scholarly journals Evaluation of Water Quality and Benthic Macrointervebrates Fauna Relationship Using Principal Component Analysis (PCA): A Case Study of Cameron Highlands Malaysia

2016 ◽  
Vol 5 (1) ◽  
pp. 187 ◽  
Author(s):  
Kok Weng Tan ◽  
Weng Chee Beh
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Dissolved Oxygen ◽  
Oxygen Demand ◽  
Principal Component ◽  
Component Analysis ◽  
Ammonia Nitrogen ◽  
Average Density ◽  
Class Iii ◽  
Cameron Highlands

<p class="ber"><span lang="EN-GB">This study applies the Principal Component Analysis (PCA) to evaluate and interpret the relationship between water quality and benthic macro-invertebrates fauna data obtained from <span class="longtext">Pauh River, Cameron Highlands. Samples were collected once every two months (in February, April, June, August and October 2013) with six chosen sampling stations. Six water quality parameters namely </span></span><span lang="EN-GB">dissolved oxygen (DO), pH, biological oxygen demand (BOD<sub>5</sub>), chemical oxygen demand (COD), ammonia-nitrogen (NH<sub>3</sub>-N), total suspended solid (TSS) and heavy metals contents <span class="longtext"><span>were analyzed according to American Public Health Association (APHA), </span></span>Standard Methods for Examination of Water and Wastewater<span class="longtext"><span> (1998)</span>. <span>Macro-invertebrates were also sampled using Surber sampler and were identified until their family level. Water Quality Index (WQI) values for all stations were class II except for the station 6 which was recorded as class III. Both the diversity and biotic indices showed decreasing value from the upstream (Station 1) to downstream (Station 6). </span></span>A total 28 to 31 taxa have been found in Station 1, 2, 3 and 5 (upstream to middle stream). However, only 7 taxa found at station 6 (downstream). Total 31 taxa with an average density 368.28 ind/m<sup>2</sup> were found in Station 4 which was highest number of taxa among the monitoring stations. <span class="longtext"><span>The </span></span><span>principal component analysis (PCA) was applied on the dataset, which explained 72.15 % of the total variance </span>of the variables<span>. Three components were extracted in this study. First component was classified as benthic macroinvertebrates which tolerated to low water quality condition and high loading of organic matters. The benthic macro-invertebrates families loaded in second component were sensitive to water environment such as NH<sub>3</sub>-N, dissolved oxygen (DO), organic matter and stream flow. The benthic macroinvertebrate families loaded in third component were recognized as species which might not tolerate low concentration of dissolved oxygen.  </span></span></p>

scholarly journals PARAMETER FISIKA, KIMIA, DAN BIOLOGI PENCIRI HABITAT IKAN BELIDA (Chitala lopis)

2017 ◽  
Vol 15 (1) ◽  
pp. 13
Author(s):  
Arif Wibowo ◽  
Mas Tri Djoko Sunarno ◽  
Safran Makmur
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Dissolved Oxygen ◽  
Air Temperature ◽  
Oxygen Demand ◽  
Principal Component ◽  
Environmental Parameters ◽  
Component Analysis ◽  
Biological Oxygen Demand ◽  
Total Dissolved Solid

