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.

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.

The Establishment of Principal Component Analysis Assessment Model for Drinking Water Quality of City Resource

2014 ◽  
Vol 675-677 ◽  
pp. 960-963
Author(s):  
Li Feng Sun ◽  
Qing Jie Qi ◽  
Xiao Liang Zhao ◽  
Rui Feng Li
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Drinking Water ◽  
Water Resources ◽  
Principal Component ◽  
Water Source ◽  
Component Analysis ◽  
Assessment Model ◽  
Affecting Factors

In order to effectively control pollution of sources of drinking water, improve the environmental quality of drinking water and guarantee the sanitation of drinking water, it is very important to assess water source quality. Main factors of drinking water were identified. Then principal component analysis was used to establish assessment model of drinking water, which could ensure that under the condition that the primitive data information was in the smallest loss, a small number of variables were used to replace the integrated multi-dimensional variables to simplify the data structure. The weightings of principal component were determinated as theirs pollution ratios. This paper was based on the theoretical study of principal component analysis, used the monitoring data on water quality of the main water resources in 2013 to evaluate and analyze the water quality of water resources. Analysis content included the main affecting factors, cause of pollution and the degree of pollution.The resulted showed that: the main affecting factors on water quality of Fo Si water source was CODMn, TP, fluoride.

scholarly journals Implementation of Long-Short Term Memory Neural Network (LSTM) for Predicting The Water Quality Parameters in Sungai Selangor

2021 ◽  
Vol 6 (4) ◽  
pp. 40-49
Author(s):  
Nur Natasya Mohd Anuar ◽  
Nur Fatihah Fauzi ◽  
Huda Zuhrah Ab Halim ◽  
Nur Izzati Khairudin ◽  
Nurizatul Syarfinas Ahmad Bakhtiar ◽  
...  
Keyword(s):  
Water Quality ◽  
Short Term Memory ◽  
Oxygen Demand ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Short Term ◽  
Term Memory ◽  
Quality Of Water ◽  
Long Short Term Memory

Predictions of future events must be factored into decision-making. Predictions of water quality are critical to assist authorities in making operational, management, and strategic decisions to keep the quality of water supply monitored under specific criteria. Taking advantage of the good performance of long short-term memory (LSTM) deep neural networks in time-series prediction, the purpose of this paper is to develop and train a Long-Short Term Memory (LSTM) Neural Network to predict water quality parameters in the Selangor River. The primary goal of this study is to predict five (5) water quality parameters in the Selangor River, namely Biochemical Oxygen Demand (BOD), Ammonia Nitrogen (NH3-N), Chemical Oxygen Demand (COD), pH, and Dissolved Oxygen (DO), using secondary data from different monitoring stations along the river basin. The accuracy of this method was then measured using RMSE as the forecast measure. The results show that by using the Power of Hydrogen (pH), the dataset yielded the lowest RMSE value, with a minimum of 0.2106 at station 004 and a maximum of 1.2587 at station 001. The results of the study indicate that the predicted values of the model and the actual values were in good agreement and revealed the future developing trend of water quality parameters, showing the feasibility and effectiveness of using LSTM deep neural networks to predict the quality of water parameters.

scholarly journals EVALUATING SURFACE WATER QUALITY AND WATER MONITORING PARAMETERS IN THE TIEN RIVER, VIETNAMESE MEKONG DELTA

2021 ◽  
Vol 83 (3) ◽  
pp. 29-36
Author(s):  
Thanh Giao Nguyen ◽  
Vo Quang Minh
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Monitoring Program ◽  
Oxygen Demand ◽  
Surface Water Quality ◽  
Principal Component ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Water Monitoring ◽  
Water Quality Parameters

The study aimed to evaluate the surface water quality of the Tien River and identify water quality parameters to be monitored using the water quality monitoring data in the period of 2011 - 2019. The water samples were collected at five locations from Tan Chau to Cho Moi districts, An Giang province for three times per year (i.e., in March, June, and September). Water quality parameters included temperature (oC), pH, dissolved oxygen (DO), total suspended solids (TSS), nitrate (NO3--N), orthophosphate (PO43--P), biological oxygen demand (BOD), and coliforms. These parameter results were compared with the national technical regulation on surface water quality QCVN 08-MT: 2015/BTNMT, column A1. Principal component analysis (PCA) was used to identify the sources of pollution and the main factors affecting water quality. The results of this study showed that DO concentration was lower and TSS, BOD, PO43--P, coliforms concentrations in the Tien river exceeded QCVN 08-MT: 2015/BTNMT, column A1. pH, temperature, and NO3--N values were in accordance with the permitted regulation. The water monitoring parameters were seasonally fluctuated. DO, BOD, TSS, and coliforms concentrations were higher in the rainy season whereas NO3--N and PO43--P were higher in the dry season. The PCA results illustrated that pH, TSS, DO, BOD, PO43--P and coliforms should be included in the monitoring program. Other indicators such as temperature and NO3--N could be considered excluded from the program to save costs. 

