Testing a multiple machine learning tool (HYDRA) for the bioassessment of fresh waters

Feio, Maria João; Viana-Ferreira, Carlos; Costa, Carlos

doi:10.1086/678768

Cited by 9 publications

(10 citation statements)

References 45 publications

(51 reference statements)

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“…To build the dirty‐water predictive model, we simultaneously used three supervised machine learning techniques (SVM, MLP and KNN from the HYDRA tool; Feio et al, 2014a, 2014b) to predict the presence of each taxon under given environmental conditions. These techniques have been adapted to ecological studies, and have the ability to model and predict the distribution of species from a complex data set including nonlinear relationships and nonnormal distribution of variables, that make it difficult to use classical models based on discriminant functions (Gevrey et al, 2004; Rose et al, 2016).…”

Section: Methodsmentioning

confidence: 99%

“…The best model for each taxon was then used to define the probability of finding a taxon at a site. As all these approaches have advantages and disadvantages, depending on the type of data (Feio et al, 2014a, 2014b), their simultaneous use allows for the best possible predictions. The minimum taxon accuracy to accept a taxon prediction is 0.5, which means that a taxon is excluded from further analyses if its accuracy falls below 0.5 in all methods.…”

Section: Methodsmentioning

confidence: 99%

“…Accordingly, simulation tools are key for assessing successful recovery because they can incorporate multiple information sources in degradation indices and test different combinations of rehabilitation scenarios (Brudvig, 2017; Hermoso et al, 2012). Predictive models based on machine learning techniques have been developed and tested for bioassessment of rivers and reservoirs (e.g., Chen and Liu, 2014; Feio et al, 2020; Feio, Viana‐Ferreira, & Costa, 2014a, 2014b; Gabriels, Goethals, Dedecker, Lek, & De Pauw, 2007; Linke, Norris, Faith, & Stockwell, 2005; Park, Cho, Park, Cha, & Kim, 2015; Sarrazin‐Delay, Somers, & Bailey, 2014), and have been shown to be promising tools in the context of river rehabilitation. These approaches have the ability to model and predict species distribution in dimensional space with advantages over classical predictive modelling techniques of: not requiring a priori reference sites that can be viewed as artificial; capturing nonlinear relationships; and being less influenced by outliers (Gevrey et al, 2004; Rose, Kennard, Moffatt, Sheldon, & Butler, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…The HYDRA machine learning tool (Feio et al, 2014a, 2014b) is a modelling tool that allows the prediction of communities through the simultaneous use of three different techniques: support vector machines (SVM;), multilayer perceptron (MLP) and K‐nearest neighbour analysis (KNN). This tool has already been widely tested with large datasets from Europe, North America and Oceania rivers and has displayed a good precision and accuracy in identifying alterations in aquatic communities (Feio et al, 2014a, 2014b, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Rehabilitation scenarios for reservoirs: Predicting their effect on invertebrate communities through machine learning

Azevêdo

Bezerra‐Neto

Molozzi

et al. 2020

River Research & Apps

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Reservoirs are ecosystems that provide important services and reserves of aquatic biodiversity, especially in arid and semiarid regions. However, they are also highly vulnerable to pollution, water abstraction and land use change. Here, we aimed to develop a “dirty‐water” predictive model that could be used to create recovery scenarios and analyze the effect of measures on the biological quality of reservoirs. Accordingly, we constructed a machine learning predictive model that was trained with environmental and macroinvertebrate community data from 129 sites, sampled in six reservoirs in Northeast Brazil. Two different rehabilitation scenarios were simulated (D1 = lower improvement, 25% change; and D2 = higher improvement, 75% change), and three initial levels of disturbance were considered based on PCA analyses: least disturbed, moderately disturbed and severely disturbed. The effects were analyzed in terms of changes in expected taxa (E), observed/expected taxon ratio (OE), biotic and trophic status indices and the spatial distribution of sensitive taxa. The simulations resulted in significant improvements (p < .001) for all disturbance levels and indicators analyzed. Furthermore, rehabilitation measures in artificial systems, such as reservoirs, could result in a higher biodiversity, biological quality and water quality, also in the least disturbed sites. Thus, this approach has the potential to provide information on management and conservation measures, since the simultaneous predictions of many taxa can be used, including: mapping the distribution of species; monitor the population dynamics of species at risk of extinction; and determining the best rehabilitation measures to improve disturbed ecosystems, allowing the determination of the most cost‐effective restoration measures.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Rehabilitation scenarios for reservoirs: Predicting their effect on invertebrate communities through machine learning

Azevêdo

Bezerra‐Neto

Molozzi

et al. 2020

River Research & Apps

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“…, Feio et al. , b, Rose et al. , b), or the approach applied apparently independently (e.g., Hargett et al.…”

Section: Introductionmentioning

confidence: 99%

Dangerous relationships: biases in freshwater bioassessment based on observed to expected ratios

Hämäläinen

Aroviita

Jyväsjärvi

et al. 2018

Ecological Applications

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The ecological assessment of freshwaters is currently primarily based on biological communities and the reference condition approach (RCA). In the RCA, the communities in streams and lakes disturbed by humans are compared with communities in reference conditions with no or minimal anthropogenic influence. The currently favored rationale is using selected community metrics for which the expected values (E) for each site are typically estimated from environmental variables using a predictive model based on the reference data. The proportional differences between the observed values (O) and E are then derived, and the decision rules for status assessment are based on fixed (typically 10th or 25th) percentiles of the O/E ratios among reference sites. Based on mathematical formulations, illustrations by simulated data and real case studies representing such an assessment approach, we demonstrate that the use of a common quantile of O/E ratios will, under certain conditions, cause severe bias in decision making even if the predictive model would be unbiased. This is because the variance of O/E under these conditions, which seem to be quite common among the published applications, varies systematically with E. We propose a correction method for the bias and compare the novel approach to the conventional one in our case studies, with data from both reference and impacted sites. The results highlight a conceptual issue of employing ratios in the status assessment. In some cases using the absolute deviations instead provides a simple solution for the bias identified and might also be more ecologically relevant and defensible.

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Spatial correlation of macroinvertebrate assemblages in streams and the implications for bioassessment programs

Shupryt

Studinski

2021

Environ Monit Assess

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Testing a multiple machine learning tool (HYDRA) for the bioassessment of fresh waters

Cited by 9 publications

References 45 publications

Rehabilitation scenarios for reservoirs: Predicting their effect on invertebrate communities through machine learning

Rehabilitation scenarios for reservoirs: Predicting their effect on invertebrate communities through machine learning

Dangerous relationships: biases in freshwater bioassessment based on observed to expected ratios

Spatial correlation of macroinvertebrate assemblages in streams and the implications for bioassessment programs

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