Penelitian mengenai parameter fisika, kimia, dan biologi penciri habitat ikan belida (Chitala lopis) dilakukan tahun 2005 - 2006 di perairan umum daratan di Sumatera, Kalimantan, dan Jawa. Tujuan nya adalah untuk mendapatkan informasi parameter lingkungan yang menjadi karakteristik habitat ikan belida dari berbagai badan air di Jawa, Sumatera, dan Kalimantan. Metode survei dan kegiatan laboratorium digunakan dalam penelitian ini. Parameter lingkungan yang diamati meliputi suhu udara, suhu air, Total Dissolved Solid (TDS), Daya Hantar Listrik (DHL), klorofil-a, kecepatan arus, Biological Oxygen Demand (BOD), oksigen terlarut, pH, alkalinitas, CO2 bebas, kedalaman air, dan kecerahan pada 116 lokasi pengambilan yang ditentukan secara sengaja di Sungai Tulang Bawang (Provinsi Lampung), Sungai Kampar, Sungai Siak (Provinsi Riau), Sungai Musi (Provinsi Sumatera Selatan), Sungai Citarum (Provinsi Jawa Barat), Sungai Kapuas (Provinsi Kalimantan Barat), dan Waduk Riam Kanan (Provinsi Kalimantan Selatan). Analisis data menggunakan pendekatan analisis multivariabel regresi berganda Metode Backward yang didasarkan pada Analisis Komponen Utama (Principal Component Analysis) dan pembeda (Discriminant Analysis), serta korespondensi analisis (correspondency analysis). Hasil penelitian menunjukkan habitat ikan belida dapat dibedakan menjadi tiga tipe, yaitu tipe yang menyerupai sungai utama, waduk, dan anak sungai. Pembeda utama sekaligus parameter lingkungan utama adalah parameter TDS yang paling besar, dan selanjutnya parameterparameter DHL, suhu udara, klorofil-a, kecepatan arus, BOD, Oksigen terlarut, pH, alkalinitas, dan CO2 bebas menyumbang yang paling sedikit. Kehadiran plankton genus Ulothrix dan Mytilina secara tidak langsung teridentifikasi sebagai penciri habitat spesifik ikan belida. Research on physical, chemical, and biological parameters indicating specific habitat of clown knife fish (Chitala lopis) was carried out at 2005 - 2006 in inlands waters of Sumatera, Borneo, and Java. This study purposed to obtain information of environmental parameters indicating habitat characteristic of the knife fish in various inland waters bodies in Sumatera, Borneo, and Java. Survey method and laboratory activities were employed in this research. Environmental parameters observed were air temperature, water temperature, Total Dissolved Solid (TDS), conductivity, water velocity, Biological Oxygen Demand (BOD), dissolved oxygen, pH, alkalinity, free C02, water depth, and water transparancy taken on 116 sampling stations distributing in Tulang Bawang River (Lampung Province), Kampar and Siak River (Riau Province), Musi River (South Sumatera Province), Kapuas River (West Kalimantan Province), Riam Kanan Reservoar (South Kalimantan Province), and Citarum River (West Java Province). Data analysis used multivariate approach of multiple regression of Backward Method such as Principal Component Analysis, Discriminant Analysis, and Corre spondency Analysis. The results showed that the clown knife fish habitats could be divided by three types of specific habitat, namely water bodies similar with main rivers, reservoir, and tributaries. Parameter of TDS indicated the primary differentization as well as habitat characteristics of the clown knife fish.Whilst the parameters of conductivity, air temperature, chlorophyill-a, water current, BOD, dissolved oxygen, pH, alcalinity, and free CO2 contributed less significance. The existence of plankton from genus Ulothrix and Mytilina was identified indirectly as the specific habitat of the clown knife fish.

Assessment of Aquatic Ecological Health: A Case Study of Lake Rewalsar, Himachal Pradesh

2020 ◽  
Vol 7 (01) ◽  
Author(s):  
RAMA KUMARI ◽  
PARMANAND KUMAR
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Water Quality Index ◽  
Quality Index ◽  
Management Strategies ◽  
Oxygen Demand ◽  
Principal Component ◽  
Component Analysis ◽  
Different Seasons

The present study was conducted for two years to analyze the water quality of the sacred lake Rewalsar. Water quality of different seasons was evaluated by water quality index. Various statistical techniques, such as correlation, principal component analysis were applied. Based on Water Quality Index, water quality of the lake was in the range of 33-80 in different seasons. Cluster analysis of similarity indicates the relationship intensity between the seasons as cluster ranged 80-100% during the study period. In the principal component analysis maximum variables (Conductivity, Alkalinity, Biochemical Oxygen Demand, Nitrates, Phosphates, and Chloride) shows maximum influence during the summer and monsoon. The outcome revealed that the major driving factors of water quality deterioration are the runoff of effluent from the domestic area and offering food materials to the fishes. So, it is necessary to implement effective management strategies for the conservation of the Rewalsarlake.

