Analysing a large dataset on long-term monitoring of water quality and plankton with the SOM clustering

Voutilainen, Ari; Rahkola-Sorsa, Minna; Parviainen, Jukka; Huttunen, Markus; Viljanen, Markku

doi:10.1051/kmae/2012021

Cited by 5 publications

(7 citation statements)

References 31 publications

(40 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Typically, the abilities of SOM to detect both spatial and temporal phenomena simultaneously (Astel et al, 2007) and associate clusters based on spatial or temporal sampling points with those based on variables measured (Voutilainen et al, 2012) are highlighted. In this study, the SOM method clustered the whole data into three major categories of which the first one consisted of variables with high concentration, biomass and/or abundance in spring during 1990s, or during the first five summer seasons and summer 2010.…”

Section: Discussionmentioning

confidence: 99%

“…Although the SOM logically clustered the data according to temporal sampling points, detecting temporal trends solely on the basis of the SOM would have been unsure. Modifications of the SOM (Barreto, 2007) as well as combinations of the SOM and other techniques (Lin and Chen, 2005) have been successfully applied to time series forecasting, but the basic unsupervised SOM is not the solution when the purpose is to search for temporal trends (see also Voutilainen et al, 2012).…”

Section: Discussionmentioning

confidence: 99%

“…Unsupervised means that only input data is provided to the network instead of both input and output (results) data (Sathya and Abraham, 2013). The SOM has been used mainly for data classification, data compression, pattern recognition, and diagnostic purposes in a wide variety of fields of science (Oja et al, 2002), including limnology (Park et al, 2003;Compin and Céréghino, 2007;Kangur et al, 2007;Rimet et al, 2009;Vilibić et al, 2011;Voutilainen et al, 2012). Due to the flexibility of SOM its results can be further analysed with other methods (Voutilainen et al, 2014) and/or combined with results provided by other methods (Voutilainen et al, 2015).…”

Section: Self-organizing Mapmentioning

confidence: 99%

“…Although long-term data can be extremely powerful to reveal causal relationships between physical, chemical, and biological variables in lakes (Magnuson et al, 2004(Magnuson et al, , 2006Voutilainen et al, 2012;, such data sets can be difficult to analyse and interpret due to the vast amount of available information as well as due to the complex interactions between different variables (Beisner et al, 2006). For example, in lake ecosystems both irregular and cyclic fluctuations are common (Gaedke and Schweizer, 1993;Adrian and Deneke, 1996;Voutilainen and Huuskonen, 2010) and many direct and indirect interactions may proceed in tandem with their own specific spatial and temporal scales, including rapid metabolic processes and slower population patterns (Heini et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…In previous surveys, the Kohonen's self-organizing map (SOM) technique (Kohonen, 1990(Kohonen, , 2013 has been found to be the method of choice for clustering large data sets related to aquatic ecology and water resources (Astel et al, 2007;Kangur et al, 2007;Kalteh et al, 2008;Rimet et al, 2009;Vilibić et al, 2011;Voutilainen et al, 2012). It can even be argued that over the past 10 years SOM has become one of the standard methods for analysing complex ecological data.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

SOM clustering of 21-year data of a small pristine boreal lake

Voutilainen

Арвола

2017

Knowl. Manag. Aquat. Ecosyst.

