scholarly journals Quantitative estimation of wastewater quality parameters by hyperspectral band screening using GC, VIP and SPA

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e8255 ◽  
Author(s):  
Zheng Xing ◽  
Junying Chen ◽  
Xiao Zhao ◽  
Yu Li ◽  
Xianwen Li ◽  
...  
Keyword(s):  
Water Quality ◽  
Oxygen Demand ◽  
Sewage Water ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Total Alkalinity ◽  
Model Accuracy ◽  
Information Preservation ◽  
Spectral Models ◽  
Spectral Ranges

Water pollution has been hindering the world’s sustainable development. The accurate inversion of water quality parameters in sewage with visible-near infrared spectroscopy can improve the effectiveness and rational utilization and management of water resources. However, the accuracy of spectral models of water quality parameters is usually prone to noise information and high dimensionality of spectral data. This study aimed to enhance the model accuracy through optimizing the spectral models based on the sensitive spectral intervals of different water quality parameters. To this end, six kinds of sewage water taken from a biological sewage treatment plant went through laboratory physical and chemical tests. In total, 87 samples of sewage water were obtained by adding different amount of pure water to them. The raw reflectance (Rraw) of the samples were collected with analytical spectral devices. The Rraw-SNV were obtained from the Rraw processed with the standard normal variable. Then, the sensitive spectral intervals of each of the six water quality parameters, namely, chemical oxygen demand (COD), biological oxygen demand (BOD), NH3-N, the total dissolved substances (TDS), total hardness (TH) and total alkalinity (TA), were selected using three different methods: gray correlation (GC), variable importance in projection (VIP) and set pair analysis (SPA). Finally, the performance of both extreme learning machine (ELM) and partial least squares regression (PLSR) was investigated based on the sensitive spectral intervals. The results demonstrated that the model accuracy based on the sensitive spectral ranges screened through different methods appeared different. The GC method had better performance in reducing the redundancy and the VIP method was better in information preservation. The SPA method could make the optimal trade-offs between information preservation and redundancy reduction and it could retain maximal spectral band intervals with good response to the inversion parameters. The accuracy of the models based on varied sensitive spectral ranges selected by the three analysis methods was different: the GC was the highest, the SPA came next and the VIP was the lowest. On the whole, PLSR and ELM both achieved satisfying model accuracy, but the prediction accuracy of the latter was higher than the former. Great differences existed among the optimal inversion accuracy of different water quality parameters: COD, BOD and TN were very high; TA relatively high; and TDS and TH relatively low. These findings can provide a new way to optimize the spectral model of wastewater biochemical parameters and thus improve its prediction precision.

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.

Dynamics of Micro Algae in Relation to Water Quality Parameters of Pasupatheswarar Temple Pond, Annamalai Nagar, Tamil Nadu

2015 ◽  
Vol 46 ◽  
pp. 1-7
Author(s):  
B. Elayaraj ◽  
M. Selvaraju
Keyword(s):  
Water Quality ◽  
Tamil Nadu ◽  
Oxygen Demand ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Total Alkalinity ◽  
Algal Community ◽  
Sampling Station ◽  
Free Carbon Dioxide ◽  
Calcium Magnesium

The current study deals with water quality variations and micro algal community structure in the highly eutrophic pond. Several water quality parameters were evaluated during the period from July 2014 to June 2015 from sampling station sited from Annamalai Nagar viz., Pasupatheswarar temple pond. The water quality parameters like Air and water temperature, turbidity, electrical conductivity, total dissolved solids, total alkalinity, pH, free carbon-dioxide, dissolved oxygen, biological oxygen demand (BOD), chemical oxygen demand (COD), calcium, magnesium, phosphate and nitrate were analysed. A total 29 species were observed during the study period of which 11 species from the class Cynophyceae, 9 species from the class Chlorophyceae, 6 species from the class Bacillariophyceae and 3 species from the class Euglenophyceae. Maximum species of the class Cyanophyceae were observed during study period. The Microcystisaeruginosa species observed in the pond indicates the signs of eutrophication of pond. The water quality parameters such as temperature, alkalinity, phosphate and nitrates are favourable for the growth of phytoplankton.