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 Dimension Reduction of Machine Learning-based Forecasting Models Employing Principal Component Analysis

2020 ◽  
Author(s):  
Yinghui Meng ◽  
Sultan Noman Qasem ◽  
Manouchehr Shokri ◽  
S Shamshirband
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Dissolved Oxygen ◽  
Dimension Reduction ◽  
Principal Component ◽  
Component Analysis ◽  
Computational Time ◽  
Aquatic Life ◽  
Ann Models ◽  
Input Variables

In this research, an attempt was made to reduce the dimension of wavelet-ANFIS/ANN (artificial neural network/adaptive neuro-fuzzy inference system) models toward reliable forecasts as well as to decrease computational cost. In this regard, the principal component analysis was performed on the input time series decomposed by a discrete wavelet transform to feed the ANN/ANFIS models. The models were applied for dissolved oxygen (DO) forecasting in rivers which is an important variable affecting aquatic life and water quality. The current values of DO, water surface temperature, salinity, and turbidity have been considered as the input variable to forecast DO in a three-time step further. The results of the study revealed that PCA can be employed as a powerful for dimension reduction of input variables and also to detect inter-correlation of input variables. Results of the PCA-Wavelet-ANN models are compared with those obtained from Wavelet-ANN models while the earlier one has the advantage of less computational time than the later models. Dealing with ANFIS models, PCA is more beneficial to avoid Wavelet-ANFIS models creating too many rules which deteriorate the efficiency of the ANFIS models. Moreover, manipulating the Wavelet-ANFIS models utilizing PCA leads to a significant decreasing in computational time. Finally, it was found that the PCA-Wavelet-ANN/ANFIS models can provide reliable forecasts of dissolved oxygen as an important water quality indicators in rivers.

scholarly journals Dimension Reduction of Machine Learning-Based Forecasting Models Employing Principal Component Analysis

Mathematics ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 1233
Author(s):  
Yinghui Meng ◽  
Sultan Noman Qasem ◽  
Manouchehr Shokri ◽  
Shahab S
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Dissolved Oxygen ◽  
Dimension Reduction ◽  
Principal Component ◽  
Component Analysis ◽  
Computational Time ◽  
Aquatic Life ◽  
Ann Models ◽  
Input Variables

In this research, an attempt was made to reduce the dimension of wavelet-ANFIS/ANN (artificial neural network/adaptive neuro-fuzzy inference system) models toward reliable forecasts as well as to decrease computational cost. In this regard, the principal component analysis was performed on the input time series decomposed by a discrete wavelet transform to feed the ANN/ANFIS models. The models were applied for dissolved oxygen (DO) forecasting in rivers which is an important variable affecting aquatic life and water quality. The current values of DO, water surface temperature, salinity, and turbidity have been considered as the input variable to forecast DO in a three-time step further. The results of the study revealed that PCA can be employed as a powerful tool for dimension reduction of input variables and also to detect inter-correlation of input variables. Results of the PCA-wavelet-ANN models are compared with those obtained from wavelet-ANN models while the earlier one has the advantage of less computational time than the later models. Dealing with ANFIS models, PCA is more beneficial to avoid wavelet-ANFIS models creating too many rules which deteriorate the efficiency of the ANFIS models. Moreover, manipulating the wavelet-ANFIS models utilizing PCA leads to a significant decreasing in computational time. Finally, it was found that the PCA-wavelet-ANN/ANFIS models can provide reliable forecasts of dissolved oxygen as an important water quality indicator in rivers.

PSXIII-26 Supplementation with a polyphenolic pine bark extract reduces ammonia nitrogen production in a RUSITEC system as determined by principal component analysis

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 435-436
Author(s):  
Nelson Vera ◽  
Constanza Gutierrez ◽  
Pamela Williams ◽  
Cecilia Fuentealba ◽  
Rodrigo Allende ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Repeated Measures ◽  
Pearson Correlation ◽  
Principal Component ◽  
Component Analysis ◽  
Ammonia Nitrogen ◽  
Pine Bark ◽  
Bark Extract ◽  
Ruminal Fermentation ◽  
Fermentation Parameters

