scholarly journals Water Quality Prediction in the Luan River Based on 1-DRCNN and BiGRU Hybrid Neural Network Model

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1273
Author(s):  
Jianzhuo Yan ◽  
Jiaxue Liu ◽  
Yongchuan Yu ◽  
Hongxia Xu
Keyword(s):  
Water Quality ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Data ◽  
Prediction Algorithm ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Quality Prediction ◽  
Water Quality Data ◽  
Water Quality Prediction

The current global water environment has been seriously damaged. The prediction of water quality parameters can provide effective reference materials for future water conditions and water quality improvement. In order to further improve the accuracy of water quality prediction and the stability and generalization ability of the model, we propose a new comprehensive deep learning water quality prediction algorithm. Firstly, the water quality data are cleaned and pretreated by isolation forest, the Lagrange interpolation method, sliding window average, and principal component analysis (PCA). Then, one-dimensional residual convolutional neural networks (1-DRCNN) and bi-directional gated recurrent units (BiGRU) are used to extract the potential local features among water quality parameters and integrate information before and after time series. Finally, a full connection layer is used to obtain the final prediction results of total nitrogen (TN), total phosphorus (TP), and potassium permanganate index (COD-Mn). Our prediction experiment was carried out according to the actual water quality data of Daheiting Reservoir, Luanxian Bridge, and Jianggezhuang at the three control sections of the Luan River in Tangshan City, Hebei Province, from 5 July 2018 to 26 March 2019. The minimum mean absolute percentage error (MAPE) of this method was 2.4866, and the coefficient of determination (R2) was able to reach 0.9431. The experimental results showed that the model proposed in this paper has higher prediction accuracy and generalization than the existing LSTM, GRU, and BiGRU models.

Assessement of Water Quality Parameters in Some Potential Pollutant Areas from Romania

2018 ◽  
Vol 69 (8) ◽  
pp. 2045-2049
Author(s):  
Catalina Gabriela Gheorghe ◽  
Andreea Bondarev ◽  
Ion Onutu
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Surface Water Quality ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Data ◽  
Chemical Parameters ◽  
Water Quality Data ◽  
Pollutant Sources ◽  
Water Quality Forecasting

Monitoring of environmental factors allows the achievement of some important objectives regarding water quality, forecasting, warning and intervention. The aim of this paper is to investigate water quality parameters in some potential pollutant sources from northern, southern and east-southern areas of Romania. Surface water quality data for some selected chemical parameters were collected and analyzed at different points from March to May 2017.

Generation of geolocated and radiometrically corrected true reflectance surfaces in the visible portion of the electromagnetic spectrum over large bodies of water using images from a sUAS

2020 ◽  
Vol 8 (3) ◽  
pp. 172-185
Author(s):  
Juan G. Arango ◽  
Brandon K. Holzbauer-Schweitzer ◽  
Robert W. Nairn ◽  
Robert C. Knox
Keyword(s):  
Water Quality ◽  
Near Infrared ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Data ◽  
Electromagnetic Spectrum ◽  
Water Quality Data ◽  
Ground Control Points ◽  
Bodies Of Water

The focus of this study was to develop true reflectance surfaces in the visible portion of the electromagnetic spectrum from small unmanned aerial system (sUAS) images obtained over large bodies of water when no ground control points were available. The goal of the research was to produce true reflectance surfaces from which reflectance values could be extracted and used to estimate optical water quality parameters utilizing limited in-situ water quality analyses. Multispectral imagery was collected using a sUAS equipped with a multispectral sensor, capable of obtaining information in the blue (0.475 μm), green (0.560 μm), red (0.668 μm), red edge (0.717 μm), and near infrared (0.840 μm) portions of the electromagnetic spectrum. To develop a reliable and repeatable protocol, a five-step methodology was implemented: (i) image and water quality data collection, (ii) image processing, (iii) reflectance extraction, (iv) statistical interpolation, and (v) data validation. Results indicate that the created protocol generates geolocated and radiometrically corrected true reflectance surfaces from sUAS missions flown over large bodies of water. Subsequently, relationships between true reflectance values and in-situ water quality parameters were developed.

