000 08 10735000 700 1.0 3.0 200 5 8 0 80 0 15 35 −50 0 8 08Q Physical parameter groundwater river
  43. 43. Pairs plot group 2 (cations ) 770000 770000 x 10735000 y 700 elv 0100 Ca 030 Mg 0.01.0 Fe 0.00 Mn 040 K 770000 0150 10735000 700 0 80 0 30 0.0 1.0 0.00 0.30 0 40 0 150 0150Na Cations groundwater river
  44. 44. Pairs plot group 3 (anions ) 770000 770000 x 10735000 y 025 CO3 0500 HCO3 0150 CO2 0150 Cl 0250 SO4 0.02.0 NO2 080 NO3 770000 1070 10735000 0 25 0 500 0 150 0 150 0 200 0.0 2.0 0 60 10 60 1070SiO2 Anions groundwater river
  45. 45. Showing PCA analysis (multivariate analysis)
  46. 46. Why PCA (Principle Component Analysis)? nature embeds multivariable process has been widely used and developed since the 60’s simple, straighforward, nearest neighbour (cluster) principles offers nice visualisation
  47. 47. [Simple] codes # install library install.packages("pcaMethods") # for PCA install.packages("gridExtra") # for plot lay out # load library library(pcaMethods) # for PCA library(gridExtra) # for plot lay out # run PCA pca1 <- pca(group1, method = "svdImpute", scale = "uv", center = T, nPcs = 3, evalPcs = 1:3)
  48. 48. [Simple] codes # evaluate results summary(pca1) # result summary sDev(pca1) # extracting eigenvalues plot(sDev(pca1)) # plotting eigenvalues loadings(pca1) # plot loadings scores(pca1) # plot scores ## Loading required package: Biobase ## Loading required package: BiocGenerics ## Loading required package: parallel ## ## Attaching package: 'BiocGenerics' ## ## The following objects are masked from 'package:parallel' ## ## clusterApply, clusterApplyLB, clusterCall, clusterEv ## clusterExport, clusterMap, parApply, parCapply, parL ## parLapplyLB, parRapply, parSapply, parSapplyLB
  49. 49. Results: summary PCA1 ## svdImpute calculated PCA ## Importance of component(s): ## PC1 PC2 PC3 ## R2 0.258 0.1672 0.1257 ## Cumulative R2 0.258 0.4251 0.5509
  50. 50. Results: summary PCA2 ## svdImpute calculated PCA ## Importance of component(s): ## PC1 PC2 PC3 ## R2 0.2891 0.1392 0.1064 ## Cumulative R2 0.2891 0.4283 0.5347
  51. 51. Results: summary PCA3 ## svdImpute calculated PCA ## Importance of component(s): ## PC1 PC2 PC3 ## R2 0.1991 0.1337 0.09922 ## Cumulative R2 0.1991 0.3327 0.43194
  52. 52. Results: Extract Eigenvalues PCA1 1.0 2.0 3.0 1.21.31.41.51.6 Principal Component Variance 1.0 2.0 3.0 1.11.21.31.41.51.61.71.8 Principal Component Variance 1.0 2.0 3.0 1.21.31.41.51.6 Principal Component Variance
  53. 53. Results: plot PCA Group 1 −2 0 2 4 −2024 PC 1 R^2 = 0.26 −2024 PC 2 R^2 = 0.17 −2 0 2 4 −3−2−1012 −2 0 2 4 −3 −2 −1 0 1 2 −3−2−1012 PC 3 R^2 = 0.13 o o groundwater river water
  54. 54. Results: plot PCA Group 2 −2 0 2 4 6 −20246 PC 1 R^2 = 0.29 −20123 PC 2 R^2 = 0.14 −2 0 2 4 6 −4−2012 −2 0 1 2 3 −4 −2 0 1 2 −4−2012 PC 3 R^2 = 0.11 o o groundwater river water
  55. 55. Results: plot PCA Group 3 −2 0 2 4 6 −20246 PC 1 R^2 = 0.2 −20246 PC 2 R^2 = 0.13 −2 0 2 4 6 −3−2−1012 −2 0 2 4 6 −3 −2 −1 0 1 2 −3−2−1012 PC 3 R^2 = 0.1 o o groundwater river water
  56. 56. Results: loadings and scores Group1 −0.4 0.0 0.4 −0.4−0.20.00.20.4 Variable loadings Group1 PC1 PC2 distx ec elv ph hard tds temp eh cumrain lag1 −2 0 2 4 −2024 Case scores Group1 PC1 PC2 Water type: Groundwater River Water
  57. 57. Results: loadings and scores Group2 −0.2 0.2 −0.6−0.4−0.20.00.20.4 Variable loadings Group2 PC1 PC2 distx ec elv Ca Mg Fe Mn K Na cumrain lag1 −2 2 4 6 −2−10123 Case scores Group2 PC1 PC2 Water type: Groundwater River Water
  58. 58. Results: loadings and scores Group3 −0.2 0.2 −0.4−0.20.00.20.4 Variable loadings Group3 PC1 PC2 distx ec elv CO3 HCO3 CO2 ClSO4 NO2 NO3 SiO2 cumrain lag1 −2 0 2 4 6 −20246 Case scores Group3 PC1 PC2 Water type: Groundwater River Water
  59. 59. spatial analysis (bubble plot)
