Prediction of the Seasonal Changes of the Chloride Concentrations in Urban Water Reservoir

Miller, Tymoteusz; Poleszczuk, G.

doi:10.1515/eces-2017-0039

Cited by 4 publications

(3 citation statements)

References 47 publications

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“…The storms a form of extreme phenomena that end periods of drought will cause the outflow of nutrients from urban areas and generate patches of acidification of surface waters [13]. Do not forget about the pollution generated directly by man, even the increase in chloride concentrations in rivers resulting from the salting of roads in winter or through the inflow of industrial or living pollution [14].…”

Section: Discussionmentioning

confidence: 99%

The Impact of Hydrometeorological Events on the State of the Urban River Quality

Pietruszyński

Cieśliński

2019

Ecological Chemistry and Engineering S

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One of the aims of this paper was to study catchment and watercourse response to extreme events. The study area consisted of Orunski Stream in the northern Polish city of Gdansk. The study period (review of cartographic materials) covered the years 1908 to 2000, while field research covered the period from May 2009 to April 2010. In addition, measurements were performed to study the dynamics of changes occurring in the creek during rainfall events. Examination of the water chemistry changes made two days before heavy rainfall showed a significant increase in sulphates and nitrogen compounds. Additionally, pollutants washed from the basin contributed to a significant deterioration in the water colour. A rainfall of 1.3 mm caused an eleven-fold increase in stream flow, a twofold increase in the water level, and a fourfold increase in the velocity of water molecules. It might appear seem that such a small amount of rainfall would have no major effect on the hydrologic and hydrochemical parameters of a small creek. However, it is the maximum flow rate that is an important variable when assessing potential flood risk levels.

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Section: Discussionmentioning

confidence: 99%

The Impact of Hydrometeorological Events on the State of the Urban River Quality

Pietruszyński

Cieśliński

2019

Ecological Chemistry and Engineering S

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“…They pointed out that the limited data set was one of the drawbacks of their research and encouraged others to collect more data to recalibrate and revalidate the model. Wang et al [19] employed a typical three-layer of MLP structure [77][78][79][80][81][82][83][84][85][86][87][88][89] with the BP algorithm to achieve Chl-a prediction. They divided the dataset into training (75%) and testing parts (25%).…”

Section: Artificial Neural Network Models For Water Quality Predictionmentioning

confidence: 99%

A Review of the Artificial Neural Network Models for Water Quality Prediction

et al. 2020

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Water quality prediction plays an important role in environmental monitoring, ecosystem sustainability, and aquaculture. Traditional prediction methods cannot capture the nonlinear and non-stationarity of water quality well. In recent years, the rapid development of artificial neural networks (ANNs) has made them a hotspot in water quality prediction. We have conducted extensive investigation and analysis on ANN-based water quality prediction from three aspects, namely feedforward, recurrent, and hybrid architectures. Based on 151 papers published from 2008 to 2019, 23 types of water quality variables were highlighted. The variables were primarily collected by the sensor, followed by specialist experimental equipment, such as a UV-visible photometer, as there is no mature sensor for measurement at present. Five different output strategies, namely Univariate-Input-Itself-Output, Univariate-Input-Other-Output, Multivariate-Input-Other(multi), Multivariate-Input-Itself-Other-Output, and Multivariate-Input-Itself-Other (multi)-Output, are summarized. From results of the review, it can be concluded that the ANN models are capable of dealing with different modeling problems in rivers, lakes, reservoirs, wastewater treatment plants (WWTPs), groundwater, ponds, and streams. The results of many of the review articles are useful to researchers in prediction and similar fields. Several new architectures presented in the study, such as recurrent and hybrid structures, are able to improve the modeling quality of future development.

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“…The mean squared prediction error (MSE) is the sum of squared values of differences between the actual and predicted values [19]…”

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confidence: 99%

Geostatistical Methods in Water Distribution Network Design - A Case Study

Potyralla

2019

Ecological Chemistry and Engineering S

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Modeling of the loads of water supply networks and their subsequent forecasting is an element necessary for making optimum decisions in the process of planning the development and operation of the water supply networks. The results of this modeling are decisive for the selection of the diameters of the pipelines and their arrangement on the water demand area. This study presents the results of estimation of average values of loads for the selected investment variants. The aim of the article is to present the possibility of simulations and analyses of the geostatistical interpolation methods. Data input in the model regarded the fragment of the real water supply network administered by the Municipal Water and Sewerage Company in Warszawa. Results of the computer analyses for the presented investment variants were related to the operating data of the water supply network and the data on water demand for the years 2014-2017 and 2018-2025. The aim of this paper is to present the advantages of GIS for the water supply systems and to prove that using the appropriate IT system, with provision of proper data processing, may lead to decisions which are optimum in view of the established, often very complex criteria.

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Prediction of the Seasonal Changes of the Chloride Concentrations in Urban Water Reservoir

Cited by 4 publications

References 47 publications

The Impact of Hydrometeorological Events on the State of the Urban River Quality

The Impact of Hydrometeorological Events on the State of the Urban River Quality

A Review of the Artificial Neural Network Models for Water Quality Prediction

Geostatistical Methods in Water Distribution Network Design - A Case Study

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