scholarly journals A new online monitoring and management system for accidental pollution events developed for the regional water basin in Ningbo, China

2011 ◽  
Vol 64 (9) ◽  
pp. 1828-1834 ◽  
Author(s):  
Gaosheng Zhang ◽  
Linlin Chen ◽  
Yuedan Liu ◽  
TaeSoo Chon ◽  
Zongming Ren ◽  
...  
Keyword(s):  
Water Quality ◽  
Management System ◽  
Online Monitoring ◽  
Water Quality Management ◽  
Warning System ◽  
Quality Data ◽  
Self Organizing Map ◽  
Water Quality Data ◽  
Accidental Pollution ◽  
Pollution Events

Due to urgency of the accidental pollution events (APE) on one side and the variability in water quality data on the other side, a new online monitoring and management system (OMMS) was developed for the purpose of sustainable water quality management and human health protection as well. The Biological Early Warning System (BEWS) based on the behavioral responses (behavior strength) of medaka (Oryzias latipes) were built in combination with the physico-chemical factor monitoring system (PFMS) in OMMS. OMMS included a monitoring center and six monitoring stations. Communication between the center and the peripheral stations was conducted by the General Packet Radio Service (GPRS) network transmission complemented by a dial-up connection for use when GPRS was unavailable. OMMS could monitor water quality continuously for at least 30 days. Once APEs occurred, OMMS would promptly notify the administrator to make some follow up decisions based on the Emergency Treatment of APE. Meanwhile, complex behavioral data were analyzed by Self-Organizing Map to properly classify behavior response data before and after contamination. By utilizing BEWS, PFMS and the modern data transmission in combination, OMMS was efficient in monitoring the water quality more realistically.

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.

Implementation of a large scale water quality data management system

1976 ◽  
Vol 10 (1) ◽  
pp. 31-36 ◽  
Author(s):  
William D. Haseman ◽  
Clyde Holsapple ◽  
Andrew B. Whinston
Keyword(s):  
Water Quality ◽  
Data Management ◽  
Management System ◽  
Large Scale ◽  
Data Management System ◽  
Quality Data ◽  
Water Quality Data

scholarly journals PENGEMBANGAN SISTEM DATABASE ONLINE MONITORING (OnLimo) KUALITAS AIR

2018 ◽  
Vol 1 (2) ◽  
Author(s):  
Heru Dwi Wahjono
Keyword(s):  
Water Quality ◽  
Real Time ◽  
Online Monitoring ◽  
Water Source ◽  
Underground Water ◽  
Decision Makers ◽  
Quality Data ◽  
Base System ◽  
Water Quality Data ◽  
Processing Water

Recent water quality decrease has caused difficult in finding clean water source for people and their daily life. Monitoring on water quality had been carried out many times, from up stream to down stream. It’s necessary to do Online Monitoring on ground and underground water quality continuously, so that the effect of water quality decrease could be detected earlier and handle directly. The output of water quality data needs to be processed so that the society and the decision makers could see the information publicly. So, we need a design of structured database of online and real-time water quality data processing. Water quality data management using structured data base system could make water source data retracing easier. Katakunci : database struktur, online monitoring, real time monitoring 

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.

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.

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.

scholarly journals Multiple stressors threaten the imperiled coastal foundation species eelgrass (Zostera marina) in Chesapeake Bay, USA

2017 ◽  
Author(s):  
Jonathan S Lefcheck ◽  
David J Wilcox ◽  
Rebecca R Murphy ◽  
Scott R Marion ◽  
Robert J Orth
Keyword(s):  
Water Quality ◽  
Chesapeake Bay ◽  
Zostera Marina ◽  
Multiple Stressors ◽  
Physiological Stress ◽  
Carbon Sink ◽  
Water Quality Management ◽  
Water Clarity ◽  
Quality Data ◽  
Water Quality Data

Interactions among global change stressors and their effects at large scales are often proposed, but seldom evaluated. This situation is primarily due to lack of comprehensive, sufficiently long-term, and spatially-extensive datasets. Seagrasses, which provide nursery habitat, improve water quality, and constitute a globally-important carbon sink, are among the most vulnerable habitats on the planet. Here, we unite 31-years of high-resolution aerial monitoring and water quality data to elucidate the patterns and drivers of eelgrass (Zostera marina) abundance in Chesapeake Bay, USA, one of the largest and most valuable estuaries in the world with an unparalleled history of regulatory efforts. We show that eelgrass area has declined 29% in total since 1991, with wide-ranging and severe ecological and economic consequences. We go on to identify an interaction between decreasing water clarity and warming temperatures as the primary driver of this trend. Declining clarity has gradually reduced eelgrass over the past two decades, primarily in deeper beds where light is already limiting. In shallow beds, however, reduced visibility exacerbates the physiological stress of acute warming, leading to recent instances of decline approaching 80%. While degraded water quality has long been known to influence underwater grasses worldwide, we demonstrate a clear and rapidly emerging interaction with climate change. We highlight the urgent need to integrate a broader perspective into local water quality management, in the Chesapeake Bay and in the many other coastal systems facing similar stressors.

scholarly journals Monitoring Lotic Ecosystem by the Application of Water Quality Index (CCMEWQI)

2020 ◽  
Vol 17 (1) ◽  
pp. 0023
Author(s):  
Salman Et al.
Keyword(s):  
Water Quality ◽  
Water Quality Index ◽  
Quality Index ◽  
Water Quality Management ◽  
Water Quality Monitoring ◽  
Quality Data ◽  
Total Hardness ◽  
Water Quality Data ◽  
Quality Of Water ◽  
Lotic Ecosystem

Water Quality Index (WQI) as a tool to assess the water quality status provides advice related to the use of water quality monitoring data and it is a way for combining the complex water quality data into a single value or single statement.The present study was conducted on Al- Hilla river in the middle of Iraq from August 2012 to July 2013 at five selected stations in the river, from Al- Musaib city to Al- Hashimya at the south of Hilla to determine its suitability for aquatic environment (GWQI), drinking water (PWSI) and irrigation (IWQI).This index offers a useful representation of the overall quality of water for public or any intended use as well as indicating pollution, water quality management, and decision making. According to the obtained results, it can be concluded that the EC, TSS, Total hardness, Ca, Mg, DO, BOD5, and NO3 moved away from the desired standards when the temperature rises. The variable of value of this index may be due to increasing the ration of organic matters and converting the carbonate to bicarbonate. The results recorded high value of calcium and magnesium more than the standard value of WHO and IQS (50 mg/l and high value of total hardness more than 500 mg/l). Irrigation water quality index (IWQI) in the study sites were ranged between 66-83 ranged between fair and good.                                                  

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