scholarly journals Correlation between macroalgae diversity and water quality in Southwest Maluku waters

2020 ◽  
Vol 45 (1) ◽  
pp. 25-32
Author(s):  
Marsya Jaqualine Rugebregt ◽  
Hairati Arfah ◽  
Ferdinand Pattipeilohy
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Species Composition ◽  
Diversity Index ◽  
Principal Component ◽  
Quality Parameters ◽  
Nursery Ground ◽  
Water Sampler ◽  
Phosphate Nitrate ◽  
Seawater Quality

Macroalgae play an important role in the ecosystem of the coastal area, serving as a shelter ground, nursery ground, and feeding ground. Macroalgae communities are directly influenced by water quality. This study aim was to determine the correlation between the macroalgae diversity and water quality in southwest Maluku waters. This research was conducted in September 2019 at seven research stations. Macroalgae samples were collected by transect method, while seawater quality was measured using Van Dorn Water Sampler. The macroalgae diversity, species composition, and dominance were determined. Water quality parameters analyzed were temperature, salinity, pH, phosphate, nitrate, and ammonia. Correlations between macroalgae diversity and water quality were determined using principal component analysis. This study recorded 45 species of macroalgae consisting of 15 species of red algae (Rhodophyta), 6 species of brown algae (Phaeophyta), and 24 species of green algae (Chlorophyta). Diversity Index varied ranged from low to moderate categories (0.969 - 2.345). Water quality in general is still quite good for macroalgae life. Macroalgae diversity and water quality correlate and influence each other.

scholarly journals Struktur Komunitas Perifiton Pada Lamun Jenis Thalassia hemprichii dan Cymodocea rotundatta di Kawasan Pantai Sanur

2017 ◽  
Vol 3 (1) ◽  
pp. 123
Author(s):  
Putu Satya Pratama ◽  
Dwi Budi Wiyanto ◽  
Elok Faiqoh
Keyword(s):  
Diversity Index ◽  
Principal Component ◽  
Total Suspended Solid ◽  
Suspended Solid ◽  
Nursery Ground ◽  
Anthropogenic Influences ◽  
Pca Analysis ◽  
Thalassia Hemprichii ◽  
Water Parameters ◽  
Phosphate Nitrate

Seagrass has function as nursery ground, spawning ground, feeding ground and habitat for many coastal organism (benthic, fish and epiphytes). Tourism activities in Sanur beach, the habitat of seagrass, could change the water condition, it indirectly influencing the existences of seagrass plants and periphyton in Sanur beach. The aim of this study are to analyze community structure of periphyton on seagrass leaves (Thalassia hemprichii and Cymodocea rotundatta) and its relationship with water parameters in four stations at Sanur beach area that has the unique characteristics. Water parameters measured were temperature, salinity, DO (Dissolved oxyen), pH, nitrate, phosphate, and TSS (Total Suspended Solid). Data analysis using PCA (Principal Component Analysis) to see the parameters that most influence on the abundance of periphyton. The results showed diversity index (H’) of periphyton is moderate, eveness index (E) moderate to high, and dominance index (C) is low to medium. It concluded that conditions of Sanur waters is stable but it is easily changed due to anthropogenic influences. PCA analysis showed that the parameters of the water have different effects on the abundance of periphyton at each seagrass leaves. Periphyton on Thalassia hemprichii was influenced by TSS, while Cymodocea rotundatta was influenced by phosphate, nitrate, temperature, DO, and TSS.

scholarly journals Analisis Kandungan N-Nitrogen (Amonia, Nitrit, Nitrat) dan Fosfat di Perairan Teluk Pandan Provinsi Lampung

2019 ◽  
Vol 8 (1) ◽  
pp. 57-66
Author(s):  
Reza Iklima AS ◽  
Gusti Diansyah ◽  
Andi Agussalim ◽  
Citra Mulia
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Data Analysis ◽  
Sampling Method ◽  
Principal Component ◽  
Pearl Oyster ◽  
Water Sampling ◽  
Cage Culture ◽  
Water Sampler ◽  
Pca Method

Iklima AS et al, 2019. Analysis of N-Nitrogen (Ammonia, Nitrate, and Nitric) and Phosphate at Teluk Pandan’s water territorial, Lampung Province. JLSO 8(1):57-66.Teluk Pandan’s water territorial was known to aquaculture activity such as prawn, pearl oyster and cage culture by community that lived in the area. It activities could makes water quality to be polluted.This research was purposed to known the content of nutrient (Ammonia, Nitrate, Nitric, and Phosphate) and to studied nutrient that related to other’s water quality parametric at Teluk Pandan water territorial. Sampling was determinate by 15station using purposive sampling method. Data analysis was used to studied relation between water quality’s parametric using Principal Component Analysis (PCA). Water sampling was taken at surface using water sampler. It was analyze in Oceanography and Instrumentation Laboratory, Department of Marine Science, Universitas Sriwijaya. Result of this research showing that rate of content nutrient at Teluk Pandan’s water territory ranging from 0.0007-0.0087 mg/L NO3-N, nitric ranging from 0.0001-0.0062 mg/L NO2-N, and phosphate ranging form 0,0012 – 0,0091 mg/L PO4-P. Based on result Teluk Pandan’s water territory still can be used for water’s ecosystem. Result using PCA method showing that correlation between parametric are directly proportional and inversely. Correlation that directly proportional showing by parametric group quadrant I (Temperature, Salinity, Velocity, and Abundance of Phytoplankton), quadrant II (DO, pH and nitrate) and quadrant III (Ammonia, nitric and phosphate). Inversely showing by parametric group quadrant I to parametric group quadrant III.

