GIS-based Surface Water Monitoring System and Modelling for Conservative and Reactive Pollutants, Applied on the Upper Basin of Olt River

2008 ◽  
Vol 59 (9) ◽  
Author(s):  
Alexandru M�sz�ros ◽  
Szabolcs L�nyi ◽  
Szilard M�t� ◽  
Nicolae Vasiliu ◽  
Daniela Vasiliu
Keyword(s):  
Experimental Data ◽  
Water Quality ◽  
Information System ◽  
Surface Water ◽  
Quality Parameters ◽  
Oxygen Balance ◽  
Data Set ◽  
Proposed Model ◽  
Environmental Information System ◽  
Pollutant Propagation

This paper presents the problem of pollutant propagation monitoring and modeling on the upper zone of a river. The first model of the pollutant propagation is based on the averaging mass balance, without computing chemical and biochemical transformations. The second model was developed using the Streeter Phelps oxygen balance, including the kinetic terms of re-aeration and oxygen consuming. Experimental data set was obtained on the upper zone of the Olt River; using this data set, the proposed model was calibrated. The development of the model is like a part of a GIS-based environmental information system. Calculated results are depicted by diagrams and thematic maps of the upper Basin of Olt River, representing the water-quality parameters.

Improvement of pre- and post-processing environments of the dynamic two-dimensional reservoir model CE-QUAL-W2 based on GIS

2003 ◽  
Vol 48 (10) ◽  
pp. 79-88 ◽  
Author(s):  
S.-R. Ha ◽  
G.-J. Bae ◽  
D.-H. Park ◽  
J.H. Cho
Keyword(s):  
Water Quality ◽  
Information System ◽  
Water Body ◽  
Model Simulation ◽  
Depth Map ◽  
Water Volume ◽  
Data Set ◽  
Environmental Information System ◽  
Water Quality Models ◽  
Optimal Set

An Environmental Information System (EIS) coupled with a Geographic Information System (GIS) and water quality models is developed to improve the pre- and post-data processing function of CE-QUAL-W2. Since the accuracy of the geometric data in terms of a diverse water body has a great effect on the water quality variables such as the velocity, kinetic reactions, the horizontal and vertical momentum, to prepare the bathymetry information has been considered a difficult issue for modellers who intend to use the model. For identifying Cross Section and Profile Information (CSPI), which precisely contains hydraulic features and geographical configuration of a waterway, the automated CSPI extraction program has been developed using Avenue Language of the PC Arc/view package. The program consists of three major steps: (1) getting the digital depth map of a waterway using GIS techniques; (2) creating a CSPI data set of segments in each branch using the program for CE-QUAL-W2 bathymetry input; (3) selecting the optimal set of bathymetry input by which the calculated water volume meets the observed volume of the water body. Through those approaches, it is clear that the model simulation results in terms of water quality as well as reservoir hydraulics rely upon the accuracy of bathymetry information.

scholarly journals Integrated versus isolated scenario for prediction dissolved oxygen at progression of water quality monitoring stations

2011 ◽  
Vol 15 (8) ◽  
pp. 2693-2708 ◽  
Author(s):  
A. Najah ◽  
A. El-Shafie ◽  
O. A. Karim ◽  
O. Jaafar
Keyword(s):  
Water Quality ◽  
Dissolved Oxygen ◽  
Prediction Model ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Effective Parameters ◽  
Data Set ◽  
Proposed Model ◽  
Input Parameters ◽  
Predicted Values

