Open Access
Issue
Knowl. Manag. Aquat. Ecosyst.
Number 418, 2017
Article Number 36
Number of page(s) 16
DOI https://doi.org/10.1051/kmae/2017027
Published online 21 August 2017
  • Adrian R, Deneke R. 1996. Possible impact of mild winters on zooplankton succession in eutrophic lakes of the Atlantic European area. Freshw Biol 36: 757–770. [CrossRef]
  • Arvola L, Salonen K, Keskitalo J, Tulonen T, Järvinen M, Huotari J. 2014. Plankton metabolism and sedimentation in a small boreal lake – a long-term perspective. Boreal Environ Res 19A: 83–96.
  • Astel A, Tsakovski S, Barbieri P, Simeonov V. 2007. Comparison of self-organizing maps classification approach with cluster and principal components analysis for large environmental data sets. Water Res 41: 4566–4578. [CrossRef] [PubMed]
  • Barreto GA. 2007. Time series prediction with the self-organizing map: a review. In: Hammer B, Hitzler P, eds. Perspectives of neural-symbolic integration. Volume 77 of the series Studies in Computational Intelligence. Berlin: Springer-Verlag, pp. 135–158.
  • Beisner BE, Peres-Neto PR, Lindström ES, Barnett A, Longhi ML. 2006. The role of environmental and spatial processes in structuring lake communities from bacteria to fish. Ecology 87: 2985–2991. [CrossRef] [PubMed]
  • Borcard D, Gillet F, Legendre P. 2011. Numerical ecology with R. New York: Springer, 306 p. [EDP Sciences]
  • Calinski T, Harabasz J. 1974. A dendrite method for cluster analysis. Commun Stat 3: 1–27.
  • Compin A, Céréghino R. 2007. Spatial patterns of macroinvertebrate functional feeding groups in streams in relation to physical variables and land-cover in Southwestern France. Landsc Ecol 22: 1215–1225. [CrossRef]
  • Forsius M, Ahonen J, Alveteg M, et al. 1998. Model interaction for the assessment of emission scenarios. In: Forsius M, Guardans R, Jenkins A, Lundin L, Nielsen KE, eds. Integrated monitoring: environmental assessment through model and empirical analysis. Final results from the EU/Life-Project Development of Assessment and Monitoring Techniques at Integrated Monitoring Sites in Europe. The Finnish Environment 218. Helsinki: Finnish Environment Institute, pp. 92–99.
  • Futter MN, Forsius M, Holmberg M, Starr M. 2009. A long-term simulation of the effects of acidic deposition and climate change on surface water dissolved organic carbon concentrations in a boreal catchment. Hydrol Res 40: 291–305. [CrossRef]
  • Gaedke U, Schweizer A. 1993. The first decade of oligotrophication in Lake Constance. I. The response of phytoplankton biomass and cell size. Oecologia 93: 268–275. [CrossRef] [PubMed]
  • Heini A, Puustinen I, Tikka M, Jokiniemi A, Leppäranta M, Arvola L. 2014. Strong dependence between phytoplankton and water chemistry in a large temperate lake: spatial and temporal perspective. Hydrobiologia 731: 139–150. [CrossRef]
  • Hipel KW, McLeod AI. 1994. Time series modelling of water resources and environmental systems. In: Developments in water science, Vol. 45. Amsterdam: Elsevier, 1012 p.
  • Holmberg M, Futter MN, Kotamäki N, et al. 2014. Effects of changing climate on the hydrology of a boreal catchment and lake DOC – probabilistic assessment of a dynamic model chain. Boreal Environ Res 19A: 66–82.
  • Huotari J, Ojala A, Peltomaa E, Pumpanen J, Hari P, Vesala T. 2009. Temporal variations in surface water CO2 concentration in a boreal humic lake based on high frequency measurements. Boreal Environ Res 14: 48–60.
  • Huttunen JT, Alm J, Liikanen A, et al. 2003. Fluxes of methane, carbon dioxide and nitrous oxide in boreal lakes and potential anthropogenic effects on the aquatic greenhouse gas emissions. Chemosphere 52: 609–621. [CrossRef] [PubMed]
  • Jones RI, Grey J, Sleep D, Arvola L. 1999. Stable isotope analysis of zooplankton carbon nutrition in humic lakes. Oikos 86: 97–104. [CrossRef]
  • Jylhä K, Laapas M, Ruosteenoja K, et al. 2014. Climate variability and trends in the Valkea-Kotinen region, southern Finland: comparisons between the past, current and projected climates. Boreal Environ Res 19A: 4–30.
