A Multivariate Technique to Develop Hybrid Water Quality Index of the Bengawan Solo River, Indonesia

被引：7

作者：

Lusiana, Evellin Dewi ^{[1
,2
]}

Mahmudi, Mohammad ^{[1
,2
]}

Hutahaean, Sarah Mega ^{[1
]}

Darmawan, Arief ^{[1
]}

Buwono, Nanik Retno ^{[1
,2
]}

Arsad, Sulastri ^{[1
,2
]}

Musa, Muhammad ^{[1
,2
]}

机构：

[1] Univ Brawijaya, Fac Fisheries & Marine Sci, Dept Aquat Resources Management, Vet St, Malang 65145, Indonesia

[2] Univ Brawijaya, Fac Fisheries & Marine Sci, AquaRES Res Grp, Vet St, Malang 65145, Indonesia

来源：

JOURNAL OF ECOLOGICAL ENGINEERING | 2022年 / 23卷 / 02期

关键词：

principal component analysis; WQI; variable selection; water quality assessment; SUSPENDED-SOLIDS;

D O I：

10.12911/22998993/144420

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

Surface water resource, such as river, is constantly contaminated by domestic and industrial pollutants. In order to properly manage the water resource, a composite index for water quality assessment, such as water quality index (WQI), has been designed to monitor and evaluate the properties of surface water. However, this index is quite subjective in terms of determination of relative weights. A principal component analysis (PCA) can be used to reduce the dimension and subjectivity of water quality variables. The purpose of this study was to implement the use of hybrid PCA and WQI methods to assess and monitor the water quality of the Bengawan Solo River, which is located in Java Island, Indonesia. The result suggested that COD, BOD, TSS, TDS, nitrate, nitrite, and ammonia were the main factors that determine water quality of the Bengawan Solo River. Furthermore, it was revealed that most samples from the river showed water quality status as slightly polluted. In addition to this, the seasonal variation of the PCWI values indicated a significant increase of water pollution in the Bengawan Solo River per year.

引用

页码：123 / 131

页数：9

共 43 条

[1] Efficient Water Quality Prediction Using Supervised Machine Learning [J].