Weather Correlated Short-Term Dynamics in Certain Water Quality Parameters of the Ganga River in Low-Flow Conditions

2019 ◽  
Vol 90 (4) ◽  
pp. 677-687
Author(s):  
Ashutosh Tripathi ◽  
Niraj Kumar ◽  
D. K. Chauhan
Keyword(s):  
Water Quality ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Ganga River ◽  
Low Flow ◽  
Flow Conditions ◽  
Short Term

Short‐Term Effects of Wildfire Ash on Water Quality Parameters: A Laboratory Approach

2021 ◽  
Author(s):  
Darlan Q. Brito ◽  
Luiz H. G. Santos ◽  
Carlos José Sousa Passos ◽  
Eduardo C. Oliveira-Filho
Keyword(s):  
Water Quality ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Short Term ◽  
Short Term Effects

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.

Change regularity of water quality parameters in leakage flow conditions and their relationship with iron release

2017 ◽  
Vol 124 ◽  
pp. 353-362 ◽  
Author(s):  
Jingqing Liu ◽  
Huabin Shentu ◽  
Huanyu Chen ◽  
Ping Ye ◽  
Bing Xu ◽  
...  
Keyword(s):  
Water Quality ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Leakage Flow ◽  
Iron Release ◽  
Flow Conditions

scholarly journals R.A.OLAOYE et al. 2021 Variation of Groundwater and Rainwater Quality in a Nigerian Leper Colony

2021 ◽  
Vol 2 (3) ◽  
pp. 27-33
Author(s):  
Rebecca A. Olaoye ◽  
Akinwale O. Coker ◽  
Mynepalli K. Sridhar
Keyword(s):  
Water Quality ◽  
Water Samples ◽  
Quality Parameters ◽  
Water Sources ◽  
Water Quality Parameters ◽  
World Health ◽  
Slight Variation ◽  
Short Term ◽  
Adequate Treatment ◽  
Bacteriological Parameters

Adequate supply of potable water is a major challenge in most leper colony with emphasis often placed on water needs of “normal” people but little concern on the safe water source for the physically challenged and vulnerable lepers with limited mobility who cannot search for other sources of water outside designated colony. This study was designed to investigate the quality of water sources within a Nigerian leper colony. Periodic characterization of groundwater and rainwater samples was conducted using American Public Health Association (APHA) methods to determine physico-chemical parameters; appearance, odour, colour, taste, chloride, pH, sulphate, copper, zinc, iron, nitrate and bacteriological parameters; coliform organism and Escherichia coli (E-coli) against the world health organization (WHO) drinking water standard. Water samples were clear and odourless. Most of the parameters tested from both sources; groundwater and rainwater were within the recommended standard. Results from short term water quality parameters taken from 2010-2012 were relatively within the same range while the long-term decadal water quality parameters showed slight variation compared to the short term. Heavy metals showed remarkable variation in 2019 while bacteriological parameters from both water sources were above the permissible threshold. For potable use, water sources require adequate treatment. Boiling or disinfection is recommended until water samples have been retested to ascertain that contamination has been eliminated. In addition, home water-treatment through the use of filters, solar disinfection, or flocculants should be provided to make the water safe.

scholarly journals Determination of Thermal Pollution Effect on End Part of Stream

2020 ◽  
Vol 9 (3) ◽  
pp. 3311-3319
Keyword(s):  
Water Quality ◽  
Power Plant ◽  
Thermal Power Plant ◽  
Thermal Power ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Low Flow ◽  
Thermal Plume ◽  
Pollutant Source ◽  
Set Up

The present study was conducted to evaluate the environmental impact of thermal effluent sources on the main water quality parameters at the low flow conditions. The low flow causes the flow velocity to be low which causes accumulation of any pollutant source. The study was performed by creating a 2-d model of the last reach of Rosetta branch at winter closure. Delft 3d software is used to create a hydro-dynamic model to simulate the flow pattern within a 5 km of the branch upstream Edfina regulator. Water quality model is coupled afterwards to simulate the water quality parameters. A base case scenario of the current state at the low flow condition is set up and calibrated. Another scenario is performed after adding a thermal pollutant source. Thermal power plant is used as an application of thermal pollutant source. Cooling water is with drawled from an intake and discharged back to the water source with a relatively higher temperature downstream the intake. A case study of Dairut thermal power plant which is planned to be constructed at this area is used. Hydrographic survey is performed to collect essential hydraulic data for the model. Field measurements are performed to collect water quality along the area. A numerical model was set up and the area was simulated. Results showed accumulation of thermal plume. The higher temperatures lowered the dissolved oxygen in the thermal plume area. On the other hand, BOD and NO3 values increased with different rates. Ammonium was positively affected and was lowered.