Effects of sampling frequency on estimation accuracies of annual loadings for water quality parameters in different sized watersheds

2020 ◽  
Vol 55 (3) ◽  
pp. 261-277
Author(s):  
Lin Gao ◽  
Junyu Qi ◽  
Sheng Li ◽  
Glenn Benoy ◽  
Zisheng Xing ◽  
...  
Keyword(s):  
Water Quality ◽  
Suspended Solids ◽  
Nitrate Nitrogen ◽  
Sampling Frequency ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Data ◽  
Estimation Errors ◽  
Water Quality Data ◽  
Novel Approach

Abstract Potential errors or uncertainties of annual loading estimations for water quality parameters such as suspended solids (SS), nitrate-nitrogen (NO3-N), ortho-phosphorus (Ortho-P), potassium (K), calcium (Ca), and magnesium (Mg) can be greatly affected by sampling frequencies. In this study, annual loading estimation errors were assessed in terms of the coefficient of variation, relative bias, and probability of potential errors that were estimated with statistical samples taken at a series of sampling frequencies for a watershed in northwestern New Brunswick, Canada, and one of its sub-watersheds. Results indicate that annual loading estimation errors increased with decreasing sampling frequency for all water quality parameters. At the same sampling frequencies, the estimation errors were several times greater for the smaller watershed than those for the larger watershed, possibly due to the flushing nature of streamflows in the smaller watershed. We also found that low sampling frequency tended to underestimate the annual loadings of water quality parameters dominated by stormflow events (SS and K) and overestimate water quality parameters dominated by baseflow (Mg and Ca). These results can be used by hydrologists and water quality managers to determine sampling frequencies that minimize costs while providing acceptable estimation errors. This study also demonstrates a novel approach to assess potential errors when analyzing existing water quality data.

WQTA — A Water Quality Trend Analysis Program

1993 ◽  
Vol 28 (2) ◽  
pp. 311-336 ◽  
Author(s):  
I.K. Tsanis
Keyword(s):  
Water Quality ◽  
Trend Analysis ◽  
Rank Correlation ◽  
Data Bank ◽  
Quality Parameters ◽  
Specific Conductance ◽  
Water Quality Parameters ◽  
Downward Trend ◽  
Quality Data ◽  
Water Quality Data

Abstract A series of programs have been developed using the statistical package Minitab to evaluate trends of water quality parameters over a time period. These programs are included in an interactive program with graphic capabilities called Water Quality Trend Analysis (WQTA). The output files from the retrieval and year programs of the National Water Quality Data Bank (NAQUADAT) are used as input files to the program. The graphic output is obtained using the graphical package Axum. Twelve-month moving averages and the Spearman’s rank correlation are applied for trend assessments. The components of variability (seasonal, trend and random) of the water quality parameters are modelled using linear regression. The methods are applied successfully to selected physical and chemical water quality parameters collected at the mouth of Niagara River, at Niagara-on-the-Lake, during the period 1976–89. The specific conductance was decreasing for the period as the discharge was increasing, due to higher dilution effects. A modest downward trend for total phosphorus was observed for the period 1976–84, and there is no trend between 1984-89. A strong decreasing trend for chloride was observed during the 1977–84 period but this has levelled off since then. A strong upward trend for iron and a weak downward trend for lead was evident over the study period.

scholarly journals FM-GRU: A Time Series Prediction Method for Water Quality Based on seq2seq Framework

Water ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1031
Author(s):  
Jianlong Xu ◽  
Kun Wang ◽  
Che Lin ◽  
Lianghong Xiao ◽  
Xingshan Huang ◽  
...  
Keyword(s):  
Water Quality ◽  
Prediction Models ◽  
Time Series Prediction ◽  
Prediction Method ◽  
Quality Data ◽  
Enterprise Management ◽  
Feature Interaction ◽  
Quality Prediction ◽  
Water Quality Data ◽  
Water Quality Prediction

Water quality prediction plays a crucial role in both enterprise management and government environmental management. However, due to the variety in water quality data, inconsistent frequency of data acquisition, inconsistency in data organization, and volatility and sparsity of data, predicting water quality accurately and efficiently has become a key problem. This paper presents a recurrent neural network water quality prediction method based on a sequence-to-sequence (seq2seq) framework. The gate recurrent unit (GRU) model is used as an encoder and decoder, and a factorization machine (FM) is integrated into the model to solve the problem of high sparsity and high dimensional feature interaction in the data, which was not addressed by the water quality prediction models in prior research. Moreover, due to the long period and timespan of water quality data, we add a dual attention mechanism to the seq2seq framework to address memory failures in deep learning. We conducted a series of experiments, and the results show that our proposed method is more accurate than several typical water quality prediction methods.

