nach oben

2006 | Buch

Kapitel lesen Erstes Kapitel lesen

Ecological Informatics

Scope, Techniques and Applications

herausgegeben von: Associate Professor Friedrich Recknagel

Verlag: Springer Berlin Heidelberg

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

Einloggen, um Zugang zu erhalten

Über dieses Buch

Ecological Informatics promotes interdisciplinary research between ecology and computer science on elucidation of principles of information processing in ecosystems, ecological sustainability by informed decision making, and bio-inspired computation. The 2nd edition of the book consolidates the scope, concepts, and techniques of this newly emerging discipline by a new preface and additional chapters on cellular automata, qualitative reasoning, hybrid evolutionary algorithms and artificial neural networks. It illustrates numerous applications of Ecological Informatics for aquatic and terrestrial ecosystems, image recognition at micro- and macro-scale as well as computer hardware design.

Case studies focus on applications of artificial neural networks, evolutionary computation, cellular automata, adaptive agents, fuzzy logic as well as qualitative reasoning.

The 2nd edition of the book includes an index with novel evolutionary algorithms for the discovery of multiple nonlinear functions and rule sets as well as parameter optimisation in complex ecological data.

Inhaltsverzeichnis

Frontmatter

Introduction

Frontmatter

Chapter 1. Ecological Applications of Fuzzy Logic

1.6 Conclusions

Heterogeneous and imprecise ecological data and vague expert knowledge can be integrated more effectively using fuzzy approach. Fuzzy logic provides the means to combine numerical data and linguistic statements and to process both of them in one simulation step. Fuzzy sets with no sharply defined boundaries reflect better the continuous character of nature. The number of applications of fuzzy sets and fuzzy logic in ecological modelling and data analysis is constantly growing.

There also are an increasing number of applications of hybrid systems which combine the fuzzy techniques with other techniques, e.g. probabilistic approach, linear programming, neural networks, cellular automata or GIS technique. An increasing interest in the development of fuzzy expert systems for environmental management and engineering can also be expected.

A. Salski

Chapter 2. Ecological Applications of Qualitative Reasoning

2.6 Conclusion

Representing qualitative ecological knowledge is of great interest for ecological modelling. QR provides means to build conceptual models and to make qualitative knowledge explicit, organized and manageable by means of symbolic computing. This chapter discusses the main characteristics of QR using well-known examples. It also shows how this technology can be used to represent ecological knowledge and an overview is given of ecological applications that have already been developed using QR. Ongoing QR research focuses on improving QR tools and technology. An additional goal is to integrate quantitative knowledge with qualitative knowledge. In a collaborative work with ecologists, particularly in the construction of reusable knowledge libraries, it is possible to foresee a wider range of applications to ecological modelling and better ways of dealing with the complexity of ecological and environmental systems. But most of all, the deployment of QR technology for ecological purposes should become an important goal in itself because, as pointed out by (Rykiel 1989), “many questions of interest in ecology can be answered in terms of ‘better or worse’, ‘more or less’, ‘sooner or later’, etc.” and when quantitative methods are inadequate or lacking, it is still possible to make estimates, predictions, and decisions with scientific support.

B. Bredeweg, P. Salles, M. Neumann

Chapter 3. Ecological Applications of Non-supervised Artificial Neural Networks

3.5 Conclusion

We presented in this paper some ways to use SOMs for visualizing an abundance dataset. Due to its extreme adaptability, the SOM can have a number of variants that make it a very convenient tool for studying the ecological communities.

The SOM enhanced by the U-matrix method is an effective clustering method including techniques to display the species abundance or abiotic variables.

The SOM is a promising approach and completes the results obtained by classical methods of classification.

