From neural networks to qualitative models in environmental engineering

Computer-Aided Civil and Infrastructure Engineering

Volumn 17, Issue 2, 2002, Pages 104-118

  Wieland, Dominik ^a   Wotawa, Franz ^b   Wotawa, Gerhard ^c  

^a VIENNA UNIVERSITY OF TECHNOLOGY   (Austria)

^b GRAZ UNIVERSITY OF TECHNOLOGY   (Austria)

^c UNIVERSITY OF NATURAL RESOURCES AND LIFE SCIENCES   (Austria)

Author keywords

[No Author keywords available]

Indexed keywords

COSTS; DECISION SUPPORT SYSTEMS; ENVIRONMENTAL ENGINEERING; OZONE;

PHYSICAL MODELS;

NEURAL NETWORKS;

NEURAL NETWORK;

EID: 0036498453     PISSN: 10939687     EISSN: None     Source Type: Journal    
DOI: 10.1111/1467-8667.00259     Document Type: Article

References (40)

1. Neural network model for maximum ozone concentration prediction
2. Learning qualitative models of dynamic systems
3. An extended neuron model for efficient time series generation and prediction
4. Long term forecasting by combining kohonen algorithm and standard prevision
5. Qualitative reasoning: A survey of techniques and applications
6. Induction of discrete-state machine by stabilizing a simple recurrent network using clustering
7. Symbolic knowledge extraction from trained neural networks: A sound approach
8. A qualitative physics based on confluences
9. Neural network design based on evolutionary programming
10. Qualitative process theory
11. Learning and extracting finite state automata with second-order recurrent neural networks
12. Extraction, insertion and refinement of symbolic rules in dynamically-driven recurrent neural networks
13. Transformation of qualitative dynamic models- Application in hydro-ecology
14. Conceptual modeling in the environmental domain
15. Long short-term memory
16. Applying neural network models to prediction and data analysis in meteorology and oceanography
17. Neural networks and periodic components used in air quality forecasting
18. Qualitative simulation
19. Embracing causality in specifying the indirect effects of actions
20. Learning long-term dependencies is not as difficult with NARX recurrent neural networks
21. Methods and problems in data mining
22. Causal theories of action and change
23. Learning by experimentation: Acquiring and refining problem-solving heuristics
24. Inference of local rainfall using qualitative reasoning
25. Identification of linear and nonlinear dynamic systems using recurrent neural networks
26. Induction of decision trees
27. Learning causal networks from data: A survey and a new algorithm for recovering possibilistic causal networks
28. Three techniques for extracting rules from feed-forward networks
29. Symbolic interpretation of artificial neural networks
30. Rule extraction from recurrent neural networks using a symbolic machine learning algorithm
31. Incremental induction of topological minimal trees
32. Deriving qualitative rules from neural networks- A case study for ozone forecasting