  60. 60. why bubble plot? shows spatial variation as well as values distribution simple and straigtforward visualisation
  61. 61. [simple] codes # load library (assuming all libraries are installed) library(gstat) library(sp) library(rgdal) library(latticeExtra) # open and load data df <- read.csv("BandungData.csv", header=TRUE) # convert xy values as coordinates coordinates(df) <- ~ x + y
  62. 62. [simple] codes # make bubble plot bubbleCa <- bubble(data, zcol="Ca", xlab="X coord", ylab="Y coord", main="Bubble plot Ca", col = data$zone, scales=list(tck=0.5)) print(bubbleCa)
  63. 63. Process ## rgdal: version: 0.9-1, (SVN revision 518) ## Geospatial Data Abstraction Library extensions to R succ ## Loaded GDAL runtime: GDAL 1.10.1, released 2013/08/26 ## Path to GDAL shared files: /usr/share/gdal/1.10 ## Loaded PROJ.4 runtime: Rel. 4.8.0, 6 March 2012, [PJ_VER ## Path to PROJ.4 shared files: (autodetected)
  64. 64. Results: groundwater end member (Ca) Bubble plot Ca X coord Ycoord 10740000 10750000 10760000 770000 780000 790000 800000 0.7 6.75 17.68 31.475 138.6
  65. 65. Results: groundwater end member (Mg) Bubble plot Mg X coord Ycoord 10740000 10750000 10760000 770000 780000 790000 800000 0.7 2.47 6.1 13.1 42.08
  66. 66. Results: river end member (Na) Bubble plot Na X coord Ycoord 10740000 10750000 10760000 770000 780000 790000 800000 2.65 6.7 14.18 27 166
  67. 67. Results: river end member (K) Bubble plot K X coord Ycoord 10740000 10750000 10760000 770000 780000 790000 800000 0.2 2.1 4.1 6.4 57.5
  68. 68. Results: contamination signature (NO3) Bubble plot NO3 X coord Ycoord 10740000 10750000 10760000 770000 780000 790000 800000 0 0.6 3.6 5.2 93.7
  69. 69. Results: contamination signature (NO2) Bubble plot NO2 X coord Ycoord 10740000 10750000 10760000 770000 780000 790000 800000 0 0 0 0.02 2.11
  70. 70. Results: contamination signature (SO4) Bubble plot SO4 X coord Ycoord 10740000 10750000 10760000 770000 780000 790000 800000 0 0.6 6.1 21.515 249
  71. 71. Results: contamination signature (Cl) Bubble plot Cl X coord Ycoord 10740000 10750000 10760000 770000 780000 790000 800000 1 5.61 12.12 41.97 164
  72. 72. Remarks higher mineral concentration in river water than groundwater should have occured in effluent flow. higher mineral concentration in groundwater than river water should have occured in influent flow. both natural indications are not detected, except for NO2.
  73. 73. Remarks the anomaly is due to dilution effect. dilution overides enrichment effect. the opposite would happen if sampling is conducted in dry season. possibility of different catchment between groundwater and river water.
  74. 74. Closing Future research opportunities: to add more data in different locations along river bank, taken in both rain and dry season. more exploratory statistical analysis, eg: multiple regression tree to extract data pattern.
  75. 75. Main references Lubis, RF and Puradimaja, DJ, 2006, Hydrodynamic relationsships between groundwater and river water: CIkapundung river stream, West Java, Indonesia Darul, A, Irawan, DE, and Trilaksono, NJ, 2014a, Groundwater and river water interaction on Cikapundung River: Revisited, International Conference on Math and Natural Sciences, ITB. Darul, A, 2014b, Model konseptual interaksi air tanah dan air sungai di bantaran S. Cikapundung, Bandung, Jawa Barat, Tesis S2, Supervisor: Dr. Dasapta Erwin Irawan dan Dr. Nurjanna Joko Trilaksono. Tanuwijaya, ZAJ, 2014, Identifikasi interaksi air sungai dan air tanah di DAS Cikapundung, Disertasi, Geologi Universitas Padjadjaran.
  76. 76. These slides were made using open-source tools Ubuntu Linux (14.04) R Dia flowcharter Gimp image editor
  77. 77. More resources Me on Wordpress Me on Blogger

×