  • Kalteh AM, Hjorth P, Berndtsson R. 2008. Review of the self-organizing map (SOM) approach in water resources: analysis, modelling and application. Environ Modell Softw 23: 835–845. [CrossRef]
  • Kangur K, Park Y-S, Kangur A, Kangur P, Lek S. 2007. Patterning long-term changes of fish community in large shallow Lake Peipsi. Ecol Model 203: 34–44. [CrossRef]
  • Keskitalo J, Salonen K. 1998. Fluctuations of phytoplankton production and chlorophyll concentrations in a small humic lake during six years (1990–1995). In: George DG, Jones JG, Punčochář P, Reynolds CS, Sutcliffe DW, eds. Management of lakes and reservoirs during global climate change. Dordrecht: Kluwer Academic Publishers, pp. 93–109. [CrossRef]
  • Kohonen T. 1990. The self-organizing map. P IEEE 78: 1464–1480. [CrossRef]
  • Kohonen T. 2013. Essentials of the self-organizing map. Neural Netw 37: 52–65. [CrossRef] [PubMed]
  • Kurka A-M, Starr M. 2014. Relationship between decomposition of cellulose in the soil and tree stand characteristics in natural boreal forests. Plant Soil 197: 1677–1675.
  • Lehtovaara A, Arvola L, Keskitalo J, et al. 2014. Responses of zooplankton to long-term environmental changes in a small boreal lake. Boreal Environ Res 19A: 97–111.
  • Lin G-F, Chen L-H. 2005. Time series forecasting by combining the radial basis function network and the self-organizing map. Hydrol Process 19: 1925–1937. [CrossRef]
  • Magnuson JJ, Benson BJ, Kratz TK. 2004. Patterns of coherent dynamics within and between lake districts at local to intercontinental scales. Boreal Environ Res 9: 359–369.
  • Magnuson JJ, Kratz TK, Benson BJ, Webster KE. 2006. Coherent dynamics among lakes. In: Magnuson JJ, Kratz TK, Benson BJ, eds. Long-term dynamics of lakes in the landscape: long-term ecological research on north temperate lakes. New York: Oxford University Press, pp. 89–106.
  • Oja M, Kaski S, Kohonen T. 2002. Bibliography of Self-Organizing Map (SOM) papers: 1998–2001 addendum. Neural Comput Surv 3: 1–156. [EDP Sciences]
  • O'Reilly C, Sharma S, Gray DK, et al. 2015. Rapid and highly variable warming of lake surface waters around the globe. Geophys Res Lett 42: 10773–10781. [CrossRef]
  • Park Y-S, Céréghino R, Compin A, Lek S. 2003. Applications of artificial neural networks for patterning and predicting aquatic insect species richness in running waters. Ecol Model 160: 265–280. [CrossRef]
  • Peltomaa E, Ojala A. 2010. Size-related photosynthesis of algae in a strongly stratified humic lake. J Plankton Res 32: 341–355. [CrossRef]
  • Peltomaa E, Ojala A, Holopainen A-L, Salonen K. 2013a. Changes in phytoplankton in a boreal lake during a 14-year period. Boreal Environ Res 18: 387–400.
  • Peltomaa E, Zingel P, Ojala A. 2013b. Weak response of the microbial food web of a boreal humic lake to hypolimnetic anoxia. Aquat Microb Ecol 68: 91–105. [CrossRef]
  • Pölzlbauer G, Dittenbach M, Rauber A. 2006. Advanced visualization of Self-Organizing Maps with vector fields. Neural Netw 19: 911–922. [CrossRef] [PubMed]
  • Rask M, Sairanen S, Vesala S, Arvola L, Estlander S, Olin M. 2014. Population dynamics and growth of perch in a small, humic lake over a 20-year period – importance of abiotic and biotic factors. Boreal Environ Res 19A: 112–123.
