scholarly journals Non-hierarchical grouping: ‘K-mean’ and ‘K-medoid’ of plaques cisterns in the Pajeu region - PE

2020 ◽  
Vol 42 ◽  
pp. e44378
Author(s):  
Manoel Rivelino Gomes de Oliveira ◽  
David Venâncio da Cruz ◽  
Moacyr Cunha Filho
Keyword(s):  
Water Quality ◽  
Hierarchical Clustering ◽  
Optimal Partition ◽  
Cluster Validation ◽  
Clustering Method ◽  
Homogeneous Groups ◽  
Water Quality Variables ◽  
Hierarchical Grouping ◽  
Validation Criterion

This work uses non-hierarchical grouping methods to evaluate the quality of the groups formed by plate cisterns according to some water quality variables. These methods use the cluster validation criterion to determine the optimal partition, which provides the most homogeneous groups possible. The methods were tested on a sample of 100 cisterns located in the Pajeú region. However, the non-hierarchical clustering method of ‘K-medoid’ formed more homogeneous groups, and thus the best performance according to the Silhouette [s (i) = 0.64] statistics.

scholarly journals Citizen Science Tools Reveal Changes in Estuarine Water Quality Following Demolition of Buildings

2021 ◽  
Vol 13 (9) ◽  
pp. 1683
Author(s):  
Nandini Menon ◽  
Grinson George ◽  
Rajamohananpillai Ranith ◽  
Velakandy Sajin ◽  
Shreya Murali ◽  
...  
Keyword(s):  
Water Quality ◽  
Dissolved Oxygen ◽  
Citizen Science ◽  
Secchi Depth ◽  
Lake Water Quality ◽  
Demolition Waste ◽  
Water Colour ◽  
Water Quality Variables ◽  
The Impact

Turbidity and water colour are two easily measurable properties used to monitor pollution. Here, we highlight the utility of a low-cost device—3D printed, hand-held Mini Secchi disk (3DMSD) with Forel-Ule (FU) colour scale sticker on its outer casing—in combination with a mobile phone application (‘TurbAqua’) that was provided to laymen for assessing the water quality of a shallow lake region after demolition of four high-rise buildings on the shores of the lake. The demolition of the buildings in January 2020 on the banks of a tropical estuary—Vembanad Lake (a Ramsar site) in southern India—for violation of Indian Coastal Regulation Zone norms created public uproar, owing to the consequences of subsequent air and water pollution. Measurements of Secchi depth and water colour using the 3DMSD along with measurements of other important water quality variables such as temperature, salinity, pH, and dissolved oxygen (DO) using portable instruments were taken for a duration of five weeks after the demolition to assess the changes in water quality. Paired t-test analyses of variations in water quality variables between the second week of demolition and consecutive weeks up to the fifth week showed that there were significant increases in pH, dissolved oxygen, and Secchi depth over time, i.e., the impact of demolition waste on the Vembanad Lake water quality was found to be relatively short-lived, with water clarity, colour, and DO returning to levels typical of that period of year within 4–5 weeks. With increasing duration after demolition, there was a general decrease in the FU colour index to 17 at most stations, but it did not drop to 15 or below, i.e., towards green or blue colour indicating clearer waters, during the sampling period. There was no significant change in salinity from the second week to the fifth week after demolition, suggesting little influence of other factors (e.g., precipitation or changes in tidal currents) on the inferred impact of demolition waste. Comparison with pre-demolition conditions in the previous year (2019) showed that the relative changes in DO, Secchi depth, and pH were very high in 2020, clearly depicting the impact of demolition waste on the water quality of the lake. Match-ups of the turbidity of the water column immediately before and after the demolition using Sentinel 2 data were in good agreement with the in situ data collected. Our study highlights the power of citizen science tools in monitoring lakes and managing water resources and articulates how these activities provide support to Sustainable Development Goal (SDG) targets on Health (Goal 3), Water quality (Goal 6), and Life under the water (Goal 14).

A semi-hierarchical clustering method for constructing knowledge trees from stackoverflow

2020 ◽  
pp. 016555152096103
Author(s):  
Chun-Hsiung Tseng ◽  
Jia-Rou Lin
Keyword(s):  
Hierarchical Clustering ◽  
Information Sources ◽  
Background Knowledge ◽  
Knowledge Map ◽  
Clustering Method ◽  
Know How ◽  
Hierarchical Relationship ◽  
Constructing Knowledge ◽  
Better Than

To help students learn how to programme, we have to give them a clear knowledge map and sufficient materials. Question-based websites, such as stackoverflow, are excellent information sources for this goal. However, for beginners, the process can be a little tricky since they may not know how to ask correct questions if they do not have sufficient background knowledge, and a knowledge tree is usually considered more helpful in such a scenario. In this research, a method to infer a knowledge tree automatically from the type of websites and to group documents based on the resulting knowledge tree is proposed. The proposed method mainly addresses two issues: first, the quality of tags cannot be guaranteed, and second, clustering-based methods usually generate the flat schema. The occurrence count and the co-occurrence ratio were used together to identify important tags. Then, an algorithm was developed to infer the hierarchical relationship between tags. Using these tags as centres, the clustering performance is better than applying k-means alone.

