Catalogue Search | MBRL
Search Results Heading
Explore the vast range of titles available.
MBRLSearchResults
-
LanguageLanguage
-
SubjectSubject
-
Item TypeItem Type
-
DisciplineDiscipline
-
YearFrom:-To:
-
More FiltersMore FiltersIs Peer Reviewed
Done
Filters
Reset
2
result(s) for
"Iskandarsyah, T.Yan W.M."
Sort by:
MULTIVARIATE DATA ANALYSIS TO ASSESS GROUNDWATER HYDROCHEMICAL CHARACTERIZATION IN RAWADANAU BASIN, BANTEN INDONESIA
by
Iskandarsyah, T.Yan W.M.
,
Alam, Boy Yoseph C.S.S. Syah
,
Sendjaja, Yoga Andriana
in
Aquifers
,
Calcium
,
Calcium ions
2024
A multivariate statistical technique of principal component analysis (PCA) and hierarchical cluster analysis (HCA) has been applied to identify and classify the various water sources that comprise the Rawadanau Basin. The data collection includes 60 samples taken during the dry (29 samples) and the rainy season (31 samples) in tropical regions. Sources of sampled water include dug wells, rivers, cold springs, and hot springs. Water chemistry measurable variables include field data (T, pH, EC), major ions (Na+, K+, Ca2+, Mg2+, Cl-, HCO3 -, SO4 2-), SiO2 , Fetotal, Mn, and stable isotopes of water (δ2H, and δ18O). The correlation of the concentration of water chemistry shows changes in the rainy season to Fetotal and Mn. Interpretation based on HCA using the dendrogram based on the chemical elements of water produces two clusters. Cluster A reflects an unconfined aquifer and bicarbonate type. Meanwhile, cluster B is a chloride type from the confined aquifer and does not change in different seasons. The PCA results show that the three-component matrix accounts for 86.12% of the data structure describing the Rawadanau Basin water sources that volcanic rocks affect and strongly correlate with Na+, K+, Ca2+, and Mg2+. PC1 has a high positive value for hydrochemical composition, indicating that lithology influences the kind of water. PC2 has a positive value for the stable isotope (δ18O and δ2H), meaning it is the main water source in Rawadanau. PC3 has a positive value for elevation and a negative for longitude, indicating a recharge area influenced by geological factors and is correlated with geothermal influences and volcanic rocks. This multivariate analysis can identify components and clusters of hydrochemical variables that have not been determined in previous studies.
Journal Article
MULTIVARIATE DATA ANALYSIS TO ASSESS GROUNDWATER HYDROCHEMICAL CHARACTERIZATION IN RAWADANAU BASIN, BANTEN INDONESIA
by
Ismawan Ismawan
,
Hendarmawan Hendarmawan
,
Yoga Andriana Sendjaja
in
Rawadanau
,
volcanic rocks
,
water chemistry
2024
Multivarijantna analiza glavnih komponenata (PCA) i hijerarhijska klasterska analiza (HCA) primijenjene su za identifikaciju i klasifikaciju različitih izvora vode koji se pojavljuju u slijevu Rawadanau. Podatci uključuju 60 uzoraka uzetih tijekom sušne (29 uzoraka) i kišne sezone (31 uzorak) u tropskim regijama. Izvori zahvaćene vode obuhvaćaju iskopane zdence, rijeke te hladne i tople izvore. Korišteni kemijski parametri vode uključuju parametre izmjerene na terenu (T, pH, EC), koncentracije glavnih iona (Na+, K+, Ca2+, Mg2+, Cl-, HCO3-, SO42-), SiO2, Feukupno, Mn i stabilne izotope kisika I vodika u vodi (δ2H i δ18O). Korelacija glavnih iona pokazuje promjene u kišnoj sezoni u odnosu na Feukupno i Mn koncentracije. Interpretacija temeljena na HCA-u definirala je dva klastera. Klaster A uključuje otvoreni vodonosnik bikarbonatnoga tipa. S druge strane, klaster B predstavlja zatvoreni vodonosnik kloridnoga tipa čiji se kemijski sastav ne mijenja u različitim godišnjim dobima. Rezultati PCA-a pokazali su da trokomponentnu matricu čini 86,12 % strukture podataka koji opisuju izvore vode u slijevu Rawadanau na koje utječu vulkanske stijene i koji su u snažnoj korelaciji s ionima Na+, K+, Ca2+ i Mg2+. PC1 ima visoku pozitivnu vrijednost za hidrokemijski sastav vode, što upućuje na snažan litološki utjecaj na kemiju vode. PC2 ima pozitivnu vrijednost za stabilne izotope (δ18O i δ2H), što upućuje na glavni izvor vode u Rawadanauu. PC3 ima pozitivan predznak za nadmorsku visinu te negativan za zemljopisnu dužinu, što upućuje na područje prihranjivanja, na koje utječu geološki čimbenici, što je dodatno povezano s geotermalnim utjecajima i vulkanskim stijenama. Ova multivarijantna analiza omogućuje identifikaciju komponenti i klastera hidrokemijskih varijabli koje nisu utvrđene u prethodnim studijama.
Journal Article