Abstract The aim of the study was to correlate the effects of supplementation with a polyphenolic pine bark extract (PBE) in diets with different forage to concentrate (F:C) ratio on methane (CH4), ammonia nitrogen (NH3–N) production and ruminal fermentation parameters using the Rumen Simulation Technique (RUSITEC). The experimental diets were F:C 70:30 (HF) or F:C 30:70 (HC) with or without 2% PBE on a DM basis. The four diets were isoproteic (15% CP), with similar OM (HF 94% and HC 96%), but different NDF (HF 40% and HC 25%). The treatments, in duplicate, were assigned in an 8 fermenter RUSITEC apparatus. Incubations were run twice, with 5 days of sampling after 10 days adaptation. The experimental design was a 2x2 factorial arrangement in a randomized complete block with repeated measures. Pearson correlation and principal component analysis (PCA) were conducted to elucidate relationships among PBE total polyphenols (TP) and the variables evaluated. The TP was highly correlated with NH3–N (r = –0.98; P &lt; 0.001) and butyrate (r = –0.85; P &lt; 0.001), and had a high correlation with propionate (r = 0.75; P &lt; 0.001) and acetate (r = 0.68; P = 0.001). Correlation with total VFA was moderate (r = –0.59; P = 0.006), and CH4 yield and IVDMD there were not correlated (r ≤ –0.07; P ≥ 0.188). The PCA (KMO = 0.655; BTS &lt; 0.001) shows that 75.2% of the total variation is explained by the first two principal components (PC1 = 46.5% and PC2 = 28.7%). In the score plot, PC1 discriminated between diets with and without PBE, while the PC2 separated based on NDF. The loading plot showed that TP and propionate were clustered, and had inverse directions to NH3–N. In conclusion, the PBE supplementation reduces NH3–N production in a RUSITEC system without decreasing CH4 yield or negatively affecting ruminal fermentation parameters.

Principal Component Analysis of Hydrological Data

2010 ◽  
pp. 364-388
Author(s):  
Petr Praus
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Drinking Water ◽  
Ground Water ◽  
Principal Component ◽  
Component Analysis ◽  
Data Sets ◽  
Hydrological Data ◽  
First Case

In this chapter the principals and applications of principal component analysis (PCA) applied on hydrological data are presented. Four case studies showed the possibility of PCA to obtain information about wastewater treatment process, drinking water quality in a city network and to find similarities in the data sets of ground water quality results and water-related images. In the first case study, the composition of raw and cleaned wastewater was characterised and its temporal changes were displayed. In the second case study, drinking water samples were divided into clusters in consistency with their sampling localities. In the case study III, the similar samples of ground water were recognised by the calculation of cosine similarity, the Euclidean and Manhattan distances. In the case study IV, 32 water-related images were transformed into a large image matrix whose dimensionality was reduced by PCA. The images were clustered using the PCA scatter plots.

scholarly journals Quantifying the Information Content of a Water Quality Monitoring Network Using Principal Component Analysis: A Case Study of the Freiberger Mulde River Basin, Germany

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 420 ◽  
Author(s):  
Thuy Hoang Nguyen ◽  
Björn Helm ◽  
Hiroshan Hettiarachchi ◽  
Serena Caucci ◽  
Peter Krebs
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
River Basin ◽  
Principal Component ◽  
Water Quality Monitoring ◽  
Component Analysis ◽  
Quality Monitoring ◽  
Critical Parameters ◽  
Spatial And Temporal Variation ◽  
Effectiveness Monitoring

Although river water quality monitoring (WQM) networks play an important role in water management, their effectiveness is rarely evaluated. This study aims to evaluate and optimize water quality variables and monitoring sites to explain the spatial and temporal variation of water quality in rivers, using principal component analysis (PCA). A complex water quality dataset from the Freiberger Mulde (FM) river basin in Saxony, Germany was analyzed that included 23 water quality (WQ) parameters monitored at 151 monitoring sites from 2006 to 2016. The subsequent results showed that the water quality of the FM river basin is mainly impacted by weathering processes, historical mining and industrial activities, agriculture, and municipal discharges. The monitoring of 14 critical parameters including boron, calcium, chloride, potassium, sulphate, total inorganic carbon, fluoride, arsenic, zinc, nickel, temperature, oxygen, total organic carbon, and manganese could explain 75.1% of water quality variability. Both sampling locations and time periods were observed, with the resulting mineral contents varying between locations and the organic and oxygen content differing depending on the time period that was monitored. The monitoring sites that were deemed particularly critical were located in the vicinity of the city of Freiberg; the results for the individual months of July and September were determined to be the most significant. In terms of cost-effectiveness, monitoring more parameters at fewer sites would be a more economical approach than the opposite practice. This study illustrates a simple yet reliable approach to support water managers in identifying the optimum monitoring strategies based on the existing monitoring data, when there is a need to reduce the monitoring costs.

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