Applications of machine learning to ecological modelling

Recknagel, Friedrich

doi:10.1016/s0304-3800(01)00316-7

Cited by 227 publications

(118 citation statements)

References 16 publications

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“…During the training phase, parameters of the models are tuned automatically by the learning algorithm in order to obtain the best classiWcation performances on the training set (Bergeron 2003). Such methods which are based on machine learning algorithms have been applied with success in other biological disciplines, particularly molecular biology (King and Sternberg 1990;Muggleton et al 1992), drug design (King et al , 1993, neurology (Zhang et al 2005a, b;Mitchell 2005, Bogacz andBrown 2003) and ecology (Stockwell 2006;Recknagel 2001;Obach et al 2001;Schleiter et al 2001).…”

Section: Introductionmentioning

confidence: 99%

Classification of dog barks: a machine learning approach

Molnár

Kaplan

Roy³

et al. 2008

Anim Cogn

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In this study we analyzed the possible contextspeciWc and individual-speciWc features of dog barks using a new machine-learning algorithm. A pool containing more than 6,000 barks, which were recorded in six diVerent communicative situations was used as the sound sample. The algorithm's task was to learn which acoustic features of the barks, which were recorded in diVerent contexts and from diVerent individuals, could be distinguished from another. The program conducted this task by analyzing barks emitted in previously identiWed contexts by identiWed dogs. After the best feature set had been obtained (with which the highest identiWcation rate was achieved), the eYciency of the algorithm was tested in a classiWcation task in which unknown barks were analyzed. The recognition rates we found were highly above chance level: the algorithm could categorize the barks according to their recorded situation with an eYciency of 43% and with an eYciency of 52% of the barking individuals. These Wndings suggest that dog barks have context-speciWc and individual-speciWc acoustic features. In our opinion, this machine learning method may provide an eYcient tool for analyzing acoustic data in various behavioral studies.

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Section: Introductionmentioning

confidence: 99%

Classification of dog barks: a machine learning approach

Molnár

Kaplan

Roy³

et al. 2008

Anim Cogn

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“…Nowadays, in case of ecological studies greater attention is drawn to artificial neural networks, and Kohonens ANN is routinely used for ordination and visualization of complex ecological data. Its offers an attractive solution to lots of problems in many critical applications, presently used in many modelling tasks (Recknagel 2001).…”

Section: Introductionmentioning

confidence: 99%

Advances in ecological modelling of soil properties by self-organizing feature maps of natural environment of Lower Silesia (Poland)

Stankiewicz¹,

Kosiba²

2011

Acta Soc Bot Pol

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The paper provides the use of self-organizing feature maps for determination of soil properties in its initial stage of development formed of massive rocks and how SOFM can be used for the study of environmental objects. The study area was Lower Silesia (Poland) overgrown with common, unique and protected vegetation of lichens, bryophytes and vascular plants. The parent rock of the studied soils consists of Miocene volcanites from the middle part of the Sudety Margin Fault. Soil samples were collected from 20 sites. The soil reaction (pH) and concentrations of Cd, Co, Cu, Fe, Mn, Mo, Ni, Pb, S, Ti, Zn in surface soils were analyzed. Statistical analysis was carried out by one-way ANOVA. The SOFM was used to demonstrate the non-linear ordination and visualization of soil properties. The SOFM showed the influence of parent rock on soil chemical properties generated by it. SOFM appeared to be effective and proper/fit for phenomena and processes taking place in natural environment and is useful in ecology and ought to be taken into account as a possible tool of estimation of various plants and their biotopes. The model can be useful as alternative techniques in modelling the ecological complex data, and provide a novel framework for the discovery and forecasting of ecosystem structure and behaviours in response to environmental changes

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“…In an attempt to cluster and pattern complex nonlinear data, Kohonen (1982) designed an ANN for self-organizing mapping based on an unsupervised learning algorithm. According to Chon et al (1996), Haykin (1999), Recknagel (2001) and Gevrey et al (2003), Kohonens ANN are routinely used for ordination and visualization of complex ecological data, and offer an attractive solution to solve lots of problems in many critical applications, which are presently used in many modeling tasks. Water ecosystems act the significant part in all aspects of environment pollution, which are for many years a problem in many countries (Hutchinson 1975;Wiegleb et al 1991;Smith et al 1999;Bell and Treshow 2002).…”

Section: Introductionmentioning

confidence: 99%

Water trophicity of Utricularia microhabitats identlfied by means of SOFM as a tool in ecological modeling

Kosiba¹,

Stankiewicz²

2011

Acta Soc Bot Pol

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The study objects were 48 microhabitats of five Utricularia species in Lower and Upper Silesia (POLAND). The aim of the paper was to focus on application of the Self-Organizing Feature Map in assessment of water trophicity in Utricularia microhabitats, and to describe how SOFM can be used for the study of ecological subjects. This method was compared with the hierarchical tree plot of cluster analysis to check whether this techniques give similar results. In effect, both topological map of SOFM and dendrogram of cluster analysis show differences between Utricularia species microhabitats in respect of water quality, from eutrophic for U. vulgaris to dystrophic for U. minor and U. intermedia. The used methods give similar results and constitute a validation of the SOFM method in this type of studies.

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Applications of machine learning to ecological modelling

Cited by 227 publications

References 16 publications

Classification of dog barks: a machine learning approach

Classification of dog barks: a machine learning approach

Advances in ecological modelling of soil properties by self-organizing feature maps of natural environment of Lower Silesia (Poland)

Water trophicity of Utricularia microhabitats identlfied by means of SOFM as a tool in ecological modeling

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