scholarly journals Multivariate statistical approaches to benthic macroinvertebrates and water quality for farming impact assessment in Selangor River, Malaysia

2021 ◽  
Author(s):  
Nadeesha Dilani Hettige ◽  
Rohasliney Binti Hashim ◽  
Zulfa Hanan Ash’aari ◽  
Ahmad Abas Kutty ◽  
Nor Rohaizah Jamil
Keyword(s):  
Water Quality ◽  
Benthic Macroinvertebrates ◽  
Oxygen Demand ◽  
Fish Farming ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Ammoniacal Nitrogen ◽  
Fish Farms ◽  
Organic Contamination ◽  
Multivariate Statistical

Abstract This study examined the influence of fish farming activities on water quality and benthic macroinvertebrates at the Rawang sub-basin of Selangor River. Multivariate statistical techniques were used to determine major influencing water quality parameters causing organic contamination and the dominant pollution-tolerant benthic macroinvertebrates. Sampling was conducted at Guntong River (SR1), Guntong River’s tributary (SR2, the control site), Kuang River (SR3 and SR6), Gong River (SR4), and Serendah River (SR5) using random sampling techniques based on accessibility and proximity to fish farms. Benthic macroinvertebrates and water samples were collected from April 2019 to March 2020. Based on the principal components analysis (PCA), electrical conductivity (EC), dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), ammoniacal-nitrogen, and total suspended solids (TSS) were major water quality parameters influenced by fish farming activities. The Canonical Correspondence Analysis (CCA) revealed that several taxa of benthic macroinvertebrates (Chironomidae, Naididae, Lumbriculidae, Tubificidae, unidentified Oligochaeta, Leeches (Helobdella sp.), Planorbidae, and some Odonata) were moderately or highly sensitive to TSS, BOD, COD, turbidity, ammoniacal-nitrogen, and EC. These taxa were dominant in the sampling sites, which were close to fish farms. Findings in this study showed that fish farming activities impacted the water quality and benthic macroinvertebrates in this sub-basin.

scholarly journals Multivariate Statistical Analysis of Water Quality and Trophic State in an Artificial Dam Reservoir

Water ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 186
Author(s):  
Md Mamun ◽  
Ji Yoon Kim ◽  
Kwang-Guk An
Keyword(s):  
Water Quality ◽  
Trophic State ◽  
Suspended Solids ◽  
Oxygen Demand ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Trophic State Index ◽  
Multivariate Statistical ◽  
Chl A ◽  
State Index

Paldang Reservoir, located in the Han River basin in South Korea, is used for drinking water, fishing, irrigation, recreation, and hydroelectric power. Therefore, the water quality of the reservoir is of great importance. The main objectives of this study were to evaluate spatial and seasonal variations of surface water quality in the reservoir using multivariate statistical techniques (MSTs) along with the Trophic State Index (TSI) and Trophic State Index deviation (TSID). The empirical relationships among nutrients (total phosphorus, TP; total nitrogen, TN), chlorophyll-a (CHL-a), and annual variations of water quality parameters were also determined. To this end, 12 water quality parameters were monitored monthly at five sites along the reservoir from 1996 to 2019. Most of the parameters (all except pH, dissolved oxygen (DO), and total coliform bacteria (TCB)) showed significant spatial variations, indicating an influence of anthropogenic activities. Principal component analysis combined with factor analysis (PCA/FA) suggested that the parameters responsible for water quality variations were primarily correlated with nutrients and organic matter (anthropogenic), suspended solids (both natural and anthropogenic), and ionic concentrations (both natural and anthropogenic). Stepwise spatial discriminant analysis (DA) identified water temperature (WT), DO, electrical conductivity (EC), chemical oxygen demand (COD), the ratio of biological oxygen demand (BOD) to COD (BOD/COD), TN, TN:TP, and TCB as the parameters responsible for variations among sites, and seasonal stepwise DA identified WT, BOD, and total suspended solids (TSS) as the parameters responsible for variations among seasons. COD has increased (R2 = 0.63, p < 0.01) in the reservoir since 1996, suggesting that nonbiodegradable organic loading to the water body is rising. The empirical regression models of CHL-a-TP (R2 = 0.45) and CHL-a-TN (R2 = 0.27) indicated that TP better explained algal growth than TN. The mean TSI values for TP, CHL-a, and Secchi depth (SD) indicated a eutrophic state of the reservoir for all seasons and sites. Analysis of TSID suggested that blue-green algae dominated the algal community in the reservoir. The present results show that a significant increase in algal chlorophyll occurs during spring in the reservoir. Our findings may facilitate the management of Paldang Reservoir.

