scholarly journals Modelling Water Quality Improvements in a South Korean Inter-Basin Water Transfer System

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3173
Author(s):  
Hye Won Lee ◽  
Bo-Min Yeom ◽  
Jung Hyun Choi
Keyword(s):  
Water Quality ◽  
Quality Management ◽  
Constructed Wetlands ◽  
Management Practices ◽  
Water Quality Management ◽  
Quality Data ◽  
Watershed Model ◽  
Selective Withdrawal ◽  
Lake Water Quality ◽  
Water Quality Data

In this study, we investigated the feasibility of using constructed wetlands for non-point source pollution reduction. The effect of constructed wetlands in reducing suspended solids (SS) was analyzed using an integrated modeling system of watershed model (HSPF), reservoir model (CE-QUAL-W2), and stream model (EFDC) to investigate the behavior and accumulation of the pollution sources based on 2017 water quality data. The constructed wetlands significantly reduced the SS concentration by approximately 30%, and the other in-lake management practices (e.g., artificial floating islands and sedimentation basins) contributed an additional decrease of approximately 7%. Selective withdrawal decreased in the average SS concentration in the influents by ~10%; however, the effluents passing through the constructed wetlands showed only a slight difference of 1.9% in the average SS concentration. In order to meet the water quality standards, it was necessary to combine the constructed wetlands, in-lake water quality management, and selective withdrawal practices. Hence, it was determined that the model proposed herein is useful for estimating the quantitative effects of water quality management practices such as constructed wetlands, which provided practical guidelines for the application of further water quality management policies.

scholarly journals River Dissolved Oxygen Prediction Based on Random Forest and LSTM

2021 ◽  
Vol 37 (5) ◽  
pp. 901-910
Author(s):  
Juan Huan ◽  
Bo Chen ◽  
Xian Gen Xu ◽  
Hui Li ◽  
Ming Bao Li ◽  
...  
Keyword(s):  
Water Quality ◽  
Random Forest ◽  
Quality Management ◽  
Dissolved Oxygen ◽  
Prediction Model ◽  
Water Quality Management ◽  
Quality Data ◽  
Mean Square ◽  
Water Quality Data ◽  
Better Than

HighlightsRandom Forest (RF) and LSTM were developed for river DO prediction.PH is the most important feature affecting DO prediction.The model base on RF is better than the model not on RF, and the dimensionality of the input data is reduced by RF.RF-LSTM model is outperformed SVR, RF-SVR, BP, RF-BP, LSTM, RNN models in DO prediction.Abstract. In order to improve the prediction accuracy of dissolved oxygen in rivers, a dissolved oxygen prediction model based on Random Forest (RF) and Long Short Term Memory networks (LSTM) is proposed. First, the Random Forest performs feature selection, which reduces the input dimension of the data and eliminates the influence of irrelevant variables on the prediction of dissolved oxygen. Then build the LSTM river dissolved oxygen prediction model to fit the relationship between water quality data and dissolved oxygen, and finally use real water quality data in the river for verification. The experimental results show that the mean square error (MSE), absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination (R2) of the RF-LSTM model are 0.658, 0.528, 13.502, 0.811, 0.744, respectively, which are better than other models. The RF-LSTM model has good predictive performance and can provide a reference for river water quality management. Keywords: Dissolved oxygen prediction, LSTM, Random forest, Time series, Water quality management.

Water quality management in the Crocodile River catchment, Eastern Transvaal, South Africa

1995 ◽  
Vol 32 (5-6) ◽  
pp. 201-208
Author(s):  
P. J. Ashton ◽  
F. C. van Zyl ◽  
R. G. Heath
Keyword(s):  
South Africa ◽  
Water Quality ◽  
Quality Management ◽  
Water Quality Management ◽  
Quality Data ◽  
Catchment Management ◽  
Land Uses ◽  
Water Quality Data ◽  
River Catchment ◽  
Management Actions

The Crocodile River catchment lies in an area which currently has one of the highest rates of sustained economic growth in South Africa and supports a diverse array of land uses. Water quality management is vital to resource management strategies for the catchment. A Geographic Information System (GIS) was used to display specific catchment characteristics and land uses, supplemented with integrative overlays depicting land-use impacts on surface water resources and the consequences of management actions on downstream water quality. The water quality requirements of each water user group were integrated to optimise the selection of rational management solutions for particular water quality problems. Time-series water quality data and cause-effect relationships were used to evaluate different water supply scenarios. The GIS facilitated the collation, processing and interpretation of the enormous quantity of spatially orientated information required for integrated catchment management.