Abstract. This study examined the potential of Multi-layer Perceptron Neural Network (MLP-NN) in predicting dissolved oxygen (DO) at Johor River Basin. The river water quality parameters were monitored regularly each month at four different stations by the Department of Environment (DOE) over a period of ten years, i.e. from 1998 to 2007. The following five water quality parameters were selected for the proposed MLP-NN modelling, namely; temperature (Temp), water pH, electrical conductivity (COND), nitrate (NO3) and ammonical nitrogen (NH3-NL). In this study, two scenarios were introduced; the first scenario (Scenario 1) was to establish the prediction model for DO at each station based on five input parameters, while the second scenario (Scenario 2) was to establish the prediction model for DO based on the five input parameters and DO predicted at previous station (upstream). The model needs to verify when output results and the observed values are close enough to satisfy the verification criteria. Therefore, in order to investigate the efficiency of the proposed model, the verification of MLP-NN based on collection of field data within duration 2009–2010 is presented. To evaluate the effect of input parameters on the model, the sensitivity analysis was adopted. It was found that the most effective inputs were oxygen-containing (NO3) and oxygen demand (NH3-NL). On the other hand, Temp and pH were found to be the least effective parameters, whereas COND contributed the lowest to the proposed model. In addition, 17 neurons were selected as the best number of neurons in the hidden layer for the MLP-NN architecture. To evaluate the performance of the proposed model, three statistical indexes were used, namely; Coefficient of Efficiency (CE), Mean Square Error (MSE) and Coefficient of Correlation (CC). A relatively low correlation between the observed and predicted values in the testing data set was obtained in Scenario 1. In contrast, high coefficients of correlation were obtained between the observed and predicted values for the test sets of 0.98, 0.96 and 0.97 for all stations after adopting Scenario 2. It appeared that the results for Scenario 2 were more adequate than Scenario 1, with a significant improvement for all stations ranging from 4 % to 8 %.

scholarly journals Integrated versus isolated scenario for prediction dissolved oxygen at progression of water quality monitoring stations

2011 ◽  
Vol 8 (3) ◽  
pp. 6069-6112 ◽  
Author(s):  
A. A. Najah ◽  
A. El-Shafie ◽  
O. A. Karim ◽  
O. Jaafar
Keyword(s):  
Water Quality ◽  
Dissolved Oxygen ◽  
Prediction Model ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Effective Parameters ◽  
Data Set ◽  
Proposed Model ◽  
Input Parameters ◽  
Predicted Values

Abstract. This study examined the potential of Multi-layer Perceptron Neural Network (MLP-NN) in predicting dissolved oxygen (DO) at Johor River Basin. The river water quality parameters were monitored regularly each month at four different stations by the Department of Environment (DOE) over a period of ten years, i.e. from 1998 to 2007. The following five water quality parameters were selected for the proposed MLP-NN modelling, namely; temperature (Temp), water pH, electrical conductivity (COND), nitrate (NO3) and ammonical nitrogen (NH3–NL). In this study, two scenarios were introduced; the first scenario (Scenario 1) was to establish the prediction model for DO at each station based on five input parameters, while the second scenario (Scenario 2) was to establish the prediction model for DO based on the five input parameters and DO predicted at previous station (upstream). The model needs to verify when output results and the observed values are close enough to satisfy the verification criteria. Therefore, in order to investigate the efficiency of the proposed model, the verification of MLP-NN based on collection of field data within duration 2009–2010 is presented. To evaluate the effect of input parameters on the model, the sensitivity analysis was adopted. It was found that the most effective inputs were oxygen-containing (NO3) and oxygen demand (NH3–NL). On the other hand, Temp and pH were found to be the least effective parameters, whereas COND contributed the lowest to the proposed model. In addition, 17 neurons were selected as the best number of neurons in the hidden layer for the MLP-NN architecture. To evaluate the performance of the proposed model, three statistical indexes were used, namely; Coefficient of Efficiency (CE), Mean Square Error (MSE) and Coefficient of Correlation (CC). A relatively low correlation between the observed and predicted values in the testing data set was obtained in Scenario 1. In contrast, high coefficients of correlation were obtained between the observed and predicted values for the test sets of 0.98, 0.96 and 0.97 for all stations after adopting Scenario 2. It appeared that the results for Scenario 2 were more adequate than Scenario 1, with a significant improvement for all stations ranging from 4 % to 8 %.