scholarly journals Distribution Of Batillaria Zonalis (Mollusca : Gastropoda) on Avicennia Marina (Forsk.) Vierh In The Coast Of Banggi, Rembang, Central Java

Omni-Akuatika ◽  
2018 ◽  
Vol 14 (3) ◽  
Author(s):  
Dafit Ariyanto Ariyanto ◽  
Dietriech Geoffrey Bengen ◽  
Tri Prartono ◽  
Yusli Wardiatno
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Temporal Pattern ◽  
Principal Component ◽  
Avicennia Marina ◽  
Nursery Ground ◽  
Chemical Parameters ◽  
Environmental Characteristics ◽  
Central Java ◽  
Physico Chemical

Mangroves prove a habitat for Batillaria zonalis as nursery ground, feeding ground and reproductive ground. This research was conducted from September 2016 - August 2017 and to determine the spasial and temporal pattern  with based on mangrove zone  and environmental characteristics. The gastropods and environmental characteristics were analyzed using Principal Component Analysis (PCA). The results showed that significant changes in gastropod assemblages were primarily due to changes in the water quality and season. Correlation between gastropod and physico-chemical parameters in A. marina  revealed significant relationship with gastropod B. zonalis distribution.

scholarly journals Evaluation of Water Quality of Kaveri River in Tiruchirappalli District, Tamil Nadu by Principal Component Analysis

2016 ◽  
Vol 11 (1) ◽  
pp. 89-95 ◽  
Author(s):  
Monikandon Sukumaran ◽  
Kesavan Devarayan
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Tamil Nadu ◽  
Oxygen Demand ◽  
Principal Component ◽  
Component Analysis ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Linear Relationships

Principal component analysis is a unique technique for reducing the dimensionality of the data. In this study, ten water quality parameters of the river Kaveri observed at five different stations of Tiruchirappalli for six years were collected and subjected to principal component analysis. A computational program was prepared in order to process and understand the data as a cluster. At first necessary data for compiling the program were listed and then fed to the program. Then the outputs were analyzed and possible linear and non-linear relationships between the water quality parameters and the timeline. It is understood that biological oxygen demand and fecal coli had a linear relationship. Further, the results suggested for group of factors that influence the water quality in a particular year.

scholarly journals Bioekologi Fitoplankton di Laguna Gampong Pulot (LGP) Kabupaten Aceh Besar

Omni-Akuatika ◽  
2016 ◽  
Vol 12 (2) ◽  
Author(s):  
Syahrul Purnawan ◽  
Irma Dewiyanti ◽  
Teuku M. Marman
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Diversity Index ◽  
Principal Component ◽  
Component Analysis ◽  
Water Parameters ◽  
Physical Chemical ◽  
Field Assessment ◽  
Chemical Water

The objective of the present study was to determine the diversity of phytoplankton and itsrelationship with physical-chemical water parameters of Gampong Pulot Lagoon, Leupungsubdistrict, Aceh Besar. The collecting of phytoplankton and water quality were conducted inDecember 2014. According to field assessment, there were six stations to represent the study site.We recorded 25 species of phytoplankton from class Bacillariophyceae, Dinophyceae andCyanophyceae. The abundance of Bacillariophyceae was 1202.02 ind / L classified as moderate,while Dinophyceae and Cyanophyceae were 621.13 ind / L and 208.49 ind / L, respectively,classified as low abundance. Bacillariophyceae was dominated by Rhizosolenia sp. with 26% ofcomposition. The diversity index has varied from 1,88 to 2,63 indicated as moderate value.  Basedon Principal Component Analysis (PCA) showed that the physical-chemical water parameters relatedto the abundance of phytoplankton in Gampong Pulot Lagoon.Keywords: phytoplankton, lagoon, diversity, leupung

scholarly journals Analysis of Water Quality for Marinculture in Moro, Karimun, Riau Islands with Principal Component Analysis

2020 ◽  
Vol 9 (2) ◽  
pp. 143
Author(s):  
Wiyoto Wiyoto ◽  
Irzal Effendi
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Quality Data ◽  
Multiple Linear Regressions ◽  
Water Quality Data ◽  
Seawater Quality ◽  
Linear Regressions ◽  
Surface Water Samples

