Application the Grey Neural Network to Evaluate the Water Quality of the Yangtze Estuary Wetland

2014 ◽  
Vol 971-973 ◽  
pp. 2180-2185
Author(s):  
Sheng Long Yang
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Sea Water ◽  
Comprehensive Evaluation ◽  
Sampling Site ◽  
Predictive Ability ◽  
Yangtze Estuary ◽  
Similar Data ◽  
Data Set

Based on the Grey neural network, combine with the sampling data from Yangtze estuary wetland which measured in fifteen sampling site in raising tide and falling tide in May 2010 to intelligent comprehensive evaluation the sea water quality of Yangtze estuary wetland. The results showed that the sea water quality of sampling data wereⅠ.The precision of training and testing data set showed the Grey neural network had good generalization capacity, with good fitting precision and strongly predictive ability. It can be used to similar data set calculation.

Long-Term and Seasonal Aspects of the Water Quality of the River Danube within the Region of Vienna (Austria)

1990 ◽  
Vol 22 (5) ◽  
pp. 51-58 ◽  
Author(s):  
Ch Ludwig ◽  
H. Ranner ◽  
G. Kavka ◽  
W. Kohi ◽  
U. Humpesch
Keyword(s):  
Water Quality ◽  
Data Collection ◽  
Catchment Area ◽  
Sampling Site ◽  
Seasonal Fluctuations ◽  
Data Set ◽  
River Danube ◽  
Seasonal Aspects

Data on water quality variables from 1968 to 1987 are analyzed statistically. The long-terra changes of five selected variables for the section of the River Danube at Vienna are investigated at four different sampling sites, two upstream and two downstream from Vienna. The influence of the efforts made to reduce wastewater inputs within the catchment area at Vienna were examined. Another objective was to obtain information about seasonal fluctuations at one selected sampling site. The quality of the data set is discussed with regard to the practical applicability of the results and suggestions are given for data collection.

scholarly journals Assessment of Sea Water Quality in some Limestone Island and Archipelagos areas, Viet Nam

2020 ◽  
Vol 36 (1) ◽  
Author(s):  
Cao Thi Thu Trang ◽  
Do Cong Thung ◽  
Le Van Nam ◽  
Pham Thi Kha ◽  
Nguyen Van Bach ◽  
...  
Keyword(s):  
Water Quality ◽  
Sea Water ◽  
Volcanic Rocks ◽  
Marine Biodiversity ◽  
Viet Nam ◽  
Data Set ◽  
Aquatic Life ◽  
Research Results ◽  
Gulf Of Tonkin

Abstract: With nearly 3,000 large and small islands, the islands and archipelagos of Vietnam have outstanding features in terms biodiversity and geology. The islands are mainly formed from carbonate (limestone), intrusive igneous rock, sedimentary and volcanic rocks, in which limestone islands predominate, distributed mainly in the Gulf of Tonkin. This paper presents the results of researches and assessments on sea water quality of Vietnam's typical limestone islands and archipelagoes through the 2017-2018 surveys. The research results show that although the water quality around of limestone and archipelago area of Viet Nam is safety for the development of aquatic life, an increase in pollutants concentration in water has been recorded when compared to previous research results. The research results supplement the data set of sea water quality in limestone areas that defining the characteristics of marine biodiversity. Keywords: limestone island, water quality, pollutants.

Classification and Prediction of Goldfish Population and Water Quality Using a Potentiometric E-Tongue

2017 ◽  
Vol 60 (4) ◽  
pp. 1037-1044
Author(s):  
Zhenbo Wei ◽  
Yu Zhao ◽  
Jun Wang
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Pattern Recognition ◽  
Comprehensive Evaluation ◽  
Back Propagation ◽  
Correlation Coefficients ◽  
Principal Component ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Pattern Recognition Methods

Abstract. In this study, a potentiometric E-tongue was employed for comprehensive evaluation of water quality and goldfish population with the help of pattern recognition methods. Four water quality parameters, i.e., pH and concentrations of dissolved oxygen (DO), nitrite (NO2-N), and ammonium (NH3-N), were tested by conventional analysis methods. The differences in water quality parameters between samples were revealed by two-way analysis of variance (ANOVA). The cultivation days and goldfish population were classified well by principal component analysis (PCA) and canonical discriminant analysis (CDA), and the distribution of each sample was clearer in CDA score plots than in PCA score plots. The cultivation days, goldfish population, and water parameters were predicted by a T-S fuzzy neural network (TSFNN) and back-propagation artificial neural network (BPANN). BPANN performed better than TSFNN in the prediction, and all fitting correlation coefficients were >0.90. The results indicated that the potentiometric E-tongue coupled with pattern recognition methods could be applied as a rapid method for the determination and evaluation of water quality and goldfish population. Keywords: Classify, E-tongue, Goldfish water, Prediction.