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.

scholarly journals Water monitoring and analytic based thingspeak

2020 ◽  
Vol 10 (4) ◽  
pp. 3588
Author(s):  
Abbas Hussien Miry ◽  
Gregor Alexander Aramice
Keyword(s):  
Water Quality ◽  
Normal Values ◽  
Water Quality Monitoring ◽  
Lag Time ◽  
Quality Parameters ◽  
Quality Monitoring ◽  
Proposed Model ◽  
Response To Water ◽  
Matlab Programming ◽  
The Internet Of Things

Diseases associated with bad water have largely reported cases annually leading to deaths, therefore the water quality monitoring become necessary to provide safe water. Traditional monitoring includes manual gathering of samples from different points on the distributed site, and then testing in laboratory. This procedure has proven that it is ineffective because it is laborious, lag time and lacks online results to enhance proactive response to water pollution. Emergence of the Internet of Things (IoT) and step towards the smart life poses the successful using of IoT. This paper presents a water quality monitoring using IoT based ThingSpeak platform that provides analytic tools and visualization using MATLAB programming. The proposed model is used to test water samples using sensor fusion technique such as TDS and Turbidity, and then uploading data online to ThingSpeak platform to monitor and analyze. The system notifies authorities when there are water quality parameters out of a predefined set of normal values. A warning will be notified to user by IFTTT protocol.

scholarly journals Validation of the STRIVE model for coupling ecological processes and surface water flow

2010 ◽  
Vol 13 (4) ◽  
pp. 741-759
Author(s):  
L. De Doncker ◽  
P. Troch ◽  
R. Verhoeven ◽  
K. Buis ◽  
P. Meire
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Water Flow ◽  
Research Question ◽  
Quality Parameters ◽  
Water Levels ◽  
Ecological Processes ◽  
Vegetation Growth ◽  
Surface Water Flow ◽  
Quality Aspects

The 1D model package STRIVE is verified for simulating the interaction between ecological processes and surface water flow. The model is general and can be adapted and further developed according to the research question. The hydraulic module, based on the Saint-Venant equations, is the core part. The presence of macrophytes influences the water quality and the discharge due to the flow resistance of the river, expressed by Manning's coefficient, and allows an ecological description of the river processes. Based on the advection–dispersion equation, water quality parameters are incorporated and modelled. Calculation of the water quantity parameters, coupled with water quality and inherent validation and sensitivity analysis, is the main goal of this research. An important study area is the River Aa near Poederlee (Belgium), a lowland river with a wealth of vegetation growth, where discharge and vegetation measurements are carried out on a regular basis. The developed STRIVE model shows good and accurate calculation results. The work highlights the possibility of STRIVE to model flow processes, water quality aspects and ecological interaction combined and separately. Coupling of discharges, water levels, amount of biomass and tracer values provides a powerful prediction modelling tool for the ecological behaviour of lowland rivers.

scholarly journals Multivariate Monitoring of Surface Water Quality: Physico-Chemical, Microbiological and 3D Fluorescence Characterization

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1673
Author(s):  
Claude Daou ◽  
Mervat El Hoz ◽  
Amine Kassouf ◽  
Bernard Legube
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Anthropogenic Activities ◽  
Surface Water Quality ◽  
Principal Component ◽  
Specific Weight ◽  
Quality Parameters ◽  
Primary Objective ◽  
Fluorescence Excitation ◽  
Fluorescence Characteristics

The primary objective of this study is to explore a water quality database on two Mediterranean rivers (the Kadisha-Abou Ali and El Jaouz rivers—located in north Lebanon), considering their physicochemical, microbiological and fluorescence characteristics. Principal Component Analysis (PCA) was applied to the matrix gathering physicochemical and microbiological data while the Common Components and Specific Weight Analysis (CCSWA) or ComDim was used for fluorescence excitation-emission matrices (EEMs). This approach provided complementary and valuable information regarding water quality in such complex ecosystem. As highlighted by the PCA and ComDim scores, the Kadisha-Abou Ali River is highly influenced by anthropogenic activities because its watershed districts are intensively populated. This influence reveals the implication of organic and bacteriological parameters. To the contrary, the El Jaouz watershed is less inhabited and is characterized by mineral parameters, which determines its water quality. This work highlighted the relationship between fluorescence EEMs and major water quality parameters, enabling the selection of reliable water quality indicators for the studied rivers. The proposed methodology can surely be generalized to the monitoring of surface water quality in other rivers. Each customized water quality fingerprint should constantly be inspected in order to account for any emerging pollution.

scholarly journals A Spatial Analysis to Define Data Requirements for Hydrological and Water Quality Models in Data-Limited Regions