The role of citizen science as a tool of public information in water quality management in the Brantas catchment, Indonesia

2021 ◽  
Author(s):  
Reza Pramana ◽  
Schuyler Houser ◽  
Daru Rini ◽  
Maurits Ertsen
Keyword(s):  
Water Quality ◽  
Quality Management ◽  
Citizen Science ◽  
Water Resource ◽  
Water Resource Management ◽  
Water Quality Management ◽  
Data Availability ◽  
Quality Data ◽  
Water Quality Data ◽  
Data Set

<p>Water quality in the rivers and tributaries of the Brantas catchment (about 12.000 km<sup>2</sup>) is deteriorating due to various reasons, including rapid economic development, insufficient domestic water treatment and waste management, and industrial pollution. Various parameters measured by agencies involved in water resource development and management and environmental management consistently demonstrate exceedance of the local water quality standards. Between the different agencies, water quality data are available – intermittently from 2009 until 2019 at 104 locations, but generally on a monthly basis. Still, opportunities to improve data availability are apparent, both to increase the amount and representability of the data sets. The opportunity to expand available data via citizen science is simultaneously an opportunity to provide education on water stewardship and empower citizens to participate in water quality management. We plan to involve people from eight communities living close to the river and researchers from two local universities in a citizen-science campaign. The community members would sample weekly at 10 locations, from upstream to downstream of the catchment. We will use probes and test strips to measure the temperature, electrical conductivity, pH, nitrate, phosphate, ammonia, iron, and dissolved oxygen. The results will potentially be combined with the data from government agencies to construct an integrated water quality data set to improve decision making and the quality of community engagement in water resource management.</p>

A storm water retrofit plan for the mimico creek watershed

1999 ◽  
Vol 39 (12) ◽  
pp. 133-140
Author(s):  
J. Y. Li ◽  
D. Banting
Keyword(s):  
Water Quality ◽  
Quality Management ◽  
Great Lakes ◽  
Management Practices ◽  
Water Quality Management ◽  
Storm Water ◽  
Planning Tool ◽  
Creek Watershed ◽  
Urbanized Areas ◽  
Goals And Objectives

Storm water quality management in urbanized areas remains a challenge to Canadian municipalities as the funding and planning mechanisms are not well defined. In order to provide assistance to urbanized municipalities in the Great Lakes areas, the Great Lakes 2000 Cleanup Fund and the Ontario Ministry of the Environment commissioned the authors to develop a Geographic Information System planning tool for storm water quality management in urbanized areas. The planning tool comprises five steps: (1) definition of storm water retrofit goals and objectives; (2) identification of appropriate retrofit storm water management practices; (3) formulation of storm water retrofit strategies; (4) evaluation of strategies with respect to retrofit goals and objectives; and (5) selection of storm water retrofit strategies. A case study of the fully urbanized Mimico Creek wateshed in the City of Toronto is used to demonstrate the application of the planning tool.

scholarly journals New Patterns of Temporal and Spatial Variation in Water Quality of a Highly Artificialized Urban River-Course—a Case Study in the Tongzhou Section of the Beiyun River

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1446 ◽  
Author(s):  
Juanhui Ren ◽  
Ji Liang ◽  
Bo Ren ◽  
Xiuqing Zheng ◽  
Chaofan Guo
Keyword(s):  
Water Quality ◽  
Management Practices ◽  
Low Impact Development ◽  
Urban River ◽  
Quality Data ◽  
Dominant Factor ◽  
Water Runoff ◽  
Reclaimed Wastewater ◽  
Water Quality Data ◽  
Impervious Area

This paper aims to gain a better understanding of urban river pollution through evaluation of water quality. Data for 10 parameters at eight sites of the Tongzhou Section of the Beiyun River (TSBR) are analyzed. Hierarchical cluster analysis, fuzzy comprehensive assessment, discriminant analysis and Spearman’s correlation analysis were used to estimate the water situation of each cluster and analyze its spatial-temporal variations. Principal component analysis/factor analysis were applied to extract and recognize the sources responsible for water-quality variations. The results showed that temporal variation is greater than spatial and sewage discharge is the dominant factor of the seasonal distribution. Moreover, during the rapid-flow period, water quality is polluted by a combination of organic matter, phosphorus, bio-chemical pollutants and nitrogen; during the gentle-flow period, water quality is influenced by domestic and industrial waste, the activities of algae, aquatic plants and phosphorus pollution. In regard to future improvement of water quality in TSBR, the control of reclaimed wastewater from adjacent factories should first be put in place, as well as other techniques, for example, an increase of the impervious area, low-impact development, and integrated management practices should also be proposed in managing storm water runoff.

scholarly journals A Multivariate and Spatiotemporal Analysis of Water Quality in Code River, Indonesia

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Mochamad A. Pratama ◽  
Yan D. Immanuel ◽  
Dwinanti R. Marthanty
Keyword(s):  
Water Quality ◽  
Land Cover ◽  
Agricultural Land ◽  
Water Quality Management ◽  
Temporal Trends ◽  
Oxygen Demand ◽  
Principal Component ◽  
Water Quality Monitoring ◽  
Quality Data ◽  
Water Quality Data