scholarly journals Remote sensing-based algorithms for water quality monitoring in Olushandja Dam, north-Central Namibia

2020 ◽  
Author(s):  
Taimi S. Kapalanga ◽  
Zvikomborero Hoko ◽  
Webster Gumindoga ◽  
Loyd Chikwiramakomo
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Linear Relationship ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Monitoring ◽  
Quality Data ◽  
Water Quality Data ◽  
Measured Water

Abstract Frequent and continuous water quality monitoring of Olushandja Dam in Namibia is needed to inform timely decision making. This study was carried out from November 2014 to June 2015 with Landsat 8 reflectance values and field measured water quality data that were used to develop regression analysis-based retrieval algorithms. Water quality parameters considered included turbidity, total suspended solids (TSS), nitrates, ammonia, total nitrogen (TN), total phosphorus (TP) and total algae counts. Results show that turbidity levels exceeded the recommended limits for raw water for potable water treatment while TN and TP values are within acceptable values. Turbidity, TN, and TP and total algae count showed a medium to strong positive linear relationship between Landsat predicted and measured water quality data while TSS showed a weak linear relationship. The regression coefficients between predicted and measured values were: turbidity (R2 = 0.767); TN (R2 = 0.798,); TP (R2 = 0.907); TSS (R2 = 0.284,) and total algae count (R2 = 0.851). Prediction algorithms are generally best fit to derive water quality parameters. Remote sensing is recommended for frequent and continuous monitoring of Olushandja Dam as it has the ability to provide rapid information on the spatio-temporal variability of surface water quality.

scholarly journals Assessment of Water Quality of Brahmani River using Correlation and Regression Analysis

2020 ◽  
Author(s):  
Saroj Nayak
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Correlation Coefficient ◽  
Surface Water Quality ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Data ◽  
Chemical Parameters ◽  
Water Quality Data

This work evaluates the surface water quality in terms of physico-chemical parameters of the Brahmani River, Odisha using statistical analysis involving the calculation of correlation coefficient and regression equation. Besides this, the work also highlights and draws attention towards the “Water Quality Index” in a simplified format which may be used at large and could represent the reliable picture of water quality. Surface water quality data is taken from OSPCB of various location i.e. Panposh D/S, Rourkela D/S, Rengali, Talcher U/S, Kamalanga D/S, Bhuban, Pattamundai and was assessed for summer, monsoon, winter for the years 2011, 2012, 2013, 2014 and 2015. Average of values, minimum of values and maximum of values of water quality parameters were obtained seasonally over the above mentioned years. Besides this, the standard deviation for the water quality parameters was also obtained for water quality parameters namely pH, Temperature, DO, TDS, Alkalinity, EC, Na+, Ca2+, Mg2+, K+, F-, Cl-, NO3-, SO42- and PO42-. Seasonal changes in various physical and chemical parameters were analysed.The values obtained were compared with the guideline values for drinking water by Bureau of Indian Standard (BIS). A systematic correlation and regression study is carried out for three seasons, showed linear relationship among different water quality parameters. This provides an easy and rapid method of monitoring water quality. Highly significant (0.8< r <1.0), moderately significant (0.6< r <0.8) and significant (0.5< r <0.6) correlations between the parameters have been worked out. High correlation coefficient has been observed between TDS,EC-Na+, Ca2+, Cl-, SO42- ; Na+- Cl-. From the collected quantities, certain parameters were selected to derive WQI for the variations in water quality of each designated sampling site. WQI of Brahmani River ranged from 36.7 to 44.1 which falls in the range of good quality of water.Panposh D/S and Rourkela D/S showed poor water quality in summer and winter season. It is shown that WQI may be a useful tool for assessing water quality and predicting trend of variation in water quality at differentlocations in the Brahmani River.

scholarly journals WATER QUALITY ASSESSMENT OF SAIGON RIVER FOR PUBLIC WATER SUPPLY BASED ON WATER QUALITY INDEX

2020 ◽  
Vol 58 (5A) ◽  
pp. 85
Author(s):  
Thuy Chau To
Keyword(s):  
Water Quality ◽  
Water Supply ◽  
Water Quality Index ◽  
Quality Index ◽  
Water Quality Management ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Data ◽  
Public Water Supply ◽  
Water Quality Data