Water ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 267 ◽  
Author(s):  
Ersilia D’Ambrosio ◽  
Anna De Girolamo ◽  
Marinella Spanò ◽  
Vera Corbelli ◽  
Gennaro Capasso ◽  
...  
Keyword(s):  
Water Quality ◽  
Spatial Analysis ◽  
Surface Water ◽  
Source Parameters ◽  
Quality Parameters ◽  
Point Sources ◽  
Water Quality Parameters ◽  
Limited Area ◽  
Climatic Data ◽  
Basin Scale

The objective of the present work is a spatial analysis aimed at supporting hydrological and water quality model applications in the Canale d’Aiedda basin (Puglia, Italy), a data-limited area. The basin is part of the sensitive environmental area of Taranto that requires remediation of the soil, subsoil, surface water, and groundwater. A monitoring plan was defined to record the streamflow and water quality parameters needed for calibrating and validating models, and a database archived in a GIS environment was built, which includes climatic data, soil hydraulic parameters, groundwater data, surface water quality parameters, point-source parameters, and information on agricultural practices. Based on a one-year monitoring of activities, the average annual loads of N-NO3 and P-PO4 delivered to the Mar Piccolo amounted to about 42 t year−1, and 2 t year−1, respectively. Knowledge uncertainty in monthly load estimation was found to be up to 25% for N-NO3 and 40% for P-PO4. The contributions of point sources in terms of N-NO3 and P-PO4 were estimated at 45% and 77%, respectively. This study defines a procedure for supporting modelling activities at the basin scale for data-limited regions.

Impact of Further Impoundments on the Oxygen Balance and Water Quality of the Danube in Germany

1990 ◽  
Vol 22 (5) ◽  
pp. 69-78 ◽  
Author(s):  
D. Müller ◽  
V. Kirchesch
Keyword(s):  
Water Quality ◽  
Quality Parameters ◽  
Nitrifying Bacteria ◽  
Oxygen Balance ◽  
Quality Model ◽  
Model Calculations ◽  
River Bed ◽  
The Impact ◽  
The Mathematical Model

The construction of two or three impounding dams in the remaining freely flowing reach (73 km) of the Danube is under discussion. The purpose of these impoundments is to guarantee a minimum navigable depth of 3 m needed for modern cargo ships and to produce electric power. The impact of these developments is discussed on the basis of experience with similar impoundments further upstream and of the results from water quality model calculations. The mathematical model used is of the deterministic type, calculating the growth of slowly-growing organisms (nitrifying bacteria, algae and zooplanktons) according to MONOD and MICHAELIS-MENTEN. Compared with impoundments on other German rivers or the Iron Gate impoundments on the Danube, the effect of the impoundments under discussion on water quality parameters is likely to be fairly small, reflecting the slight changes in morphology which would be necessary for attaining the water depth required. Therefore, the more important effects of these developments would be the changes in the ecologic situation at the river bed and near the banks of the river.

scholarly journals Integration Multi-Model to Evaluate the Impact of Surface Water Quality on City Sustainability: A Case from Maanshan City in China

Processes ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 25 ◽  
Author(s):  
Zhanbo Chen ◽  
Hui Zhang ◽  
Mingxia Liao
Keyword(s):  
Water Pollution ◽  
Water Quality ◽  
Surface Water ◽  
Evaluation Method ◽  
Multiple Criteria Decision Making ◽  
Surface Water Quality ◽  
Policy Makers ◽  
Proposed Model ◽  
Evaluation Problem ◽  
The Impact

Water pollution is a worldwide problem that needs to be solved urgently and has a significant impact on the efficiency of sustainable cities. The evaluation of water pollution is a Multiple Criteria Decision-Making (MCDM) problem and using a MCDM model can help control water pollution and protect human health. However, different evaluation methods may obtain different results. How to effectively coordinate them to obtain a consensus result is the main aim of this work. The purpose of this article is to develop an ensemble learning evaluation method based on the concept of water quality to help policy-makers better evaluate surface water quality. A valid application is conducted to illustrate the use of the model for the surface water quality evaluation problem, thus demonstrating the effectiveness and feasibility of the proposed model.

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