scholarly journals Water quality assessment of an urban river receiving tail water using the single-factor index and principal component analysis

2018 ◽  
Vol 19 (2) ◽  
pp. 603-609 ◽  
Author(s):  
Weiwei Lu ◽  
Juan Wu ◽  
Zhu Li ◽  
Naxin Cui ◽  
Shuiping Cheng
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Urban River ◽  
Single Factor ◽  
Sampling Points ◽  
Factor Index ◽  
Tail Water

Abstract Tail water from wastewater treatment plants (WWTP) serves as a major supplementary water source for scenic water bodies, whose water quality is one of the major focuses of public and scientific inquiries. This study investigated the temporal and spatial variations in water quality of Tangxihe River, a eutrophic urban river receiving tail water from a nearby WWTP in Hefei City, using the single-factor index (SFI) and principal component analysis (PCA). The results of SFI indicated that the most important parameters responsible for low water quality were total nitrogen (TN) and ammonia (NH4+-N). PCA showed that tail water from the WWTP greatly reduced water quality, as demonstrated by the significantly increased SFIs and integrated principal component values (F values) of the sampling points around the drain outlet of the WWTP (T3, T4 and T5). The sampling points located at the upstream of the river (T1) and up the water-gate of Chaohu Lake (T6) had negative F values, indicating relatively higher water quality. In addition, the season had a significant effect on the water quality of the river. Moreover, we discuss measures to improve the water quality of urban rivers in order to maintain their ecological functions.

scholarly journals Assessment of Water Quality for Aquaculture in Hau River, Mekong Delta, Vietnam Using Multivariate Statistical Analysis

Water ◽  
2021 ◽  
Vol 13 (22) ◽  
pp. 3307
Author(s):  
Fridah Gacheri Mutea ◽  
Howard Kasigwa Nelson ◽  
Hoa Van Au ◽  
Truong Giang Huynh ◽  
Ut Ngoc Vu
Keyword(s):  
Water Quality ◽  
Principal Component ◽  
Quality Parameters ◽  
Point Sources ◽  
Water Quality Parameters ◽  
Soluble Solids ◽  
Accumulation Factor ◽  
Multivariate Statistical ◽  
Aquaculture Production

The deterioration signs of water quality in the Hau River are apparent. The present study analyzed the surface water quality of the Hau River using multivariate statistical techniques, including principal component analysis (PCA) and Cluster Analysis (CA). Eleven water quality parameters were analyzed at 19 different sites in An Giang and Can Tho Provinces for 12 months from January to December 2019. The findings show high levels of Biological Oxygen Demand (BOD), Total Soluble Solids (TSS), and total coliform, all year round. The PCA revealed that all the water quality parameters influenced the water quality of the Hau River, hence the relevance for water sample scrutiny. The dendrogram of similarity between sampling sites showed a maximum similarity of 95.6%. The Accumulation Factor (AF) trend showed that the concentrations/values of TSS, BOD, and phosphate (PO43−) in the downstream were 1.29, 1.53, and 1.52 times, respectively, greater than the upstream levels. Despite most of the parameters analyzed supporting aquaculture production, caution is needed in the regulation of pollution point sources to undertake sustainable aquaculture production.

Assessment of Anthropogenic Impacts on the Water Quality of Marreco River, Brazil, Based on Principal Component Analysis and Toxicological Assays

2016 ◽  
Vol 227 (9) ◽  
Author(s):  
Soraya Moreno Palácio ◽  
Fernando Rodolfo Espinoza-Quiñones ◽  
Aline Roberta de Pauli ◽  
Pitágoras Augusto Piana ◽  
Caroline Bressan Queiroz ◽  
...  
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Anthropogenic Impacts ◽  
Principal Component ◽  
Component Analysis

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>

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