Legal aspects of sea water quality: Turkish and EU directives

2005 ◽  
Vol 51 (11) ◽  
pp. 45-52
Author(s):  
D. Dolgen
Keyword(s):  
Water Quality ◽  
Sea Water ◽  
Legal Aspects ◽  
Coastal Environment ◽  
Bathing Water ◽  
Eu Directives ◽  
Laws And Regulations

This paper primarily addresses underlying laws and regulations related to protection of the coastal environment and examines their implementation. In this context, Directive 76/160/EEC which is the leading directive on the quality of bathing water and its revision, i.e. Commission Proposal COM (2000) 860 Final, is investigated particularly and criticized on technical and scientific manner; and similar and dissimilar issues between the Community Directives and Turkish Laws are evaluated. The conducted study reveals that although the Turkish legislation in the field of water quality is largely in line with the acquis, further efforts are also needed with respect to implementation in order to achieve full harmonization.

A framework of characteristics identification and source apportionment of water pollution in a river: a case study in the Jinjiang River, China

2012 ◽  
Vol 65 (11) ◽  
pp. 2071-2078 ◽  
Author(s):  
Haiyang Chen ◽  
Yanguo Teng ◽  
Jinsheng Wang
Keyword(s):  
Water Pollution ◽  
Water Quality ◽  
Source Apportionment ◽  
Comprehensive Evaluation ◽  
Principal Component ◽  
Anthropogenic Pollution ◽  
Ammonia Nitrogen ◽  
Support Vector ◽  
Jinjiang River

A framework for characteristics identification and source apportionment of water pollution in the Jinjiang River of China was proposed in this study for evaluation. A total of 114 water samples which were generated between May 2009 and September 2010 at 13 sites were collected and analysed. First, support vector machine (SVM) and water quality pollutant index (WQPI) were used for water quality comprehensive evaluation and identifying characteristic contaminants. Later, factor analysis with nonnegative constraints (FA-NNC) was employed for source apportionment. Finally, multi-linear regression of the absolute principal component score (APCS/MLR) was applied to further estimate source contributions for each characteristic contaminant. The results indicated that the water quality of the Jinjiang River was mainly at the third level (65.79%) based on national surface water quality permissible standards in China. Ammonia nitrogen, total phosphorus, mercury, iron and manganese were identified as characteristic contaminants. Source apportionment results showed that industrial activities (63.16%), agricultural non-point source (16.50%) and domestic sewage (12.85%) were the main anthropogenic pollution sources which were influencing the water quality of Jinjiang River. This proposed method provided a helpful framework for conducting water pollution management in aquatic environment.

scholarly journals The Irminger Sea and the Iceland Sea time series measurements of sea water carbon and nutrient chemistry 1983–2008

2010 ◽  
Vol 2 (1) ◽  
pp. 99-104 ◽  
Author(s):  
J. Olafsson ◽  
S. R. Olafsdottir ◽  
A. Benoit-Cattin ◽  
T. Takahashi
Keyword(s):  
Time Series ◽  
Surface Waters ◽  
Sea Water ◽  
Dissolved Inorganic Carbon ◽  
Data Set ◽  
Carbon Chemistry ◽  
Irminger Sea ◽  
Nutrient Chemistry ◽  
Seawater Pco2

Abstract. This paper describes the ways and means of assembling and quality controling the Irminger Sea and Iceland Sea time-series biogeochemical data which are included in the CARINA data set. The Irminger Sea and the Iceland Sea are hydrographically different regions where measurements of sea water carbon and nutrient chemistry were started in 1983. The sampling is seasonal, four times a year. The carbon chemistry is studied with measurements of the partial pressure of carbon dioxide in seawater, pCO2, and total dissolved inorganic carbon, TCO2. The carbon chemistry data are for surface waters only until 1991 when water column sampling was initiated. Other measured parameters are salinity, dissolved oxygen and the inorganic nutrients nitrate, phosphate and silicate. Because of the CARINA criteria for secondary quality control, depth >1500 m, the IRM-TS could not be included in the routine QC and the IS-TS only in a limited way. However, with the information provided here, the quality of the data can be assessed, e.g. on the basis of the results obtained with the use of reference materials.

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