scholarly journals Using multivariate statistical techniques to assess water quality of Nhu Y river in Thua Thien Hue province

2014 ◽  
Vol 17 (2) ◽  
pp. 50-60
Author(s):  
Ky Minh Nguyen ◽  
Lam Hoang Nguyen
Keyword(s):  
Water Quality ◽  
Correlation Analysis ◽  
Oxygen Demand ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Total Variance ◽  
Quality Data ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical

The aims of this research are to assess water quality by organic and nutrient matters and identifying the environmental pressures, examine the impact of the loads to Nhu Y River, Thua Thien-Hue Province. Five stations were sampled at Nhu Y River, the research had monitoring of water quality parameters such as Temperature (Temp), Dissolved Oxygen (DO), Biological Oxygen Demand (BOD5), Chemical Oxygen Demand (COD), Nitrate (NO3-) and Phosphate (PO43-). The research used multivariate statistical techniques such as correlation analysis, principal component analysis (PCA) and cluster analysis (CA) to assess water quality. The correlation analysis shown a strong positive correlation exists between water quality parameters such as TempDO and BOD5COD (p<0.01). The PCA technique was applied to water quality data sets, which was obtained from Nhu Y River and the results show that the indices which has changed water quality. The results of the PCA using a varimax rotation technique were illustrated with two principal components (PC) and accounts for 62.207% of the overall total variance. The first PC accounted for 40.873% of the total variance, which was loaded with Temp, DO, BOD5 and COD. The second PC consists of NO3- and PO43- which accounts for 21.334% of the total variance, it can be due to the discharge of agricultural activities. Similarly, the CA has identified two major clusters involving: BOD5, COD, Temp, DO (the first cluster) and NO3-, PO43- (the second cluster).

scholarly journals Cladoceran abundance in relation to water quality parameters of Chakki Talab, Bodhan, Telangana

2018 ◽  
Vol 8 (01) ◽  
Author(s):  
Vasudha Lingampally ◽  
V.R. Solanki ◽  
D. L. Anuradha ◽  
Sabita Raja
Keyword(s):  
Water Quality ◽  
Dissolved Oxygen ◽  
Oxygen Demand ◽  
Total Dissolved Solids ◽  
Quality Parameters ◽  
Biological Oxygen Demand ◽  
Water Quality Parameters ◽  
Total Alkalinity ◽  
Total Hardness ◽  
One Year

In the present study an attempt has been made to evaluate water quality and related density of Cladocerans for a period of one year, October 2015 to September 2016. Water quality parameters such as temperature, PH, total dissolved solids, dissolved oxygen, biological oxygen demand, total alkalinity, total hardness, chlorides, phosphates, and nitrates are presented here to relate with the abundance of Cladocerans. The Cladoceran abundance reflects the eutrophic nature of the Chakki talab.

scholarly journals Potensi budidaya ikan di Waduk Embung Klamalu Kabupaten Sorong Provinsi Papua Barat: Kajian kualitas fisika kimia air

2013 ◽  
Vol 1 (3) ◽  
Author(s):  
Agustina Frasawi ◽  
Robert J Rompas ◽  
Juliaan Ch. Watung
Keyword(s):  
Carbon Dioxide ◽  
Water Quality ◽  
Dissolved Oxygen ◽  
Good Condition ◽  
Fish Farming ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Total Alkalinity ◽  
Physical Chemical ◽  
Papua Barat