  • Rimet F, Druart J-C, Anneville O. 2009. Exploring the dynamics of plankton diatom communities in Lake Geneva using emergent self-organizing maps (1974–2007). Ecol Inform 4: 99–110. [CrossRef]
  • Ruoho-Airola T, Hatakka T, Kyllönen K, Makkonen U, Porvari P. 2014. Temporal trends in the bulk deposition and atmospheric concentration of acidifying compounds and trace elements in the Finnish Integrated Monitoring catchment Valkea-Kotinen during 1988–2011. Boreal Environ Res 19A: 31–46.
  • Salonen K, Arvola L, Tulonen T, et al. 1992a. Planktonic food chains of a highly humic lake. I. A mesocosm experiment during the spring primary production maximum. Hydrobiologia 229: 125–142. [CrossRef]
  • Salonen K, Kankaala P, Tulonen T, et al. 1992b. Planktonic food chains of a highly humic lake. II. A mesocosm experiment in summer during dominance of heterotrophic processes. Hydrobiologia 229: 143–157. [CrossRef]
  • Saloranta T, Forsius M, Järvinen M, Arvola L. 2009. Impacts of projected climate change on the thermodynamics of a shallow and deep lake in Finland: model simulations and Bayesian uncertainty analysis. Hydrol Res 40: 234–247. [CrossRef]
  • Samad T, Harp SA. 1992. Self-organization with partial data. Network 3: 205–212. [CrossRef]
  • Sathya R, Abraham A. 2013. Comparison of supervised and unsupervised learning algorithms for pattern classification. IJARAI 2: 34–38. [CrossRef]
  • Siegel S, Castellan NJ Jr. 1988. Nonparametric statistics for the behavioural sciences. Singapore: McGraw-Hill Book Company, 399 p.
  • Starr M, Ukonmaanaho L. 2004. Results from the first round of the integrated monitoring soil chemistry subprogramme. In: Ukonmaanaho L, Raitio H, eds. Forest condition in Finland. National report 2000. Research papers 824. Helsinki: Finnish Forest Research Institute, pp. 140–157.
  • Vesanto J. 1999. SOM-based data visualization methods. Intell Data Anal 3: 111–126. [CrossRef]
  • Vesanto J, Alhoniemi E. 2000. Clustering of the self-organizing map. IEEE Trans Neural Netw 11: 586–600. [CrossRef] [PubMed]
  • Vesanto J, Himberg J, Alhoniemi E, Parhankangas J. 2000. SOM toolbox for Matlab 5. SOM toolbox team, report A57. Helsinki: Helsinki University of Technology, 59 p.
  • Vilibić I, Mihanović H, Šepić J, Matijević S. 2011. Using Self-Organising Maps to investigate long-term changes in deep Adriatic water patterns. Cont Shelf Res 31: 695–711. [CrossRef]
  • Voutilainen A, Huuskonen H. 2010. Long-term changes in the water quality and fish community of a large boreal lake affected by rising water temperatures and nutrient-rich sewage discharges – with special emphasis on the European perch. Knowl Manag Aquat Ecosyst 397: 03. [CrossRef] [EDP Sciences]
  • Voutilainen A, Rahkola-Sorsa M, Parviainen J, Huttunen MJ, Viljanen M. 2012. Analysing a large dataset on long-term monitoring of water quality and plankton with the SOM clustering. Knowl Manag Aquat Ecosyst 406: 04. [CrossRef] [EDP Sciences]
  • Voutilainen A, Kvist T, Sherwood PR, Vehviläinen-Julkunen K. 2014. A new look at patient satisfaction: learning from self-organizing maps. Nurs Res 63: 333–345. [CrossRef]
  • Voutilainen A, Hartikainen S, Sherwood PR, Taipale H, Tolppanen A-M, Vehviläinen-Julkunen K. 2015. Associations across spatial patterns of disease incidences, socio-demographics, and land use in Finland 1991–2010. Scand J Public Health 43: 356–363. [PubMed]
  • Vuorenmaa J, Salonen K, Arvola L, Mannio J, Rask M, Horppila P. 2014. Water quality of a small headwater lake reflects long-term variations in deposition, climate and in-lake processes. Boreal Environ Res 19A: 47–65.
  • Vähätalo AV, Salonen K, Münster U, Järvinen M, Wetzel RG. 2003. Photochemical transformation of allochthonous organic matter provides bioavailable nutrients in a humic lake. Arch Hydrobiol 156: 287–314. [CrossRef]

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