Assessment of Self-Organizing Map artificial neural networks for the classification of sediment quality

Alvarez‐Guerra, Manuel; González-Piñuela, Cristina; Andrés, A.; Galán, Berta; Viguri, Javier R.

doi:10.1016/j.envint.2008.01.006

Cited by 98 publications

(51 citation statements)

References 40 publications

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“…Nevertheless, some examples can be found in the literature, such as the application of SOM to group 71 stations in Taiwan according to daily average PM 10 concentrations [13], to evaluate relationships with source fingerprints obtained from multi-element PM 2.5 data from different sites of Australia [14], to detect seasonal patterns of air pollutants [15], or to identify redundant sensors and evaluate a network's capability to correctly follow and represent SO 2 fields in Bilbao [16]. Nevertheless, according to the satisfactory results obtained with the application of SOM to classify data in other environmental compartments, like water [17], sediments [18], or soils [19], these kinds of techniques can also be beneficial for the field of air pollution.…”

Section: Introductionmentioning

confidence: 93%

“…It is important to clarify that although the three different normalizations implemented in SOM Toolbox were tried, the preliminary results as well as previous studies [18] suggested focusing on the normalization of variables in the range [0, 1] for subsequent detailed analyses, since it allowed achieving the maps with lowest values of both QE and TE. Therefore, eight classifications of the Spanish monitoring stations were analyzed, corresponding to all the possible combinations of values for the following three binary parameters.…”

Section: Overview Of Legal Limit Values In Spanish Stationsmentioning

confidence: 99%

“…Two outputs of the application of SOM algorithm are very important for interpreting the results: the SOM map itself and the ''component planes'' [18,27].…”

Section: Self-organizing Maps (Soms)mentioning

confidence: 99%

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A SOM‐based methodology for classifying air quality monitoring stations

Alvarez‐Guerra

Molina

Viguri

et al. 2011

Env Prog and Sustain Energy

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The application of mathematical tools can be necessary to provide an integrated analysis and interpretation of the abundant information that can be collected in air quality monitoring networks. This article develops a methodology based on the use of SelfOrganizing Map (SOM) artificial neural networks for integrating data about multiple measured pollutants to group monitoring stations according to their similar air quality. The proposed method considers the subsequent geographical mapping of the clusters of stations observed with the SOM, which can make it possible to detect geographically different areas but that share similar air pollution problems. This methodology is illustrated with its application to a case study in which 517 stations of the Spanish air quality monitoring network were classified considering simultaneously their levels of regulated pollutants in 2005, highlighting some implications of data normalization in the process. In particular, the use of legal limit values to normalize the concentrations of pollutants proved to be especially advisable. Results obtained with the SOM-based methodology, when compared to classifications based directly on legislation, provided more useful classifications for further air quality management actions, and revealed that these types of tools can facilitate the design of air pollution reduction programs by discovering different areas with similar problems.

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Section: Introductionmentioning

confidence: 93%

Section: Overview Of Legal Limit Values In Spanish Stationsmentioning

confidence: 99%

See 1 more Smart Citation

A SOM‐based methodology for classifying air quality monitoring stations

Alvarez‐Guerra

Molina

Viguri

et al. 2011

Env Prog and Sustain Energy

Self Cite

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“…The Self-Organizing Maps (SOM) (Kohonen, 1990), an ANN with unsupervised learning is the other commonly used clustering algorithm in environmental data (Andina, Jevtic, Marcano, & Barrón-Adame, 2007). SOM is suitable for data classification because of its visualization property (Alvarez-Guerra, Gonzlez-Piuela, AndrTs, Gain, & Viguri, 2008;Seo & Obermayer, 2004;Vesanto & Alhoniemi, 2000). For example, the SOM has been used to identify patterns in satellite imagery in oceanography (Richardson, Risien, & Shillington, 2003); to visualize and cluster volcanic ash (Ersoy, Aydar, Gourgaud, Artuner, & Bayhan, 2007); or to estimate the risk of insect species invasion associated with geographic regions (Watts & Worner, 2009).…”

Section: Introductionmentioning

confidence: 99%

Unsupervised system to classify SO2 pollutant concentrations in Salamanca, Mexico

Barrón-Adame

Cortina-Januchs

Vega-Corona

et al. 2012

Expert Systems with Applications

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Keywords:Air pollution Meteorological variables Artificial neural networks Self-Organizing Maps (SOM) Clustering Salamanca is cataloged as one of the most polluted cities in Mexico. In order to observe the behavior and clarify the influence of wind parameters on the Sulphur Dioxide (S0 2 ) concentrations a Self-Organizing Maps (SOM) Neural Network have been implemented at three monitoring locations for the period from January 1 to December 31, 2006. The maximum and minimum daily values of S0 2 concentrations measured during the year of 2006 were correlated with the wind parameters of the same period. The main advantages of the SOM Neural Network is that it allows to integrate data from different sensors and provide readily interpretation results. Especially, it is powerful mapping and classification tool, which others information in an easier way and facilitates the task of establishing an order of priority between the distinguished groups of concentrations depending on their need for further research or remediation actions in subsequent management steps. For each monitoring location, SOM classifications were evaluated with respect to pollution levels established by Health Authorities. The classification system can help to establish a better air quality monitoring methodology that is essential for assessing the effectiveness of imposed pollution controls, strategies, and facilitate the pollutants reduction.

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“…Chang et al, (1998Chang et al, ( , 2000Chang et al, ( , 2002 associated well log data with lithofacies, using Kohonen self organizing maps, in order to easily understand the relationships between clusters. The SOM was employed to evaluate water quality (Lee & Scholtz, 2006), to cluster volcanic ash arising from different fragmentation mechanisms (Ersoya et al, 2007), to categorize different sites according to similar sediment quality (Alvarez-Guerra et al, 2008), to assess sediment quality and finally define mortality index on different sampling sites (Tsakovski et al, 2009). SOM was also used for supervised assessment of erosion risk (Barthkowiak & Evelpidou, 2006).…”

mentioning

confidence: 99%