The objective of this research was to measure and analyze the water quality parameters including temperature, brightness, pH, dissolved oxygen, total alkalinity, carbon dioxide and BOD in reservoir Embung Klamalu Sorong regency, and to know the factors that affected the water quality of Embung Klamalu. Measurement of water quality parameters was done in situ for temperature, brightness, pH and in laboratory for dissolved oxygen, total alkalinity, carbon dioxide, and BOD. The results showed the temperature at the five observation stations ranged from 26.2 to 29.8 0C, brightness 38 to 46 cm, pH 7.20 to 8.48 mg /L, dissolved oxygen from 7.20 to 8.48 mg / L, alkalinity 100 to 150 mg /L, carbon dioxide from 25.90 to 28.95 mg / L, BOD from 0.20 to 0.38. Refers to the standards of water quality according to the PP. 82, 2001, it could be concluded that water physical-chemical qualities in fish farming locations in the Village Klamalu were still in good condition. Keywords: Water physical-chemical quality, aquaculture, waduk Embung Klamalu

scholarly journals IOT Based Smart Monitoring System for Fish Farming

2019 ◽  
Vol 8 (6S) ◽  
pp. 420-424 ◽  
Keyword(s):  
Water Quality ◽  
Current Method ◽  
Fish Farming ◽  
High Water ◽  
Quality Parameters ◽  
Water Levels ◽  
Water Quality Parameters ◽  
Fish Pond ◽  
Fish Ponds ◽  
Fish Tank

The purpose of the current method is to create a safe and secure that helps the fish pond owners and aquatic planters in producing high quality fish by maintaining normal water levels in the fish tank. The flow of the low or high water in the fish pond will solve the long-term problem of killing fish in a fish tank. Each water quality can affect the health of animals alone. The flow of water on fish ponds discusses how every day should be monitored. This should ensure quality by handling the PH, membrane, temperature, ammonia etc. It is a symbol of good quality water quality standards and poor water quality pools and how it should be upgraded. It is recommended that a prerequisite to increase production by ensuring sustainable fresh quality, and consequently, priority should be given priority. Therefore, water quality parameters maintain balanced positions, culture is the basis for the health and development of living organisms. It is recommended to monitor and evaluate water quality parameters on a regular basis

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 Wavelet and statistical analyses of river water quality parameters: a case study in the Lower Minnesota River

2018 ◽  
Vol 13 (4) ◽  
pp. 922-931 ◽  
Author(s):  
Ang Gao ◽  
Shiqiang Wu ◽  
Senlin Zhu ◽  
Zhun Xu
Keyword(s):  
Water Quality ◽  
River Water ◽  
Wavelet Decomposition ◽  
Oxygen Demand ◽  
Quality Parameters ◽  
River Water Quality ◽  
Water Quality Parameters ◽  
Strong Negative Correlation ◽  
Minnesota River ◽  
One Year

Abstract Statistical and wavelet analyses are useful tools for analyzing river water quality parameters. In this study, they were employed to study parameters including biochemical oxygen demand (BOD), dissolved oxygen (DO), nitrate (NO3), ammonium (NH4), phosphate (PO4), total phosphorus (TP), total Kjeldahl nitrogen (TKN), chlorophyll a (CHLA), total suspended solids (TSS) and water temperature (TEMP) monitored at five hydrologic stations on the Lower Minnesota River, USA. Strong positive correlations were observed between CHLA-BOD, TP-TKN, TP-TSS and TKN-TSS, with strong negative correlation between DO-TEMP. Daubechies wavelet at level 5 has been calculated for some key water quality parameters as it gives the finer scale approximation and decomposition of each water parameter. The results show that TEMP and DO have relative quasi-periodicity of about one year, while the quasi-periodicity of NH4 and PO4 are weaker than for TEMP and DO. Correlations between some parameters based on wavelet decomposition results are consistent. The fluctuation range characteristics of some parameters were also analyzed through wavelet decomposition.

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