Learning habitat models for the diatom community in Lake Prespa

Kocev, Dragi; Naumoski, Andreja; Mitreski, Kosta; Krstić, Svetislav; Džeroski, Sašo

doi:10.1016/j.ecolmodel.2009.09.002

Cited by 36 publications

(29 citation statements)

References 12 publications

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“…Some research has applied data mining approaches to study the fauna aspect, represented by macroinvertebrates [29], fish communities [60], as well as the fauna dimension with diatoms [35] or phytoplankton populations [45]. State of art methods have revealed the importance of considering and combining biological and physicochemical variables in order to find relevant knowledge.…”

Section: Mining Water Quality Datamentioning

confidence: 99%

HydroQual: Visual analysis of river water quality

Accorsi

Lalande²,

Fabrègue

et al. 2014

2014 IEEE Conference on Visual Analytics Science and Technology (VAST)

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International audienceEconomic development based on industrialization, intensive agriculture expansion and population growth places greater pressure on water resources through increased water abstraction and water quality degradation [40]. River pollution is now a visible issue, with emblematic ecological disasters following industrial accidents such as the pollution of the Rhine river in 1986 [31]. River water quality is a pivotal public health and environmental issue that has prompted governments to plan initiatives for preserving or restoring aquatic ecosystems and water resources [56]. Water managers require operational tools to help interpret the complex range of information available on river water quality functioning. Tools based on statistical approaches often fail to resolve some tasks due to the sparse nature of the data. Here we describe HydroQual, a tool to facilitate visual analysis of river water quality. This tool combines spatiotem-poral data mining and visualization techniques to perform tasks defined by water experts. We illustrate the approach with a case study that illustrates how the tool helps experts analyze water quality. We also perform a qualitative evaluation with these experts

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Section: Mining Water Quality Datamentioning

confidence: 99%

HydroQual: Visual analysis of river water quality

Accorsi

Lalande²,

Fabrègue

et al. 2014

2014 IEEE Conference on Visual Analytics Science and Technology (VAST)

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“…It has been described as mesotrophic (Wagner et al, 2009) and is taken as an indicator of nutrient enrichment in this highly oligotrophic lake compared to C. fottii (Lorenschat et al, 2014). Cyclotella ocellata is also found to be favoured by high nitrogen concentration in shallower, mesotrophic Lake Prespa, the sister lake of Lake Ohrid (Kocev et al, 2010). Cyclotella minuscula is very small (3-5 µm diameter), and probably has a similar ecological niche as other small-celled Cyclotella sensu lato species (Saros and Anderson, 2015), which have low nutrient and light requirements, high growth rates and low sinking rates, owing to their high ratio of surface area to volume (Winder et al, 2009;Finkel et al, 2009).…”

Section: Discussionmentioning

confidence: 99%

“…Rühland et al, 2008). Moreover, high nitrogen concentration may favour both C. ocellata and C. minuscula (Kocev et al, 2010;Saros and Anderson, 2015). Thus, it is not surprising that C. ocellata and C. minuscula concur to respond to enhanced anthropogenic nutrient input during this period.…”

Section: The Late Holocene (Ca 2600 Cal Yr Bp-present)mentioning

confidence: 99%

Complexity of diatom response to Lateglacial and Holocene climate and environmental change in ancient, deep and oligotrophic Lake Ohrid (Macedonia and Albania)

et al. 2016

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Abstract. Lake Ohrid (Macedonia and Albania) is a rare example of a deep, ancient Mediterranean lake and is a key site for palaeoclimate research in the northeastern Mediterranean region. This study conducts the analysis of diatoms as a proxy for Lateglacial and Holocene climate and environmental change in Lake Ohrid at a higher resolution than in previous studies. While Lake Ohrid has the potential to be sensitive to water temperature change, the data demonstrate a highly complex diatom response, probably comprising a direct response to temperature-induced lake productivity in some phases and an indirect response to temperaturerelated lake stratification or mixing and epilimnetic nutrient availability in others. The data also demonstrate the possible influence of physical limnological (e.g. the influence of wind stress on stratification or mixing) and chemical processes (e.g.

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“…One such technique is single target regression trees (STRT) conducting binary recursive partitioning producing a set of rules and a regression model to predict a single response variable [27,28]. Several studies have successfully demonstrated the utility of STRTs as a powerful tool for data prediction [29][30][31]. This study attempts to predict biocontrol damage on water hyacinth plants which to the author's knowledge has not been explored before.…”

Section: Introductionmentioning

confidence: 99%

“…Multitarget regression trees (MTRTs) predict several numeric response variables simultaneously [29] and offer several advantages over STRTs. For example, MTRTs are smaller in size than STRTs and are faster to train thus making them more efficient to implement [30].…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Regression Tree Approaches to Modelling the Efficacy of Water Hyacinth Biocontrol Using Multitemporal Spectral Datasets

Agjee

Mutanga

Gebreselasie

et al. 2018

Journal of Spectroscopy

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Water hyacinth (Eichhornia crassipes) is an exotic plant species that is effectively controlled byNeochetinaspp. weevils. This study is aimed at determining if spectroscopic data may be utilized to predict insect-induced stress on water hyacinth plants. Single target regression trees (STRTs), multitarget regression trees (MTRTs), and random forest multitarget regression trees (RF-MTRTs) have been used to predict feeding scar damage (FSD) and relative leaf chlorophyll content (RLCC) from hyperspectral canopy reflectance data. Results from this study show that the correlation coefficient of STRTs (training accuracy: 76%–97%; validation accuracy: 47%–86%) performs better than MTRTs (training accuracy: 74%–90%; validation accuracy: 45%–77%) for all infestation levels but are difficult to interpret simultaneously. In contrast, MTRTs (size: 23–35 nodes) are much smaller and more interpretable than STRTs (size: 11–47 nodes) because they predict FSD and RLCC simultaneously. Importantly, RF-MTRTs (training accuracy: 95%–98%; validation accuracy: 55%–88%) yield better predictive performance than STRTs and MTRTs for all infestation levels. It is concluded that MTRTs can be utilized for model interpretation as they are more interpretable; however, RF-MTRTs offer an improved predictive performance.

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Learning habitat models for the diatom community in Lake Prespa

Cited by 36 publications

References 12 publications

HydroQual: Visual analysis of river water quality

HydroQual: Visual analysis of river water quality

Complexity of diatom response to Lateglacial and Holocene climate and environmental change in ancient, deep and oligotrophic Lake Ohrid (Macedonia and Albania)

A Comparison of Regression Tree Approaches to Modelling the Efficacy of Water Hyacinth Biocontrol Using Multitemporal Spectral Datasets

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