Modelling Ecological Interrelations in Running Water Ecosystems with Artificial Neural Networks

Schleiter, Ingrid M.; Obach, Michael; Wagner, Rüdiger; Werner, Heinrich; Schmidt, Hans-Heinrich; Borchardt, Dietrich

doi:10.1007/978-3-662-05150-4_7

Cited by 3 publications

(4 citation statements)

References 15 publications

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“…Measuring frequency of occurrence of taxa [52]. Modeling and prediction of the richness of ecological and environmental data [82] through a selection of important predictors, information compression [45,96], and self-organization [47,48]. Data reduction through hierarchical clustering [38,42].…”

Section: Classifiers (C)mentioning

confidence: 99%

“…On the other hand, regarding the most employed algorithms in water quality assessment, one finds artificial neural networks (ANNs) (Figure 7), mainly Kohonen Self-Organizing Maps (SOM). Primarily, they were implemented for decreasing data complexity [11,42]; modeling, visualization, and prediction of ecological data to FwM community dynamics [38,[44][45][46][47][48][49]; recognizing patterns in complex spatio-temporal data [50]; establishing the relationship between benthic fauna and biological, environmental parameters [51]; and determining of frequency of occurrence of taxa related to physical or chemical variables [52]. The second group, concentrated between 2010 and 2015, has widely used machine learning to classify species or families.…”

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confidence: 99%

“…Based on the results obtained from the relationship between these application areas and the machine learning algorithms (Figure 8), ANNs are the most widely used algorithms as a modeling method [38,[44][45][46][47][48][49]. However, they are more relevant for the measurement of relative abundance.…”

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confidence: 99%

“…The use of black-box approaches such as multilayer ANNs has low desirable interpretive power to gain insight into the relationships elicited by ecological phenomena [84], to detect causalities inherent in the data [45], or because not all pollution-related factors are evaluated [48]. Comparison of different methods using computational technologies' relative accuracy in FwM studies is challenging because they depend on the data sampling, frequency of occurrence of taxa, region, and types of streams [23,73].…”

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confidence: 99%

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Computer-Assisted Bioidentification Using Freshwater Macroinvertebrates: A Scoping Review

Cruz-Cruz

López

Vargas-Cañas

et al. 2022

Water

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Background: Evaluation and prediction of the freshwater status based on freshwater macroinvertebrates (FwM) has become valuable in bioindication because they provide a more general and accurate picture of the ecological status of water bodies over time. Recent research on bioindication through FwM has increased the use of computational technologies, mainly in the classification and data analysis stages of water quality assessment and prediction. Objective: This scoping review aims to provide an overview of different approaches in computer-assisted bioindication with FwM. Particularly, the objective is to identify the techniques and strategies employed for FwM automatic classification or data treatment, characterize their use in recent years, and discuss gaps and challenges to broaden the scope of bioindication as a tool for understanding real conditions in a water body. Design: The scoping review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) extension for scoping reviews (ScR). Scopus and Web of Science databases were used to identify articles published between 1999 and 2022. We selected 81 publications that used computational technology for automatic FwM classification or data analysis to predict water quality using biological indices. Results and conclusions: We identified two areas of applying computational technologies in bioindication studies with FwM. Firstly, computer-assisted technologies are used to evaluate water quality through samples already classified by human experts which correspond to 57% of the documents analyzed. The second application area is the automatic classification of FwM. In addition, we determined the main critical factors affecting strategy selection in each of the studies, such as taxonomic resolution, sample size and quality, image quality, data size, and complexity. Finally, we established the relationship between the strategies and algorithms employed in a timeline for automatic classification according to available FwM image databases. The research will allow taxonomic and related experts to better understand the role of computational technologies in FwM studies and thus increase confidence in these techniques to extend their use in bioassessment tasks.

