A taxonomy-free approach based on machine learning to assess the quality of rivers with diatoms

Benthic Diatoms in River Biomonitoring—Present and Future Perspectives within the Water Framework Directive

“…Taxonomy-free indices presented similar results concerning water quality assessment when compared to taxonomy-based indices, highlighting their potential in routine biomonitoring [108,113]. However, they were only tested in small datasets from France [108], Switzerland [113] and Portugal [119], and they were directly related to environmental gradients of those datasets. Comparisons between quality classes determined by taxonomy-based and molecular-based indices showed an agreement of over 65% in French rivers [120].…”

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

“…The success of the model was probably due to the high accuracy obtained for each taxon (>0.7 with several >0.9), as a result, of the selection of the most accurate of the three techniques available for each taxon , unlike with the single modelling approach. Powerful bioassessment tools based on machine learning techniques have significantly advanced all over the world (e.g., Feio et al, 2014a, 2014b; Feio et al, 2020; Mayfield et al, 2017; Rose et al, 2016; Sarrazin‐Delay et al, 2014; Tamvakis et al, 2014). However, to our knowledge, the use of simultaneous multiple machine learning techniques for simulations of environmental improvement has not been attempted.…”

“…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).…”

“…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).…”

The use of machine learning predictive models to assess rivers quality with molecular data

“…One alternative to the grouping step inherent to the typological approach, and that may be viewed as artificial (nature is a continuum), is the prediction of site-specific reference conditions based on abiotic characteristics of sites. Thus, we tested a combination of machine-learning modelling techniques to build a taxonomic-free site-specific index to assess rivers based on diatom assemblages, from 81 ‡ sites located in Portugal (Feio et al (2020)Feio et al 2020. The models are trained to predict diatom OTUs expected under reference conditions, from environmental data.…”