Clustering spatio–seasonal hydrogeochemical data using self-organizing maps for groundwater quality assessment in the Red River Delta, Vietnam

Nguyen, Thuy Thanh; Kawamura, Akira; Tong, Thanh Ngoc; Nakagawa, Naoko; Amaguchi, Hideo; Gilbuena, Romeo

doi:10.1016/j.jhydrol.2015.01.023

Cited by 143 publications

(66 citation statements)

References 31 publications

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“…Nowadays, the latter is the most widely used [24,25] because it provides a denser connection between neurons, a fact that provides more interactions between them during competition. There is not an "a priori" rule which can provide the best network structure and different ones must be tested in order to find that which performs the best.…”

Section: Self-organizing Map (Som)mentioning

confidence: 99%

“…So, many authors have used this tool to analyze wastewater treatment processes [20][21][22][23]. It has been also used to analyze other water treatment processes, such as that of drinking water [16], or to study the water quality of rivers [17,[24][25][26]. They all analyze the water properties by studying both the component planes and the SOM output, providing information about treatment process dynamics or the water properties.…”

Section: Introductionmentioning

confidence: 99%

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Obtaining Key Parameters and Working Conditions of Wastewater Biological Nutrient Removal by Means of Artificial Intelligence Tools

Vega

Jaramillo-Morán

2018

Water

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Abstract:The oxidation-reduction potential (ORP) and the dissolved oxygen (DO) have been monitored in a municipal wastewater treatment plant (WWTP). Three thousand two hundred aeration-non-aeration cycles were recorded. They were analyzed by defining 16 parameters to characterize each one of them. The vectors so obtained were treated with the box-plot tool to reject those with outliers (abnormally high or low values). The remaining data were processed by a neural network (self-organizing map: SOM) in order to classify them into classes and to obtain relations between parameters to identify those more representative of the system dynamics. They were: the oxygen uptake rate (OUR), the oxygen rise average slope (ORAS), and the oxidation-reduction potential "arrow" (ORP arrow , the maximum distance between the ORP curve and its linearization). Finally, the classes obtained from SOM were grouped into four macro-classes by means of the K-means algorithm in order to define four operation states related to seasonal and load characteristics, which may be taken into account, along with the key parameters, in the WWTP management with the aim of improving the nutrient removal performance by adapting their controllers to seasonal and load variations.

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Section: Self-organizing Map (Som)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Obtaining Key Parameters and Working Conditions of Wastewater Biological Nutrient Removal by Means of Artificial Intelligence Tools

Vega

Jaramillo-Morán

2018

Water

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“…In this study, we chose a heuristic rule often used in previous studies to calculate the number of nodes. The rule was known as m = 5 √ n, in which m is the number of SOM nodes and n is the number of input sites [26]. After the training process, preliminary grouping of samples was achieved and further clustering could be applied for the referenced vectors.…”

Section: The Self-organizing Mapmentioning

confidence: 99%

“…The k-means algorithm was one of the most frequently used methods and chosen for use in this study [27]. The Davies-Bouldin index (DBI) was calculated for different numbers of clusters, while the number with the lowest DBI was considered as the most optimal one for the trained SOM [26,28]. The samples with similar characteristics were classified into the same group and supplemented by additional analysis.…”

Section: The Self-organizing Mapmentioning

confidence: 99%

Chlorophyll-A Prediction of Lakes with Different Water Quality Patterns in China Based on Hybrid Neural Networks

Sha

Wang

2017

Water

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Abstract:One of the most important water quality problems affecting lakes and reservoirs is eutrophication, which is caused by multiple physical and chemical factors. As a representative index of eutrophication, the concentration of chlorophyll-a has always been a key indicator monitored by environmental managers. The most influential factors on chlorophyll-a may be dependent on the different water quality patterns in lakes. In this study, data collected from 27 lakes in different provinces of China during 2009-2011 were analyzed. The self-organizing map (SOM) was first applied on the datasets and the lakes were classified into four clusters according to 24 water quality parameters. Comparison amongst the clusters revealed that Cluster I was the least polluted and at the lowest trophic level, while Cluster IV was the most polluted and at the highest trophic level. The genetic algorithm optimized back-propagation neural network (GA-BPNN) was applied to each lake cluster to select the most influential input variables for chlorophyll-a. The results of the four clusters showed that the performance of GA-BPNN was satisfied with nearly half of the input variables selected from the predictor pool. The selected factors varied for the lakes in different clusters, which indicates that the control for eutrophication should be separate for lakes in different provinces of one country.

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“…Clustering analysis is an important and useful tool for analyzing large datasets that contain many variables and experimental parameters. Therefore, the application of cluster analysis to complex datasets has attracted a high level of scientific interest in various aspects of geochemistry research (Nguyen et al, 2015). In order to investigate the distribution of elements, it is essential for a robust classification scheme to cluster chemistry samples into homogeneous groups (Guler et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

Application of unsupervised pattern recognition approaches for exploration of rare earth elements in Se-Chahun iron ore, central Iran

Sarparandeh

Hezarkhani

2017

Geosci. Instrum. Method. Data Syst.

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Abstract. The use of efficient methods for data processing has always been of interest to researchers in the field of earth sciences. Pattern recognition techniques are appropriate methods for high-dimensional data such as geochemical data. Evaluation of the geochemical distribution of rare earth elements (REEs) requires the use of such methods. In particular, the multivariate nature of REE data makes them a good target for numerical analysis. The main subject of this paper is application of unsupervised pattern recognition approaches in evaluating geochemical distribution of REEs in the Kiruna type magnetite-apatite deposit of SeChahun. For this purpose, 42 bulk lithology samples were collected from the Se-Chahun iron ore deposit. In this study, 14 rare earth elements were measured with inductively coupled plasma mass spectrometry (ICP-MS). Pattern recognition makes it possible to evaluate the relations between the samples based on all these 14 features, simultaneously. In addition to providing easy solutions, discovery of the hidden information and relations of data samples is the advantage of these methods. Therefore, four clustering methods (unsupervised pattern recognition) -including a modified basic sequential algorithmic scheme (MBSAS), hierarchical (agglomerative) clustering, k-means clustering and selforganizing map (SOM) -were applied and results were evaluated using the silhouette criterion. Samples were clustered in four types. Finally, the results of this study were validated with geological facts and analysis results from, for example, scanning electron microscopy (SEM), X-ray diffraction (XRD), ICP-MS and optical mineralogy. The results of the k-means clustering and SOM methods have the best matches with reality, with experimental studies of samples and with field surveys. Since only the rare earth elements are used in this division, a good agreement of the results with lithology is considerable. It is concluded that the combination of the proposed methods and geological studies leads to finding some hidden information, and this approach has the best results compared to using only one of them.

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Clustering spatio–seasonal hydrogeochemical data using self-organizing maps for groundwater quality assessment in the Red River Delta, Vietnam

Cited by 143 publications

References 31 publications

Obtaining Key Parameters and Working Conditions of Wastewater Biological Nutrient Removal by Means of Artificial Intelligence Tools

Obtaining Key Parameters and Working Conditions of Wastewater Biological Nutrient Removal by Means of Artificial Intelligence Tools

Chlorophyll-A Prediction of Lakes with Different Water Quality Patterns in China Based on Hybrid Neural Networks

Application of unsupervised pattern recognition approaches for exploration of rare earth elements in Se-Chahun iron ore, central Iran

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