View full text Add to dashboard Cite

-In order to improve our understanding of the connections between the biological processes and abiotic factors, we clustered complex long-term ecological data with the self-organizing map (SOM) technique. The available 21-year long data set from a small pristine humic lake, in southern Finland, consisted of 27 meteorological, physical, chemical, and biological variables. The SOM grouped the data into three categories of which the first one was the largest with 12 variables, including metabolic processes, dissolved oxygen, total nitrogen and phosphorus, chlorophyll a, and taxonomical groups of plankton known to exist in spring. The second cluster comprised of water temperature and precipitation together with cyanobacteria, algae, rotifers, and crustacean zooplankton, an association emphasized with summer. The third cluster was consisted of six physical and chemical variables linked to autumn, and to the effects of inflow and/or water column mixing. SOM is a useful method for grouping the variables of such a large multi-dimensional data set, especially, when the purpose is to draw comprehensive conclusions rather than to search for associations across sporadic variables. Sampling should minimize the number of missing values. Even flexible statistical techniques, such as SOM, are vulnerable to biased results due to incomplete data.Keywords: boreal lake / data partitioning / ecological complexity / long-term data / self-organizing map Résumé -Application de carte auto-adaptative SOM pour des données écologiques complexes : un test avec des données à long terme d'un petit lac boréal. Afin d'améliorer notre compréhension des liens entre les processus biologiques et les facteurs abiotiques, nous avons regroupé des données écologiques complexes à long terme avec la technique de carte auto-adaptative (SOM). L'ensemble de données disponibles de 21 ans (1990 à 2020) d'un petit lac humique vierge, dans le sud de la Finlande, comprenait 27 variables météorologiques, physiques, chimiques et biologiques. La SOM a regroupé les données en trois catégories dont la première était la plus importante avec 12 variables, y compris les processus métaboliques, l'oxygène dissous, l'azote total et le phosphore, la chlorophylle a et les groupes taxonomiques de plancton connus au printemps. Le deuxième groupe composé de la température de l'eau et des précipitations avec des cyanobactéries, trois groupes d'algues, des rotifères et du zooplancton crustacé, une association principalement estivale. Le troisième groupe était constitué de six variables physiques et chimiques (décharge, couleur, carbone organique dissous, carbone inorganique dissous, ammonium et nitrite et nitrate d'azote) qui peuvent être liés à l'automne et aux effets d'apports et / ou au mélange de la colonne d'eau. SOM est une méthode utile pour regrouper les variables d'un tel ensemble de données multidimensionnelles, surtout lorsque l'objectif est de tirer des conclusions globales plutôt que de rechercher des associations dans des variables sporadiques.Mots-clés : ...

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Self-organizing Mapmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

SOM clustering of 21-year data of a small pristine boreal lake

Voutilainen

Арвола

2017

Knowl. Manag. Aquat. Ecosyst.

View full text Add to dashboard Cite

show abstract

Assessment of surface water quality using a growing hierarchical self-organizing map: a case study of the Songhua River Basin, northeastern China, from 2011 to 2015

Jiang

Wang

Yang

et al. 2018

Environ Monit Assess

View full text Add to dashboard Cite

The analysis of a large number of multidimensional surface water monitoring data for extracting potential information plays an important role in water quality management. In this study, growing hierarchical self-organizing map (GHSOM) was applied to a water quality assessment of the Songhua River Basin in China using 22 water quality parameters monitored monthly from 13 monitoring sites from 2011 to 2015 (14,782 observations). The spatial and temporal features and correlation between the water quality parameters were explored, and the major contaminants were identified. The results showed that the downstream of the Second Songhua River had the worst water quality of the Songhua River Basin. The upstream and midstream of Nenjiang River and the Second Songhua River had the best. The major contaminants of the Songhua River were chemical oxygen demand (COD), ammonia nitrogen (NH-N), total phosphorus (TP), and fecal coliform (FC). In the Songhua River, the water pollution at downstream has been gradually eased in years. However, FC and biochemical oxygen demand (BOD) showed growth over time. The component planes showed that three sets of parameters had positive correlations with each other. GHSOM was found to have advantages over self-organizing maps and hierarchical clustering analysis as follows: (1) automatically generating the necessary neurons, (2) intuitively exhibiting the hierarchical inheritance relationship between the original data, and (3) depicting the boundaries of the classification much more clearly. Therefore, the application of GHSOM in water quality assessments, especially with large amounts of monitoring data, enables the extraction of more information and provides strong support for water quality management.

show abstract