Multilevel split of high-dimensional water quality data using artificial neural networks for the prediction of dissolved oxygen in the Danube River

2019 ◽  
Vol 32 (8) ◽  
pp. 3957-3966 ◽  
Author(s):  
Davor Antanasijević ◽  
Viktor Pocajt ◽  
Aleksandra Perić-Grujić ◽  
Mirjana Ristić
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Artificial Neural Networks ◽  
Dissolved Oxygen ◽  
Danube River ◽  
Quality Data ◽  
High Dimensional ◽  
Water Quality Data ◽  
The Danube River ◽  
Artificial Neural

scholarly journals Detecting Technical Anomalies in High-Frequency Water-Quality Data Using Artificial Neural Networks

2020 ◽  
Vol 54 (21) ◽  
pp. 13719-13730
Author(s):  
Javier Rodriguez-Perez ◽  
Catherine Leigh ◽  
Benoit Liquet ◽  
Claire Kermorvant ◽  
Erin Peterson ◽  
...  
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Artificial Neural Networks ◽  
High Frequency ◽  
Quality Data ◽  
Water Quality Data ◽  
Artificial Neural

Modelling of dissolved oxygen content using artificial neural networks: Danube River, North Serbia, case study

2013 ◽  
Vol 20 (12) ◽  
pp. 9006-9013 ◽  
Author(s):  
Davor Antanasijević ◽  
Viktor Pocajt ◽  
Dragan Povrenović ◽  
Aleksandra Perić-Grujić ◽  
Mirjana Ristić
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Dissolved Oxygen ◽  
Oxygen Content ◽  
Danube River ◽  
Artificial Neural

Large-scale Groundwater Simulation using Artificial Neural Networks in the Danube River Basin

2020 ◽  
Author(s):  
Illias Landros ◽  
Ioannis Trichakis ◽  
Emmanouil Varouchakis ◽  
George P. Karatzas
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
River Basin ◽  
Danube River ◽  
Bayesian Regularization ◽  
Data Set ◽  
The Danube River ◽  
Precipitation Temperature ◽  
Artificial Neural ◽  
Danube River Basin

<p>In recent years, Artificial Neural Networks (ANNs) have proven their merit in being able to simulate the changes in groundwater levels, using as inputs other parameters of the water budget, e.g. precipitation, temperature, etc.. In this study, ANNs have been used to simulate hydraulic head in a large number of wells throughout the Danube River Basin, taking as inputs, precipitation, temperature, and evapotranspiration data in the region. Different ANN architectures have been examined, to minimize the simulation error of the testing data-set. Among the different training algorithms, Levenberg-Marquardt and Bayesian Regularization are used to train the ANNs, while the different activation functions of the neurons that were deployed include tangent sigmoid, logarithmic sigmoid and linear. The initial application comprised of data from 128 wells between 1 January 2000 and 31 October 2014. The best performance was achieved by the algorithm Bayesian Regularization with a error of the order  based on all observation wells. A second application, compared the results of the first one, with the results of an ANN used to simulate a single well. The pros and cons of the two approaches, and the synergies of using both of them is further discussed in order to distinguish the differences, and guide researchers in the field for further applications.</p>

scholarly journals Multivariate Analysis of Water Quality Measurements on the Danube River

Water ◽  
2021 ◽  
Vol 13 (24) ◽  
pp. 3634
Author(s):  
Zoltan Horvat ◽  
Mirjana Horvat ◽  
Kristian Pastor ◽  
Vojislava Bursić ◽  
Nikola Puvača
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Multivariate Analysis ◽  
Cross Sections ◽  
Principal Component ◽  
Component Analysis ◽  
Danube River ◽  
Quality Data ◽  
Water Quality Data ◽  
The Danube River

This study investigates the potential of using principal component analysis and other multivariate analysis techniques to evaluate water quality data gathered from natural watercourses. With this goal in mind, a comprehensive water quality data set was used for the analysis, gathered on a reach of the Danube River in 2011. The considered measurements included physical, chemical, and biological parameters. The data were collected within seven data ranges (cross-sections) of the Danube River. Each cross-section had five verticals, each of which had five sampling points distributed over the water column. The gathered water quality data was then subjected to several multivariate analysis techniques. However, the most attention was attributed to the principal component analysis since it can provide an insight into possible grouping tendencies within verticals, cross-sections, or the entire considered reach. It has been concluded that there is no stratification in any of the analyzed water columns. However, there was an unambiguous clustering of sampling points with respect to their cross-sections. Even though one can attribute these phenomena to the unsteady flow in rivers, additional considerations suggest that the position of a cross-section can have a significant impact on the measured water quality parameters. Furthermore, the presented results indicate that these measurements, combined with several multivariate analysis methods, especially the principal component analysis, may be a promising approach for investigating the water quality tendencies of alluvial rivers.

Identification of nitrate sources, hot spots, and dilution in the Danube River Basin using a multitracer approach

2020 ◽  
Author(s):  
Janine Halder ◽  
Yuliya Vystavna ◽  
Cedric Douence ◽  
Christian Resch ◽  
Roman Gruber ◽  
...  
Keyword(s):  
Water Quality ◽  
Stable Isotopes ◽  
River Basin ◽  
Danube River ◽  
Quality Data ◽  
The Black Sea ◽  
Main Stream ◽  
Water Quality Data ◽  
The Danube River ◽  
Danube River Basin

<p>The Danube is Europe`s second longest river, stretching from Germany to the Black Sea. Water quality in the Danube River Basin is regularly monitored by the national authorities of all riparian countries and in addition for specific water quality data during the Joint Danube Surveys (JSD), which is organised by the International Commission for the Protection of the Danube River every 6 years.</p><p>This study presents the results of water stable isotopes and stable isotopes (<sup>15</sup>N and <sup>18</sup>O) of nitrate as well as major ion analysis from 3 JDS (2001, 2007, 2019). Results indicate that water stable isotopes allow to trace differences in the amount of snowmelt contribution to the Danube and hence the dilution effects of pollutants e.g. nitrate. The oxygen and nitrogen isotope compositions of nitrate are clearly indicating that nitrate in the Danube main stream mainly derives from waste water effluents, which input is increasing along the stream. This can furthermore be confirmed by results of micropollutant studies that demonstrate an increase of widely consumed pharmaceuticals (carbamazepine, diclofenac and caffeine) at different sections of the Danube River affected by tributary inflows and discharge from urban settlements.</p><p>In summary, this study is an example of combining isotope techniques, hydrological methods but also emerging compounds in order to approach the fate of anthropogenically derived nitrate within the Danube Basin. The results of this study aim to support the 2021 update of the Danube River Basin Management Plan as well as water monitoring practices across the Danube countries.</p>

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