Water Quality Index (WQI) is a dimensional number that aggregates information from many water quality parameters according to a defined method. WQI is accepted as an efficient tool for water quality management. In this study, WQI of Saigon river for public water supply was calculated from nine water quality parameters including pH, suspended solids (SS), dissolved oxygen (DO), chemical oxygen demand (COD), nitrite, ammonia, phosphate, total dissolved iron and total coliform based on water quality data obtained monthly from January 2016 to December 2019 at three sampling sites. The results showed that most of WQI values belonged to class III (medium water quality with the WQIs of 35 – 64) and class IV (poor water quality with the WQIs of 11 – 34) and a deteriorating trend was observed from upstream to downstream of Saigon river. The river water quality could not be used for public water supply.

scholarly journals Water Quality Study for Grouper Mariculture in Divur Bay Dullah Island, Tual City

Omni-Akuatika ◽  
2017 ◽  
Vol 13 (1) ◽  
Author(s):  
Daniel Djokosetiyanto ◽  
Henny Fitri Nawati ◽  
Machfud Machfud ◽  
Achmad Fahrudin
Keyword(s):  
Water Quality ◽  
Current Velocity ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Data ◽  
Substrate Type ◽  
Water Quality Data ◽  
Quality Study ◽  
Culture Development ◽  
Range Of Values

Water quality is one of the factors that determine the feasibility and success of the development of grouper mariculture. The objective of this research was to analyze water quality which was prepared for grouper mariculture. This research was carried out in Divur Bay, Dullah island of Tual City. The water quality parameters observed were including temperature, brightness, salinity, pH, current velocity, depth, substrate type, DO, nitrate and phosphate in ten stations spread in Divur Bay. Water quality data were analyzed descriptively. The results showed that Divur Bay was feasible for the grouper culture development with a range of values of temperature, brightness, salinity, pH, current velocity, depth, DO, nitrate and phosphate obtained were 30-31oC; 2.28-7.86 m; 33-35 ppt; 7.7-8.1; 0.06-0.617m/s; 2..28-18.58 m; 3.7-4.8 ppm; 0.0015-0.219 ppm; 0.0076-0.0767 ppm.

scholarly journals A Long-Term Water Quality Prediction Method Based on the Temporal Convolutional Network in Smart Mariculture

Water ◽  
2021 ◽  
Vol 13 (20) ◽  
pp. 2907
Author(s):  
Yuexin Fu ◽  
Zhuhua Hu ◽  
Yaochi Zhao ◽  
Mengxing Huang
Keyword(s):  
Water Quality ◽  
Prediction Method ◽  
Quality Parameters ◽  
Quality Data ◽  
Convolutional Network ◽  
Water Quality Data ◽  
Water Quality Prediction ◽  
Term Prediction ◽  
Long Term Prediction

In smart mariculture, traditional methods are not only difficult to adapt to the complex, dynamic and changeable environment in open waters, but also have many problems, such as poor accuracy, high time complexity and poor long-term prediction. To solve these deficiencies, a new water quality prediction method based on TCN (temporal convolutional network) is proposed to predict dissolved oxygen, water temperature, and pH. The TCN prediction network can extract time series features and in-depth data features by introducing dilated causal convolution, and has a good effect of long-term prediction. At the same time, it is predicted that the network can process time series data in parallel, which greatly improves the time throughput of the model. Firstly, we arrange the 23,000 sets of water quality data collected in the cages according to time. Secondly, we use the Pearson correlation coefficient method to analyze the correlation information between water quality parameters. Finally, a long-term prediction model of water quality parameters based on a time domain convolutional network is constructed by using prior information and pre-processed water quality data. Experimental results show that long-term prediction method based on TCN has higher accuracy and less time complexity, compared with RNN (recurrent neural network), SRU (simple recurrent unit), BI-SRU (bi-directional simple recurrent unit), GRU (gated recurrent unit) and LSTM (long short-term memory). The prediction accuracy can reach up to 91.91%. The time costs of training model and prediction are reduced by an average of 64.92% and 7.24%, respectively.

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