J. L. Giraudel, S. Lek

Chapter 4. Ecological Applications of Genetic Algorithms

D. Morrall

Chapter 5. Ecological Applications of Evolutionary Computation

5.5 Conclusion

The previous sections have described some of the basic applications of evolutionary computation techniques to various aspects of ecological modelling. Although there are many areas that have not been given adequate attention, it is clear that the use of difference and differential equations, the modelling of cooperation and community structure, the use of space and spatial behavior and the construction of hierarchical organization are areas where evolutionary computation techniques match well with ecological modelling. Models from large-scale behavior of communities, through to the way in which genetic material evolves in a species, can be studied using these types of models. The future is extremely positive for these evolutionary techniques to support and extend the current understanding of ecological processes and functions.

P. A. Whigham, G. B. Fogel

Chapter 6. Ecological Applications of Adaptive Agents

6.5 Conclusions

Adaptive agents (AA) provide a realistic framework for ecosystem simulation, evolving ecosystem structures and behaviours by emerging, submerging, interacting and evolving ecological entities.

Individual-based AA prove applicable to a spatially explicit simulation of highly simplified terrestrial food webs.

State variable-based AA where evolutionary computation is embodied appear to be relevant for simulations of aquatic food webs dynamics and plankton species interactions.

Embodiment of evolutionary computation in adaptive agents for aquatic species or functional groups can be achieved by evolving predictive rules (ER), differential equations (EDE) or artificial neural networks (ANN) from a diverse lake database.

Ecosystem simulation by state variable-based adaptive agents gains resilience to environmental change from an agent bank providing alternative agents for same species or functional groups evolved from a diverse lake database.

The presented concepts are currently tested by means of a multivariate time-series database for nine lakes different in climate, eutrophication and morphology.

F. Recknagel

Chapter 7. Bio-Inspired Design of Computer Hardware by Self-Replicating Cellular Automata

G. Tempesti, D. Mange, A. Stauffer, E. Petraglio

Prediction and Elucidation of Stream Ecosystems

Frontmatter

Chapter 8. Development and Application of Predictive River Ecosystem Models Based on Classification Trees and Artificial Neural Networks

P. Goethals, A. Dedecker, W. Gabriels, N. De Pauw

Chapter 9. Modelling Ecological Interrelations in Running Water Ecosystems with Artificial Neural Networks

I. M. Schleiter, M. Obach, R. Wagner, H. Werner, H. -H. Schmidt, D. Borchardt

Chapter 10. Non-linear Approach to Grouping, Dynamics and Organizational Informatics of Benthic Macroinvertebrate Communities in Streams by Artificial Neural Networks

T. -S. Chon, Y. S. Park, I. -S. Kwak, E. Y. Cha

Chapter 11. Elucidation of Hypothetical Relationships between Habitat Conditions and Macroinvertebrate Assemblages in Freshwater Streams by Artificial Neural Networks

11.5 Conclusions

The sensitivity analyses by means of validated ANN models can contribute to improved understanding of the ecology of streams and rivers. The interpretation of resulting sensitivity curves may reveal impacts of environmental conditions on the occurrence of macroinvertebrate taxa. Such additional knowledge can be useful for the bioindication of stream habitats by means of macroinvertebrate assemblages, and enhance our capacity to monitor and mitigate stream ecosystems. The shape of the sensitivity curves of taxa would indicate how important it is to manage disturbances within certain bounds in order to maintain healthy aquatic ecosystems. Taxa with a threshold response to a disturbance appear to be eliminated at a stream site that proves to be beyond a certain disturbance level. Taxa with ramp responses would gradually become rarer as disturbance intensified. The identification of such threshold conditions would provide catchment and water resource managers with a powerful tool.

Overall it can be concluded that ANN provide a powerful tool for stream modelling allowing the user not only to achieve highly accurate predictions but discover information on general trends in the data. Therefore, this methodology can efficiently be applied to determine ecological requirements of stream organisms that are not fully understood.