Finding a good location is of important aspects in mariculture. This can be done by evaluating the water quality data. The aims of the study were to evaluate the seawater quality at Moro, Karimun, Riau Islands and to analyze its compatibility for mariculture by using principal component analysis (PCA) and multiple linear regressions. Generally, seawater qualities in the study area were in the tolerance range for mariculture. Surface water samples were collected from five different sampling points around Moro Sea. PCA results demonstrated that there were eleven variation factors which explained 95.4% of the total variance. In addition, based on PCA and multiple linear regressions, four water quality predictors for environmental quality could be identified, that is nitrite (NO2), temperature, pH and dissolved oxygen. Multiple linear regressions showed that the contribution of each parameter to the water quality was significant (R2=1, P < 0.05).

scholarly journals Monitoring and assessment of Deepor Beel water quality using multivariate statistical tools

2018 ◽  
Vol 13 (4) ◽  
pp. 893-908
Author(s):  
Siddhant Dash ◽  
Smitom Swapna Borah ◽  
Ajay Kalamdhad
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Quality Parameters ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Statistical Tools ◽  
Multivariate Statistical ◽  
Sampling Locations

AbstractThe aim of this study was application of multivariate statistical techniques – e.g., hierarchical cluster analysis (HCA), principal component analysis (PCA) and discriminant analysis (DA) – to analyse significant sources affecting water quality in Deepor Beel. Laboratory analyses for 20 water quality parameters were carried out on samples collected from 23 monitoring stations. HCA was used on the raw data, categorising the 23 sampling locations into three clusters, i.e., sites of relatively high (HP), moderate (MP) and low pollution (LP), based on water quality similarities at the sampling locations. The HCA results were then used to carry out PCA, yielding different principal components (PCs) and providing information about the respective sites' pollution factors/sources. The PCA for HP sites resulted in the identification of six PCs accounting for more than 84% of the total cumulative variance. Similarly, the PCA for LP and MP sites resulted in two and five PCs, respectively, each accounting for 100% of total cumulative variance. Finally, the raw dataset was subjected to DA. Four parameters, i.e., BOD5, COD, TSS and SO42− were shown to account for large spatial variations in the wetland's water quality and exert the most influence.

Remote sensing inversion of water quality in coastal sea area based on machine learning: a case study of Shenzhen bay, China

2021 ◽  
Author(s):  
Xiaotong Zhu ◽  
Jinhui Jeanne Huang
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Water Quality ◽  
Predictive Accuracy ◽  
Water Environment ◽  
Quality Parameters ◽  
Machine Learning Algorithms ◽  
Dynamic Monitoring ◽  
Support Vector ◽  
Seawater Quality

&lt;p&gt;Remote sensing monitoring has the characteristics of wide monitoring range, celerity, low cost for long-term dynamic monitoring of water environment. With the flourish of artificial intelligence, machine learning has enabled remote sensing inversion of seawater quality to achieve higher prediction accuracy. However, due to the physicochemical property of the water quality parameters, the performance of algorithms differs a lot. In order to improve the predictive accuracy of seawater quality parameters, we proposed a technical framework to identify the optimal machine learning algorithms using Sentinel-2 satellite and in-situ seawater sample data. In the study, we select three algorithms, i.e. support vector regression (SVR), XGBoost and deep learning (DL), and four seawater quality parameters, i.e. dissolved oxygen (DO), total dissolved solids (TDS), turbidity(TUR) and chlorophyll-a (Chla). The results show that SVR is a more precise algorithm to inverse DO (R&lt;sup&gt;2&lt;/sup&gt; = 0.81). XGBoost has the best accuracy for Chla and Tur inversion (R&lt;sup&gt;2&lt;/sup&gt; = 0.75 and 0.78 respectively) while DL performs better in TDS (R&lt;sup&gt;2&lt;/sup&gt; =0.789). Overall, this research provides a theoretical support for high precision remote sensing inversion of offshore seawater quality parameters based on machine learning.&lt;/p&gt;

Classification and Prediction of Goldfish Population and Water Quality Using a Potentiometric E-Tongue

2017 ◽  
Vol 60 (4) ◽  
pp. 1037-1044
Author(s):  
Zhenbo Wei ◽  
Yu Zhao ◽  
Jun Wang
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Pattern Recognition ◽  
Comprehensive Evaluation ◽  
Back Propagation ◽  
Correlation Coefficients ◽  
Principal Component ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Pattern Recognition Methods

Abstract. In this study, a potentiometric E-tongue was employed for comprehensive evaluation of water quality and goldfish population with the help of pattern recognition methods. Four water quality parameters, i.e., pH and concentrations of dissolved oxygen (DO), nitrite (NO2-N), and ammonium (NH3-N), were tested by conventional analysis methods. The differences in water quality parameters between samples were revealed by two-way analysis of variance (ANOVA). The cultivation days and goldfish population were classified well by principal component analysis (PCA) and canonical discriminant analysis (CDA), and the distribution of each sample was clearer in CDA score plots than in PCA score plots. The cultivation days, goldfish population, and water parameters were predicted by a T-S fuzzy neural network (TSFNN) and back-propagation artificial neural network (BPANN). BPANN performed better than TSFNN in the prediction, and all fitting correlation coefficients were &gt;0.90. The results indicated that the potentiometric E-tongue coupled with pattern recognition methods could be applied as a rapid method for the determination and evaluation of water quality and goldfish population. Keywords: Classify, E-tongue, Goldfish water, Prediction.

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