Ecological Informatics

Recknagel, Friedrich

doi:10.1007/3-540-28426-5

Cited by 29 publications

(2 citation statements)

References 192 publications

(279 reference statements)

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“…Statistical methods that have been used to support data exploration are numerous (Jain, Duin, and Mao 2000;Recknagel 2006;Zuur, Ieno, and Elphick 2010). A common theme among data intensive methods is the use of machine-learning algorithms where the primary objective is to identify emergent patterns with minimal human intervention.…”

Section: Introductionmentioning

confidence: 99%

NeuralNetTools: Visualization and Analysis Tools for Neural Networks

Beck¹

2018

J. Stat. Soft.

227

147

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Supervised neural networks have been applied as a machine learning technique to identify and predict emergent patterns among multiple variables. A common criticism of these methods is the inability to characterize relationships among variables from a fitted model. Although several techniques have been proposed to "illuminate the black box", they have not been made available in an open-source programming environment. This article describes the NeuralNetTools package that can be used for the interpretation of supervised neural network models created in R. Functions in the package can be used to visualize a model using a neural network interpretation diagram, evaluate variable importance by disaggregating the model weights, and perform a sensitivity analysis of the response variables to changes in the input variables. Methods are provided for objects from many of the common neural network packages in R, including caret, neuralnet, nnet, and RSNNS. The article provides a brief overview of the theoretical foundation of neural networks, a description of the package structure and functions, and an applied example to provide a context for model development with NeuralNetTools. Overall, the package provides a toolset for neural networks that complements existing quantitative techniques for data-intensive exploration.

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Section: Introductionmentioning

confidence: 99%

NeuralNetTools: Visualization and Analysis Tools for Neural Networks

Beck¹

2018

J. Stat. Soft.

227

147

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“…Ecological informatics (ecoinformatics) is an interdisciplinary framework for the understanding of information processing and transfer in ecology (Hogeweg, 2007) as well as management, analysis and synthesis of ecological data by advanced computational technology (Recknagel, 2006).…”

Section: Introductionmentioning

confidence: 99%

Current Scope, Case Studies and Future Directions of Ecological Informatics

Recknagel

2013

J ENV INFORM

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Ecological data management, analysis and synthesis as well as information processing and transfer in ecology are considered feature areas of ecological informatics. The paper presents case studies for analysis and synthesis of ecological data by means of unsupervised artificial neural networks and hybrid evolutionary algorithms to assist in computational bioindication of wetland water quality and early warning of cyanobacteria blooms in lakes and reservoirs. Integrated analysis and synthesis of ecological and genomics data, hybrid model libraries generic for ecosystem categories as well as internet-based data and model sharing are identified as key research areas of ecological informatics.

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Applications of artificial neural networks predicting macroinvertebrates in freshwaters

et al. 2007

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To facilitate decision support in freshwater ecosystem protection and restoration management, habitat suitability models can be very valuable. Data driven methods such as artificial neural networks (ANNs) are particularly useful in this context, seen their time-efficient development and relatively high reliability. However, specialized and technical literature on neural network modelling offers a variety of model development criteria to select model architecture, training procedure, etc. This may lead to confusion among ecosystem modellers and managers regarding the optimal training and validation methodology. This paper focuses on the analysis of ANN development and application for predicting macroinvertebrate communities, a species group commonly used in freshwater assessment worldwide. This review reflects on the different aspects regarding model development and application based on a selection of 26 papers reporting the use of ANN models for the prediction of macroinvertebrates. This analysis revealed that the applied model training and validation methodologies can often be improved and moreover crucial steps in the modelling process are often poorly documented. Therefore, suggestions to improve model development, assessment and application in ecological river management are presented. In particular, data pre-processing determines to a high extent the reliability of the induced models and their predictive relevance. This also counts for the validation criteria, that need to be better tuned to the practical simulation requirements. Moreover, the use of sensitivity methods can help to extract knowledge on the habitat preference of species and allow peer-review by ecological experts. The selection of relevant input variables remains a critical challenge as well. Model coupling is a missing crucial step to link human activities, hydrology, physical habitat conditions, water quality and ecosystem status. This last aspect is probably the most valuable aspect to enable decision support in water management based on ANN models.

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Ecological Informatics

Abstract: The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.

Cited by 29 publications

References 192 publications

NeuralNetTools: Visualization and Analysis Tools for Neural Networks

NeuralNetTools: Visualization and Analysis Tools for Neural Networks

Current Scope, Case Studies and Future Directions of Ecological Informatics

Applications of artificial neural networks predicting macroinvertebrates in freshwaters

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