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Section: Classifiers (C)mentioning

confidence: 99%

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confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Computer-Assisted Bioidentification Using Freshwater Macroinvertebrates: A Scoping Review

Cruz-Cruz

López

Vargas-Cañas

et al. 2022

Water

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MODELING PHYTOPLANKTON ABUNDANCE IN SAGINAW BAY, LAKE HURON: USING ARTIFICIAL NEURAL NETWORKS TO DISCERN FUNCTIONAL INFLUENCE OF ENVIRONMENTAL VARIABLES AND RELEVANCE TO A GREAT LAKES OBSERVING SYSTEM¹

Millie

Weckman

Pigg

et al. 2006

Journal of Phycology

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Phytoplankton abundance, as chl a, in Saginaw Bay, Lake Huron was modeled using artificial neural networks. Suites of abiotic variables served as predictors for the trends/patterns in chl a concentrations. Spatial and temporal gradients of sampling stations throughout the bay were evident, with physical/chemical differences arising from hydrological/meteorological forcing and zebra mussel recruitment. Chlorophyll a concentrations displayed corresponding disparities; concentrations differed between the inner and outer bays and varied intra-and inter-annually. Trained networks reproduced the intrinsic variance and magnitude of chl a dynamics. Modeled-measured concentrations best approximated a 1:1 relationship in a hybrid network incorporating both supervised and unsupervised training whereas concentrations greater than 15 lg/L were underestimated in networks utilizing only supervised training, likely because of inadequate training data. Variables indicative of phytoplankton nutrition, acting as proxy measurements of algal biomass, and/or corresponding to descriptors of hydrological and meteorological forcing had the greatest influence upon modeled concentrations. A conjunctive decision tree and a novel sensitivity analysis provided rule-based information and comprehensible interpretation of relationships among multiple predictor variables. From this, the ''knowledge'' embedded in trained networks proved extractable and usable for ecological theory generation and/or decision making within water-quality problem solving. Forecasting initiatives within the developing Great Lakes Observing System may be best served by embedding neural networks in mechanistic models to quantitatively initialize variables, qualitatively delineate conditions for projecting ecological structure, and/or estimate deviations from predictability within mechanistic simulations.

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Ecological informatics by means of neural, evolutionary and object-oriented computation

Recknagel

Cao

2009

Handbook of Ecological Modelling and Informatics

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Modelling Ecological Interrelations in Running Water Ecosystems with Artificial Neural Networks

Cited by 3 publications

References 15 publications

Computer-Assisted Bioidentification Using Freshwater Macroinvertebrates: A Scoping Review

Computer-Assisted Bioidentification Using Freshwater Macroinvertebrates: A Scoping Review

MODELING PHYTOPLANKTON ABUNDANCE IN SAGINAW BAY, LAKE HURON: USING ARTIFICIAL NEURAL NETWORKS TO DISCERN FUNCTIONAL INFLUENCE OF ENVIRONMENTAL VARIABLES AND RELEVANCE TO A GREAT LAKES OBSERVING SYSTEM¹

Ecological informatics by means of neural, evolutionary and object-oriented computation

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Modelling Ecological Interrelations in Running Water Ecosystems with Artificial Neural Networks

Cited by 3 publications

References 15 publications

Computer-Assisted Bioidentification Using Freshwater Macroinvertebrates: A Scoping Review

Computer-Assisted Bioidentification Using Freshwater Macroinvertebrates: A Scoping Review

MODELING PHYTOPLANKTON ABUNDANCE IN SAGINAW BAY, LAKE HURON: USING ARTIFICIAL NEURAL NETWORKS TO DISCERN FUNCTIONAL INFLUENCE OF ENVIRONMENTAL VARIABLES AND RELEVANCE TO A GREAT LAKES OBSERVING SYSTEM1

Ecological informatics by means of neural, evolutionary and object-oriented computation

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About

MODELING PHYTOPLANKTON ABUNDANCE IN SAGINAW BAY, LAKE HURON: USING ARTIFICIAL NEURAL NETWORKS TO DISCERN FUNCTIONAL INFLUENCE OF ENVIRONMENTAL VARIABLES AND RELEVANCE TO A GREAT LAKES OBSERVING SYSTEM¹