H. Hoang, F. Recknagel, J. Marshall, S. Choy

Prediction and Elucidation of River Ecosystems

Frontmatter

Chapter 12. Prediction and Elucidation of Population Dynamics of the Blue-green Algae Microcystis aeruginosa and the Diatom Stephanodiscus hantzschii in the Nakdong River-Reservoir System (South Korea) by a Recurrent Artificial Neural Network

12.6 Conclusions

Artificial neural networks were applied to the prediction and elucidation of two bloom forming algal species in the Nakdong river-reservoir system. The lower Nakdong River, which has characteristics of both rivers and reservoirs, represents a complicated system for algal bloom modeling. Yet, RNN proved capable not only to predict the distinct seasonal abundance and succession of Microcystis aeruginosa and Stephanodiscus hantzschii but elucidate key driving variables by means of sensitivity analyses. Findings of the sensitivity analysis corresponded very well with existing theories on the ecology of these two algae species.

This study yields promising results for the application of machine learning to complex ecosystems such as regulated rivers. It encourages inter-disciplinary research between ecologists, modelers and computer scientists in the newly emerging area of ecological informatics in order to better understand and predict ecological phenomena at different levels of organization.

K. -S. Jeong, F. Recknagel, G. -J. Joo

Chapter 13. An Evaluation of Methods for the Selection of Inputs for an Artificial Neural Network Based River Model

G. J. Bowden, G. C. Dandy, H. R. Maier

Chapter 14. Utility of Sensitivity Analysis by Artificial Neural Network Models to Study Patterns of Endemic Fish Species

14.6 Conclusions

The results obtained with both methods match closely with the previous results. The predictive power of ANNs has often been demonstrated, and this new study puts to the fore their explicative power which is very interesting in ecological research.

This article paves the way forward for broad research concerning the contribution of the input variables in ANN’s, firstly by the use of other databases to test the methods, secondly by the discovery of new methods and finally by the investigation of other existing methods.

M. Gevrey, S. Lek, T. Oberdorff

Prediction and Elucidation of Lake and Marine Ecosystems

Frontmatter

Chapter 15. A Comparison between Neural Network Based and Multiple Regression Models for Chlorophyll-a Estimation

C. Karul, S. Soyupak

Chapter 16. Artificial Neural Network Approach to Unravel and Forecast Algal Population Dynamics of Two Lakes Different in Morphometry and Eutrophication

16.5 Conclusions

The current study has demonstrated that complex limnological time-series data can beneficially be processed by ANN in order to provide: (1) one-week-ahead forecasting of outbreaks of harmful algae or water quality changes by recurrent supervised ANN, and (2) clusters to unravel ecological relationships regarding seasons, water quality ranges and long-term environmental changes by non-supervised ANN. It has also been shown that these methods provide a useful framework for comparative studies between largely different lakes. Future work will focus on the integration of super- and non-supervised ANN into a representative lake data warehouse archiving long-term time-series of a broad range of lakes and rivers reflecting diverse climate, morphometric and eutrophic conditions. It will further facilitate “basic research on complex interactions (that) will lead to explanations for the variability and unpredictability that presently hamper lake management efforts...” Carpenter (1988).

F. Recknagel, A. Welk, B. Kim, N. Takamura

Chapter 17. Hybrid Evolutionary Algorithm for Rule Set Discovery in Time-Series Data to Forecast and Explain Algal Population Dynamics in Two Lakes Different in Morphometry and Eutrophication

17.4 Conclusions

A hybrid evolutionary algorithm (HEA) has been developed to discover predictive rule sets in complex ecological data. It has been designed to evolve the structure of rule sets by using genetic programming and to optimise the random parameters in the rule sets by means of a genetic algorithm.

HEA was successfully applied to long-term monitoring data of the shallow, eutrophic Lake Kasumigaura (Japan) and the deep, mesotrophic Lake Soyang (Korea). The results have demonstrated that HEA is able to discover rule sets, which can forecast for 7-days-ahead seasonal abundances of blue-green algae and diatom populations in the two lakes with relatively high accuracy but are also explanatory for relationships between physical, chemical variables and the abundances of algal populations. The explanations and the sensitivity analysis for the best rule sets correspond well with theoretical hypotheses and experimental findings in previous studies.