Short‐term effect of the inoculation of probiotics in mature bioflocs: Water quality parameters and abundance of heterotrophic and ammonia‐oxidizing bacteria

2019 ◽  
Vol 51 (1) ◽  
pp. 255-264 ◽  
Author(s):  
Anselmo Miranda‐Baeza ◽  
Mariela Nolasco‐López ◽  
Martha Elisa Rivas‐Vega ◽  
José Alberto Huerta‐Rábago ◽  
Luis Rafael Martínez‐Córdova ◽  
...  
Keyword(s):  
Water Quality ◽  
Quality Parameters ◽  
Term Effect ◽  
Water Quality Parameters ◽  
Ammonia Oxidizing Bacteria ◽  
Short Term ◽  
Short Term Effect

scholarly journals A Water Quality Prediction Method Based on the Deep LSTM Network Considering Correlation in Smart Mariculture

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1420 ◽  
Author(s):  
Zhuhua Hu ◽  
Yiran Zhang ◽  
Yaochi Zhao ◽  
Mingshan Xie ◽  
Jiezhuo Zhong ◽  
...  
Keyword(s):  
Water Quality ◽  
Water Temperature ◽  
Prediction Accuracy ◽  
Prediction Method ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Prediction ◽  
Short Term ◽  
Water Quality Prediction ◽  
Term Prediction

An accurate prediction of cage-cultured water quality is a hot topic in smart mariculture. Since the mariculturing environment is always open to its surroundings, the changes in water quality parameters are normally nonlinear, dynamic, changeable, and complex. However, traditional forecasting methods have lots of problems, such as low accuracy, poor generalization, and high time complexity. In order to solve these shortcomings, a novel water quality prediction method based on the deep LSTM (long short-term memory) learning network is proposed to predict pH and water temperature. Firstly, linear interpolation, smoothing, and moving average filtering techniques are used to repair, correct, and de-noise water quality data, respectively. Secondly, Pearson’s correlation coefficient is used to obtain the correlation priors between pH, water temperature, and other water quality parameters. Finally, a water quality prediction model based on LSTM is constructed using the preprocessed data and its correlation information. Experimental results show that, in the short-term prediction, the prediction accuracy of pH and water temperature can reach 98.56% and 98.97%, and the time cost of the predictions is 0.273 s and 0.257 s, respectively. In the long-term prediction, the prediction accuracy of pH and water temperature can reach 95.76% and 96.88%, respectively.

Investigation of Some Water Quality Parameters of Pond Water under Mymensingh Municipality

2015 ◽  
Vol 8 (1) ◽  
pp. 85-89
Author(s):  
F Zannat ◽  
MA Ali ◽  
MA Sattar
Keyword(s):  
Water Quality ◽  
Water Samples ◽  
Chemical Properties ◽  
Pond Water ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Urban Region ◽  
Mn Toxicity ◽  
Ph Values ◽  
Drinking Water Standard

A study was conducted to evaluate the water quality parameters of pond water at Mymensingh Urban region. The water samples were collected from 30 ponds located at Mymensingh Urban Region during August to October 2010. The chemical analyses of water samples included pH, EC, Na, K, Ca, S, Mn and As were done by standard methods. The chemical properties in pond water were found pH 6.68 to 7.14, EC 227 to 700 ?Scm-1, Na 15.57 to 36.00 ppm, K 3.83 to 16.16 ppm, Ca 2.01 to 7.29 ppm, S 1.61 to 4.67 ppm, Mn 0.33 to 0.684 ppm and As 0.0011 to 0.0059 ppm. The pH values of water samples revealed that water samples were acidic to slightly alkaline in nature. The EC value revealed that water samples were medium salinity except one sample and also good for irrigation. According to drinking water standard Mn toxicity was detected in pond water. Considering Na, Ca and S ions pond water was safe for irrigation and aquaculture. In case of K ion, all the samples were suitable for irrigation but unsuitable for aquaculture.J. Environ. Sci. & Natural Resources, 8(1): 85-89 2015

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