scholarly journals Teleconsultation demand classification and service analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Wenjia Chen ◽  
Jinlin Li
Keyword(s):  
Hierarchical Clustering ◽  
Cosine Similarity ◽  
Graph Representation ◽  
Clustering Methods ◽  
Clustering Method ◽  
Network Graph ◽  
Before And After ◽  
Hierarchical Clustering Methods ◽  
Demand Classification

Abstract Background To enhance teleconsultation management, demands can be classified into different patterns, and the service of each pattern demand can be improved. Methods For the effective teleconsultation classification, a novel ensemble hierarchical clustering method is proposed in this study. In the proposed method, individual clustering results are first obtained by different hierarchical clustering methods, and then ensembled by one-hot encoding, the calculation and division of cosine similarity, and network graph representation. In the built network graph about the high cosine similarity, the connected demand series can be categorized into one pattern. For verification, 43 teleconsultation demand series are used as sample data, and the efficiency and quality of teleconsultation services are respectively analyzed before and after the demand classification. Results The teleconsultation demands are classified into three categories, erratic, lumpy, and slow. Under the fixed strategies, the service analysis after demand classification reveals the deficiencies of teleconsultation services, but analysis before demand classification can’t. Conclusion The proposed ensemble hierarchical clustering method can effectively category teleconsultation demands, and the effective demand categorization can enhance teleconsultation management.

A fuzzy hybrid hierarchical clustering method with a new criterion able to find the optimal partition

2002 ◽  
Vol 128 (3) ◽  
pp. 323-338 ◽  
Author(s):  
Arnaud Devillez ◽  
Patrice Billaudel ◽  
Gérard Villermain Lecolier
Keyword(s):  
Hierarchical Clustering ◽  
Optimal Partition ◽  
Clustering Method ◽  
New Criterion

Multivariate analysis of the water quality of a set of Quebec lakes: finding watershed explicative factors of the acidification process

1991 ◽  
Vol 18 (4) ◽  
pp. 644-653
Author(s):  
M. Lachance ◽  
B. Bobée ◽  
G. de Marsily
Keyword(s):  
Water Quality ◽  
Multivariate Analysis ◽  
Spatial Variability ◽  
Correspondence Analysis ◽  
Multiple Correspondence Analysis ◽  
Hierarchical Classification ◽  
Water Quality Variables ◽  
Geographical Regions ◽  
Saguenay River

In this study, a new methodological approach is proposed to analyze the spatial variability of the water quality of a set of lakes. The Quebec region under study is located in the southeast part of the Canadian Shield between the Ottawa River and the Saguenay River. The proposed methodology is based on the combined use of correspondence analysis and hierarchical classification analysis. These methods provided the analyst with a geometric representation of the spatial variation of the physicochemical parameters of the water quality (pH, alkalinity, calcium + magnesium, sulfate) recognized as indicators of the acidification of aquatic ecosystems. Afterwards by imposing a contiguity constraint in hierarchical classification, the analysis led to the delimitation of five geographical regions that are similar with respect to the four variables related to the acidification process. Then the relations between the water quality variables and some biophysical variables are examined. The multiple correspondence analysis made it easier to identify the biophysical variables of the watershed that are explicative of the water quality variables. Amongst these explicative variables examined, we have identified that the sulfate concentration in the precipitation, the annual precipitation, the altitude of lakes, and the type of vegetation and geology are more or less explicative for the alkalinity, the pH, the Ca + Mg, and the sulfate. The results can be used to build a regression model for the prediction of some physicochemical variables from the knowledge of certain key variables describing the biophysical characteristics of the watersheds. Key words: spatial variability, multivariate analysis, acidification, water quality, explicative factors, lakes, Quebec.

scholarly journals Prediction of Water Quality in Riva River Watershed

2019 ◽  
Vol 26 (4) ◽  
pp. 727-742
Author(s):  
Nurtac Oz ◽  
Bayram Topal ◽  
Halil Ibrahim Uzun
Keyword(s):  
Water Quality ◽  
Decision Tree ◽  
Temporal Variations ◽  
The Black Sea ◽  
Marmara Region ◽  
Training Set ◽  
Logistic Regression Models ◽  
Water Quality Variables ◽  
Quality Of Water

Abstract The Riva River is a water basin located within the borders of Istanbul in the Marmara Region (Turkey) in the south-north direction. Water samples were taken for the 35 km drainage area of the Riva River Basin before the river flows into the Black Sea at 4 stations on the Riva River every month and analyses were carried out. Changes were observed in the quality of water from upstream to downstream. For this purpose, the spatial and temporal variations of water quality were investigated using 13 water quality variables with the ANOVA test. It was observed that COD, DO, S and BOD were important in determining the spatial variation. On the other hand, it was found out that all the variables were effective in determining the temporal variation. Moreover, the correlation analysis which was carried out in order to assess the relations between water quality variables showed that the variables of BOD-COD, BOD-EC, COD-EC, BOD-T and COD-T were correlated and the regression analysis showed that COD, TKN and NH4-N explained BOD and BOD, NH4-N, T and TSS explained COD by approximately 80 %. Consequently, the Artificial Neural Network (ANN), Decision Tree and Logistic Regression models were developed using the data of training set in order to predict the water quality classes of the variables of COD, BOD and NH4-N. Quality classes were predicted for the variables by inputting the data of testing set into the developed models. According to these results, it was seen that the ANN was the best prediction model for COD, the Decision Tree for BOD and the ANN and Decision Tree for NH4-N.

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