H. Cao, F. Recknagel, B. Kim, N. Takamura

Chapter 18. Multivariate Time Series Prediction of Marine Zooplankton by Artificial Neural Networks

C. H. Reick, A. Grünewald, B. Page

Chapter 19. Classification of Fish Stock-Recruitment Relationships in Different Environmental Regimes by Fuzzy Logic with Bootstrap Re-sampling Approach

D. G. Chen

Chapter 20. Computational Assemblage of Ordinary Differential Equations for Chlorophyll-a Using a Lake Process Equation Library and Measured Data of Lake Kasumigaura

20.4. Conclusions

The software LAGRAMGE for computational assemblage and adaptation of ODE by using the expert knowledge and measured data has been applied for the simulation of chl-a in Lake Kasumigaura. As a result two types of chl-a models were discovered: (1) chl-a equations without considering zooplankton grazing assembled and trained by data of consecutive years were data of the last year was used for testing, and (2) chl-a equations considering zooplankton grazing assembled and trained by data of the years 1986 to 1989. The test results of the different models have demonstrated that LAGRAMGE can discover ODE that allow to simulate chl-a in Lake Kasumigaura for a variety of years. However the generalisation of discovered equations for unseen data of consecutive years was unsatisfactory, and the accuracy of calculated trajectories with regards to timing and magnitudes of peak events was moderate. The results have highlighted the importance of nutrients as growth limiting factors, and the need for considering functional algae groups in order to appropriately represent their selective grazing by zooplankton.

N. Atanasova, F. Recknagel, L. Todorovski, S. Džeroski, B. Kompare

Classification of Ecological Images at Micro and Macro Scale

Frontmatter

Chapter 21. Identification of Marine Microalgae by Neural Network Analysis of Simple Descriptors of Flow Cytometric Pulse Shapes

21.5 Conclusions

The use of AFC pulse shape information does improve discrimination of microalgal taxa, and is likely to be even more useful when species that form chains are to be discriminated. The use of RBF ANNs was again shown to be a rapid and useful tool for analysing large sets of high dimensional data.

M. F. Wilkins, L. Boddy, G. B. J. Dubelaar

Chapter 22. Age Estimation of Fish Using a Probabilistic Neural Network

S. G. Robertson, A. K. Morison

Chapter 23. Pattern Recognition and Classification of Remotely Sensed Images by Artificial Neural Networks

23.5 Conclusions

Neural networks are powerful general purpose computing tools. They have become popular in the analysis of remotely sensed data, particularly for classification and regression-type problems in which they have often been demonstrated to extract information more accurately than conventional methods. Although not free from problems, it seems likely that neural networks will be used increasingly in ecological research using remote sensing. Moreover, as some of the problems encountered in use of neural networks arise from a tendency to focus upon the MLP only it is likely that there will be a greater use of other network types. In addition, it is expected that the range of applications of neural networks in remote sensing will broaden. Applications in which neural networks have already been used and increased usage may be expected include: image preprocessing (e.g. geometric, atmospheric and radiometric correction), stereo-matching imagery, image compression, feature extraction, map generalisation, multi-source data analysis, data fusion and image sharpening (e.g. Day, 1997; Foody, 1999a). Thus while neural networks have rapidly become established in remote sensing it is likely that they will be used increasingly and in a broader range of activities that will help exploit more fully the potential of remote sensing as a useful tool in ecological research.

G. M. Foody

Backmatter

Titel: Ecological Informatics
herausgegeben von: Associate Professor Friedrich Recknagel
Verlag: Springer Berlin Heidelberg
Electronic ISBN: 978-3-540-28426-0
Print ISBN: 978-3-540-28383-6
DOI: https://doi.org/10.1007/3-540-28426-5