The efficacy of a water quality management strategy highly depends on the analysis of water quality data, which must be intensively analyzed from both spatial and temporal perspectives. This study aims to analyze spatial and temporal trends in water quality in Code River in Indonesia and correlate these with land use and land cover changes over a particular period. Water quality data consisting of 15 parameters and Landsat image data taken from 2011 to 2017 were collected and analyzed. We found that the concentrations of total dissolved solid, nitrite, nitrate, and zinc had increasing trends from upstream to downstream over time, whereas concentrations of parameter biological oxygen demand, cuprum, and fecal coliform consistently undermined water quality standards. This study also found that the proportion of natural vegetation land cover had a positive correlation with the quality of Code River’s water, whereas agricultural land and built-up areas were the most sensitive to water pollution in the river. Moreover, the principal component analysis of water quality data suggested that organic matter, metals, and domestic wastewater were the most important factors for explaining the total variability of water quality in Code River. This study demonstrates the application of a GIS-based multivariate analysis to the interpretation of water quality monitoring data, which could aid watershed stakeholders in developing data-driven intervention strategies for improving the water quality in rivers and streams.

scholarly journals Watershed-Based Evaluation of Automatic Sensor Data: Water Quality and Hydroclimatic Relationships

2020 ◽  
Vol 12 (1) ◽  
pp. 396 ◽  
Author(s):  
Jacopo Cantoni ◽  
Zahra Kalantari ◽  
Georgia Destouni
Keyword(s):  
Water Quality ◽  
Lake Water ◽  
Water Discharge ◽  
Sensor Data ◽  
Quality Data ◽  
High Temporal Resolution ◽  
Lake Water Quality ◽  
Water Quality Data ◽  
Blue Green Algae ◽  
Water Quality Variables

Water is a fundamental resource and, as such, the object of multiple environmental policies requiring systematic monitoring of its quality as a main management component. Automatic sensors, allowing for continuous monitoring of various water quality variables at high temporal resolution, offer new opportunities for enhancement of essential water quality data. This study investigates the potential of sensor-measured data to improve understanding and management of water quality at watershed level. Self-organizing data maps, non-linear canonical correlation analysis, and linear regressions are used to assess the relationships between multiple water quality and hydroclimatic variables for the case study of Lake Mälaren in Sweden, and its total catchment and various watersheds. The results indicate water discharge from dominant watersheds into a lake, and lake water temperature as possible proxies for some key water quality variables in the lake, such as blue-green algae; the latter is, in turn, identified as a potential good proxy for lake concentration of total nitrogen. The relationships between water discharges into the lake and lake water quality dynamics identify the dominant contributing watersheds for different water quality variables. Seasonality also plays an important role in determining some possible proxy relationships and their usefulness for different parts of the year.

scholarly journals Investigation of the Soan River Water Quality Using Multivariate Statistical Approach

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Zakaullah ◽  
Naeem Ejaz
Keyword(s):  
Water Quality ◽  
River Water ◽  
Water Quality Management ◽  
Oxygen Demand ◽  
River Water Quality ◽  
Quality Data ◽  
Spatiotemporal Pattern ◽  
Multivariate Statistical ◽  
Water Quality Data

Evaluating the quality of river water is a critical process due to pollution and variations of natural or anthropogenic origin. For the Soan River (Pakistan), seven sampling sites were selected in the urban area of Rawalpindi/Islamabad, and 18 major chemical parameters were examined over two seasons, i.e., premonsoon and postmonsoon 2019. Multivariate statistical approaches such as the Spearman correlation coefficient, cluster analysis (CA), and principal component analysis (PCA) were used to evaluate the water quality of the Soan River based on temporal and spatial patterns. Analytical results obtained by PCA show that 92.46% of the total variation in the premonsoon season and 93.11% in the postmonsoon season were observed by only two loading factors in both seasons. The PCA and CA made it possible to extract and recognize the origins of the factors responsible for water quality variations during the year 2019. The sampling stations were grouped into specific clusters on the basis of the spatiotemporal pattern of water quality data. The parameters dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), turbidity, and total suspended solids (TSS) are among the prominent contributing variations in water quality, indicating that the water quality of the Soan River deteriorates gradually as it passes through the urban areas, receiving domestic and industrial wastewater from the outfalls. This study indicates that the adopted methodology can be utilized effectively for effective river water quality management.

A New Assessment Model for Coastal Water Quality Based on Nonlinear Mapping Theory

2013 ◽  
Vol 726-731 ◽  
pp. 1073-1077
Author(s):  
Ren Qiang Lu
Keyword(s):  
Water Quality ◽  
Coastal Water ◽  
Water Quality Management ◽  
Mapping Method ◽  
Assessment Model ◽  
Quality Data ◽  
Nonlinear Mapping ◽  
Water Quality Data ◽  
Coastal Water Quality ◽  
Mapping Theory

A new assessment model for coastal water quality was proposed based on the nonlinear mapping theory. Taking the water quality monitoring data of Tianjins coastal marine as an example, firstly, the high-dimension water quality data were mapped to the two-dimension plane by nonlinear mapping method. Secondly, the water quality assessment model was established according to the position relationship of mapping points. Then, the water quality was assessed based on the model. Through application we could found the method proposed in this paper was simple and practicable. It is science and effectiveness of applying the nonlinear mapping method to assessment the water quality. It could be used to supply the decision support for the coastal water quality management.

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