An Investigation on Water Quality of Darlik Dam Drinking Water using Satellite Images

Alparslan, Erhan; Coskun, H. Gonca; Algancı, Uğur

doi:10.1100/tsw.2010.125

Cited by 10 publications

(6 citation statements)

References 26 publications

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“…The region has rapid population growth and industrial development, which increase the mass of sewage discharge. With an increase in population, surface water quality needs to be monitored continuously in order to take measures, when necessary to sustain the portability of the surface water resources [ 20 ]. Jakara Basin is located on longitude 8°31′E to 8°45′ and latitude 12°10′N and 12°13′N.…”

Section: Introductionmentioning

confidence: 99%

Temporal Aspects of Surface Water Quality Variation Using Robust Statistical Tools

Mustapha

Aris

Ramli

et al. 2012

The Scientific World Journal

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Robust statistical tools were applied on the water quality datasets with the aim of determining the most significance parameters and their contribution towards temporal water quality variation. Surface water samples were collected from four different sampling points during dry and wet seasons and analyzed for their physicochemical constituents. Discriminant analysis (DA) provided better results with great discriminatory ability by using five parameters with (P < 0.05) for dry season affording more than 96% correct assignation and used five and six parameters for forward and backward stepwise in wet season data with P-value (P < 0.05) affording 68.20% and 82%, respectively. Partial correlation results revealed that there are strong (r p = 0.829) and moderate (r p = 0.614) relationships between five-day biochemical oxygen demand (BOD5) and chemical oxygen demand (COD), total solids (TS) and dissolved solids (DS) controlling for the linear effect of nitrogen in the form of ammonia (NH3) and conductivity for dry and wet seasons, respectively. Multiple linear regression identified the contribution of each variable with significant values r = 0.988, R 2 = 0.976 and r = 0.970, R 2 = 0.942 (P < 0.05) for dry and wet seasons, respectively. Repeated measure t-test confirmed that the surface water quality varies significantly between the seasons with significant value P < 0.05.

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Section: Introductionmentioning

confidence: 99%

Temporal Aspects of Surface Water Quality Variation Using Robust Statistical Tools

Mustapha

Aris

Ramli

et al. 2012

The Scientific World Journal

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“…The prototype model was initially developed to a miniaturized dam in a test tube, and thereby physical constraints were not addressed. However, the geotechnical features of the dam also have to be studied (Alparslan et al 2010;Koszelnik et al 2018). The flow of water in the dam depends on the sediment bed, density of water and, volume of the reservoir.…”

Section: Resultsmentioning

confidence: 99%

“…The constraints in selecting an LED with a particular wavelength limit the usage of such systems, irrespective of the advantages. Apart from the wireless technologies, remote sensing and microwave sensing also paved the way for efficient monitoring online than manual measurements (Alparslan et al 2010;Bresciani et al 2019;Meriem et al 2019;Zhang et al 2019). Gholizadeh et al (2016) reviews several strategies of measurements and gives an insight into various methods adopted considering the cost, flexibility, and complexity of the sensor development, its usage, and interpretation.…”

Section: Graphical Abstract Introductionmentioning

confidence: 99%

Integration of sensors for dam water quality analysis – a prototype

Rose

Mary

Karthik

2021

Water Science and Technology

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Water consumed is stored in several water bodies in and around us, out of which dams accommodate a major portion of water. The quantity and quality monitoring of water in Dams is troublesome due to its large surface area and high depths. Though groundwater resources are the primary water source in India, Dams plays a vital role in water distribution and storage network. Central Water Commission in India has identified more than 5,000 dams of which a major portion is persistently consumed by the rural and urban population for drinking and irrigation. The water quality of these reservoirs is of serious concern as it would not only affect the socio-economic status of the nation but the aquatic systems as well. Water quality control and management are vital for delivering clean water supply to the common society. Because of their size, collecting, assessing, and managing a vast volume of water quality data is critical. Water quality data is primarily obtained through manual field sampling; however, real-time sensor monitoring is increasingly being used for more efficient data collection. The literature depicts that the methodsinvolving remote sensing and image processing of water quality analysis consume time, require sample collection at various depths, analysis of collected samples, and manual interpretations. The objective of this study is to propose a novel cost-effective method to monitor water quality devoid of considerable human intervention. The sensor-based online monitoring aids in assessing the sample with limited technology, at various depths of water in the dam to analyze turbidity which gives the major indication of pure water. The quality analysis of the dam water is worthy if the water is assessed at the distribution end before consumption. Hence, to enhance the water management system, other quality parameters like pH, conductivity, temperature are sensed and monitored in the distribution pipeline. The unstable pH can alter the chemical and microbiological aspects of water resulting in a variation of other water quality parameters Temperature variations affect the amount of dissolved oxygen in the water bodies which results in unstable quality parameters. The change in dissolved solvents and the ionic concentration alters the electrical conductivity of the water and the increased concentration of salts also results in turbidity. The data from all the sensors are processed by the microcontroller, transmitted, and displayed in a mobile application comprehensible to the layman.

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“…The direct result of multicollinearity is that the standard error of coefficients of explanatory variables is inflated, which means that coefficients of the derived model are not reliable. Unfortunately, many past studies neither discuss the issue of multicollinearity, nor provide results of validation of the derived regression models [ 4 , 5 , 11 , 19 , 28 , 29 , 30 , 31 ]. A common way to identify multicollinearity of a model is through the use of indicators such as Akaike’s Information Criteria [ 32 ], Mallow’s Cp [ 33 ], PRESS [ 34 ], etc.…”

Section: Introductionmentioning

confidence: 99%

Hybrid forward-selection method-based water-quality estimation via combining Landsat TM, ETM+, and OLI/TIRS images and ancillary environmental data

Smith

Filippi

2018

PLoS ONE

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A simple approach to enable water-management agencies employing free data to create a single set of water quality predictive equations with satisfactory accuracy is proposed. Multiple regression-derived equations based on surface reflectance, band ratios, and environmental factors as predictor variables for concentrations of Total Suspended Solids (TSS) and Total Nitrogen (TN) were derived using a hybrid forward-selection method that considers both p-value and Variance Inflation Factor (VIF) in the forward-selection process. Landsat TM, ETM+, and OLI/TIRS images were jointly utilized with environmental factors, such as wind speed and water surface temperature, to derive the single set of equations. Through splitting data into calibration and validation groups, the coefficients of determination are 0.73 for TSS calibration and 0.70 for TSS validation, respectively. The coefficients of determination for TN calibration and validation are 0.64 and 0.37, respectively. Among all chosen predictor variables, ratio of reflectance of visible red (Band 3 for Landsat TM and ETM+, or Band 4 for Landsat OLI/TIRS) to visible blue (Band 1 for Landsat TM and ETM+, or Band 2 for Landsat OLI/TIRS) has a strong influence on the predictive power for TSS retrieval. Environmental factors including wind speed, remote sensing-derived water surface temperature, and time difference (in days) between the image acquisition and water sampling were found to be important in water-quality quantity estimation. The hybrid forward-selection method consistently yielded higher validation accuracy than that of the conventional forward-selection approach.

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An Investigation on Water Quality of Darlik Dam Drinking Water using Satellite Images

Cited by 10 publications

References 26 publications

Temporal Aspects of Surface Water Quality Variation Using Robust Statistical Tools

Temporal Aspects of Surface Water Quality Variation Using Robust Statistical Tools

Integration of sensors for dam water quality analysis – a prototype

Hybrid forward-selection method-based water-quality estimation via combining Landsat TM, ETM+, and OLI/TIRS images and ancillary environmental data

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