A Spatially Weighted Neural Network Based Water Quality Assessment Method for Large-Scale Coastal Areas

2021 ◽  
Vol 55 (4) ◽  
pp. 2553-2563
Author(s):  
Zhenhong Du ◽  
Jin Qi ◽  
Sensen Wu ◽  
Feng Zhang ◽  
Renyi Liu
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Quality Assessment ◽  
Large Scale ◽  
Water Quality Assessment ◽  
Assessment Method ◽  
Coastal Areas ◽  
Quality Assessment Method

Water Quality Evaluation Based on Multiclass Fuzzy Support Vector Machine

2010 ◽  
Vol 113-116 ◽  
pp. 708-711 ◽  
Author(s):  
Wei Guo Zhao ◽  
Li Ying Wang
Keyword(s):  
Water Quality ◽  
Support Vector Machine ◽  
Quality Assessment ◽  
Water Quality Assessment ◽  
Training Model ◽  
Assessment Method ◽  
Complex Problem ◽  
Support Vector ◽  
Fuzzy Support Vector Machine ◽  
Quality Assessment Method

It has been a more complex problem for water quality assessment. And its aim is to well and truly evaluate its degree of pollution for bodies of water, which will be easy to provide some principled projects and criterions for water resource’s protection and their integration application. So, a water quality assessment method based on Multiclass Fuzzy Support Vector Machine is put forward. and a two-step cross-validation was used to search for the best combination of parameters to obtain an optimal training model. The test results show that the method proposed in this paper has an excellent performance on correct ratio compared to BP. It indicated that the performance of the proposed model is practically feasible in the application of water quality assessment.

scholarly journals A Comprehensive Method for Water Environment Assessment considering Trends of Water Quality

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Sheng Bi ◽  
Li Wang ◽  
Yongrong Li ◽  
Zhenping Zhang ◽  
Zhimian Wang ◽  
...  
Keyword(s):  
Water Quality ◽  
Quality Assessment ◽  
Temporal Trends ◽  
Rank Correlation ◽  
Pollution Index ◽  
Principal Component ◽  
Water Quality Assessment ◽  
Assessment Method ◽  
Danjiangkou Reservoir ◽  
Quality Assessment Method

Water quality is a significant issue, and its assessment plays an important role in environmental management and pollution control. In this paper, we proposed a comprehensive water quality assessment method which takes into account both absolute and temporal trends in water quality. As the first step, we derived and applied a comprehensive pollution index (CPI) to characterize water pollution in 16 major tributaries to the Danjiangkou Reservoir, located in the upper reaches of the Hanjiang River in China. Next, we used Spearman’s rank correlation analysis to quantify temporal CPI trends in each tributary. As the final step, we conducted principal component analysis (PCA) using data on 8 water quality parameters and the temporal CPI trend from each of the 16 tributaries. The resultant comprehensive water quality assessment method identified tributaries, which stand to improve and threaten water quality in the Danjiangkou Reservoir from both immediate and future perspectives.

Taxonomy, ultrastructure, and distribution of some gomphonemoid diatoms (Bacillariophyceae: Gomphonemataceae) from rivers in the United States

1999 ◽  
Vol 77 (5) ◽  
pp. 686-705 ◽  
Author(s):  
J P Kociolek ◽  
J C Kingston
Keyword(s):  
Water Quality ◽  
Quality Assessment ◽  
North American ◽  
Large Scale ◽  
Taxonomic Status ◽  
Water Quality Assessment ◽  
The United States ◽  
Great Promise ◽  
Continental Scale ◽  
National Water

Using the continental-scale collections of the U.S. Geological Survey's National Water-Quality Assessment (NAWQA) Program, we examined selected members of the family Gomphonemataceae to expand the current state of knowledge of diatom taxonomy, morphology, and distribution. Ten taxa in the genera Gomphonema, Gomphoneis, and Gomphosphenia are examined. The taxonomic status of two taxa is revised, and one new species is described. Two new features are described for the genus Gomphonema: slit-like areolae and ridge-like flaps or flanges on pseudosepta. Many North American gomphonemoid species appear to be restricted to certain geographic regions; the differences between western and eastern North America are striking. Trained analysts have had difficulty identifying and discriminating many of these taxa. We believe that this difficulty, in large part, results from our poor knowledge of the North American flora. Large-scale monitoring programs such as NAWQA, when teamed up with research organizations with common interests, hold great promise to expand our knowledge of the biodiversity of North American ecosystems.Key words: biogeography, Gomphoneis, Gomphonema, Gomphosphenia, National Water-Quality Assessment (NAWQA) Program, taxonomy; ultrastructure.

scholarly journals Improving Water Quality Assessment through Anomaly Detection Using Hybrid Convolutional Neural Network Approach

2021 ◽  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Water Quality ◽  
Deep Learning ◽  
Anomaly Detection ◽  
Convolutional Neural Network ◽  
Quality Assessment ◽  
Water Quality Assessment ◽  
Machine Learning Algorithms ◽  
Quality Analysis

<p>Water being a precious commodity for every person around the world needs to be quality monitored continuously for ensuring safety whilst usage. The water data collected from sensors in water plants are used for water quality assessment. The anomaly present in the water data seriously affects the performance of water quality assessment. Hence it needs to be addressed. In this regard, water data collected from sensors have been subjected to various anomaly detection approaches guided by Machine Learning (ML) and Deep Learning framework. Standard machine learning algorithms have been used extensively in water quality analysis and these algorithms in general converge quickly. Considering the fact that manual feature selection has to be done for ML algorithms, Deep Learning (DL) algorithm is proposed which involve implicit feature learning. A hybrid model is formulated that takes advantage of both and presented it is data invariant too. This novel Hybrid Convolutional Neural Network (CNN) and Extreme Learning Machine (ELM) approach is used to detect presence of anomalies in sensor collected water data. The experiment of the proposed CNN-ELM model is carried out using the publicly available dataset GECCO 2019. The findings proved that the model has improved the water quality assessment of the sensor water data collected by detecting the anomalies efficiently and achieves F1 score of 0.92. This model can be implemented in water quality assessment.</p>

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