scholarly journals ANALYSIS OF POTENTIAL PHYSICO CHEMICAL WATER QUALITY PARAMETERS OF ELENGENA BEEL, JAGIROAD, MORIGAON, ASSAM

YMER Digital ◽  
2021 ◽  
Vol 20 (10) ◽  
pp. 100-106
Author(s):  
JAYANTA KUMAR BORA ◽  
◽  
MD. Y HASSAN ◽  
M BURAGOHAIN ◽  
◽  
...  
Keyword(s):  
Water Quality ◽  
Water Temperature ◽  
Oxygen Demand ◽  
Paper Mill ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Total Alkalinity ◽  
Total Hardness ◽  
Physico Chemical ◽  
Chemical Water

The study was made to investigate the potential physico-chemical water quality of Elengena beel. In this study 40 nos water samples were collected from 4 sampling sites (10 from each) of Elengena beel and had been analyzed for some water quality parameters and ranges of results were found as - water temperature, transperancy, depth, pH, dissolved oxygen (DO), biological oxygen demand (BOD), chemical oxygen demand (COD), total dissolved solids (TDS), total suspended solids (TSS), total solids (TS), total alkalinity (TA), total hardness (TH), chloride (Cl-) and fluoride (F-). silicates (SiO2), free carbondioxide (FCO2), nitrate (NO3-), phosphate (PO43-), colour and odour. Nutrients were determined by following the standard procedures outlined in the American Public Health Association (APHA). The result showed that water temperature, transperancy, depth, pH, DO, BOD, COD, TA, TH, TS, TDS, SiO2 , F-CO2 , Cl- , NO3- , and PO43-were 19.9 ±0.28; 21.5 ± 0.71; 1.0 ± 0.23; 6.8 ± 0.15; 4.1 ± 0.34; 51.8 ± 2.32; 58.61 ± 3.22; 156.2 ± 1.2; 210.5 ± 0.2; 153.8 ±0.90; 170.2 ±0.60; 49.2 ± 0.85; 2.87 ± 0.02; 10.91 ± 1.32; 0.20 ± 0.01 and 0.10 ± 0.1 mg/L respectively. All the measured parameters were within the standard values of WHO. In general the present investigation found that the maximum parameters were not at a level of pollution. In order to stop further deterioration of Elengena beel water quality and to eventually restore the beneficial uses of the beel, management of effluents of Nagaon paper mill in the beel watershed should be given urgent priority.

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 GIS-Based Modelling of Water Quality in Different Hilsha Spawning Grounds and Sanctuaries of Bangladesh

2018 ◽  
pp. 1-11
Author(s):  
Rumana Yasmin ◽  
Mehady Islam
Keyword(s):  
Water Quality ◽  
Present Status ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Total Alkalinity ◽  
Total Hardness ◽  
Quality Model ◽  
Spawning Grounds ◽  
Temporal Modelling ◽  
Spatio Temporal

The current study was performed to monitor in situ condition and spatio-temporal modelling of the present status of water quality parameters of different spawning grounds and sanctuaries of Hilsha. The study was conducted in nine sites in lower Padma River (Maowa) to lower Meghna River (Bhola, Patuakhali) from 1 August 2015 to 31 January 2016. This study demonstrates surface water temperature, salinity, conductivity and transparency were ranged from 19.00-33.00°C, 0.10-2.90 ppt, 125.60-4720.00 µS/cm and 6.60-74.00 cm respectively. The values of pH, DO, free CO2, total alkalinity, total hardness and free NH3 were varied from 6.00-9.50, 4.50-11.60 mg/L, 3.46-24.00 mg/L, 33.00-172.50 mg/L, 34.20-1291.00 mg/L and 0.20-1.40 mg/L respectively. Moreover, water quality model reveals that the present status of some water quality parameters (free CO2, free NH3, transparency) deviated from optimum condition suitable for the normal physiological process and spawning of Hilsha.

scholarly journals Access of Ground Water Quality with Associated Physico-Chemical Parameters of Cuttack District, Odisha, India

2019 ◽  
Vol 8 (12S) ◽  
pp. 260-267
Keyword(s):  
Water Quality ◽  
Ground Water ◽  
Correlation Coefficient ◽  
Fluoride Concentration ◽  
Chemical Properties ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Total Alkalinity ◽  
Total Hardness ◽  
Potentially Toxic Trace Elements

The aim of present investigation was to analyze the variations in the physio-chemical properties of the ground water of Cuttack district Odisha. In the present study 98 samples were collected and analyzed to assess the quality of ground water. The pH, electrical conductivity (EC), total hardness and total alkalinity of the collected 98 samples were in the range of 4.6-7.3, 36-4550 μS/cm, 40-200 mgl-1, 20-680 mgl-1 respectively. Similarly, the other important water quality parameters such as; chloride, nitrate sulphate and phosphate concentration were varies between BDL-327, 1.8-86.25, BDL-194 and BDL to 3.2 mgl-1 respectively. The pH of the alluvial groundwater is controlled by the HCO3. The fluoride concentration was varies from BDL to 2.38 mgl-1. Apart from few samples, 90.81% fluoride contaminated samples comes under the category of quality group A (< 1 mgl-1flouride). Similarly, out of total samples collected only in three samples the uranium concentration estimated to be more than 5ppb. Among the water quality parameters there exist a positive correlation between pH and fluoride with a correlation coefficient of 0.641. From the correlation analysis it is found that, higher concentration of fluorid correlated with higher pH. Similarly the correlation coefficient between calcium and chloride is very high i.e. 0.500, which strongly supported the existence of calcium in the study area is predominantly in the form of CaCl2. Most of the ground water samples meet the requirements of the WHO drinking water standards with respect to salinity, main constituents and potentially toxic trace elements such as uranium

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.

scholarly journals Mapping Water Quality Parameters in Urban Rivers from Hyperspectral Images Using a New Self-Adapting Selection of Multiple Artificial Neural Networks

2020 ◽  
Vol 12 (2) ◽  
pp. 336 ◽  
Author(s):  
Yishan Zhang ◽  
Lun Wu ◽  
Huazhong Ren ◽  
Yu Liu ◽  
Yongqian Zheng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Water Quality ◽  
Artificial Neural Networks ◽  
Oxygen Demand ◽  
Quality Parameters ◽  
Hyperspectral Remote Sensing ◽  
Water Quality Parameters ◽  
Proposed Model ◽  
Selection Of

Protection of water environments is an important part of overall environmental protection; hence, many people devote their efforts to monitoring and improving water quality. In this study, a self-adapting selection method of multiple artificial neural networks (ANNs) using hyperspectral remote sensing and ground-measured water quality data is proposed to quantitatively predict water quality parameters, including phosphorus, nitrogen, biochemical oxygen demand (BOD), chemical oxygen demand (COD), and chlorophyll a. Seventy-nine ground measured data samples are used as training data in the establishment of the proposed model, and 30 samples are used as testing data. The proposed method based on traditional ANNs of numerical prediction involves feature selection of bands, self-adapting selection based on multiple selection criteria, stepwise backtracking, and combined weighted correlation. Water quality parameters are estimated with coefficient of determination R 2 ranging from 0.93 (phosphorus) to 0.98 (nitrogen), which is higher than the value (0.7 to 0.8) obtained by traditional ANNs. MPAE (mean percent of absolute error) values ranging from 5% to 11% are used rather than root mean square error to evaluate the predicting precision of the proposed model because the magnitude of each water quality parameter considerably differs, thereby providing reasonable and interpretable results. Compared with other ANNs with backpropagation, this study proposes an auto-adapting method assisted by the above-mentioned methods to select the best model with all settings, such as the number of hidden layers, number of neurons in each hidden layer, choice of optimizer, and activation function. Different settings for ANNS with backpropagation are important to improve precision and compatibility for different data. Furthermore, the proposed method is applied to hyperspectral remote sensing images collected using an unmanned aerial vehicle for monitoring the water quality in the Shiqi River, Zhongshan City, Guangdong Province, China. Obtained results indicate the locations of pollution sources.

Sign in / Sign up

Export Citation Format

Share Document