The DOI matches its Crossref record; 92 of the 92 references it cites were found in a registry or an index.
Artificial Intelligence techniques: An introduction to their use for modelling environmental systems
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Citations
Cites
Kennedy, J., & Eberhart, R. (2002) · Proceedings of ICNN'95 - International Conference on Neural Networks · doi:10.1109/icnn.1995.488968 · Cited by 58,192
Genetic algorithms in search, optimization, and machine learning
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Reinforcement Learning: An Introduction
(2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 24,496
Ant system: optimization by a colony of cooperating agents
Dorigo, M., Maniezzo, V., & Colorni, A. (1996) · IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) · doi:10.1109/3477.484436 · Cited by 12,067
Goldberg, D. E. (1989) · Medical Entomology and Zoology · Cited by 10,109
Reinforcement Learning: A Survey
Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996) · Journal of Artificial Intelligence Research · doi:10.1613/jair.301 · Cited by 8,972
Ant colony system: a cooperative learning approach to the traveling salesman problem
Dorigo, M., & Gambardella, L. M. (1997) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.585892 · Cited by 8,081
Introduction to Reinforcement Learning
Sutton, R. S., & Barto, A. G. (1998) · The MIT Press eBooks · Cited by 6,855
Swarm Intelligence: From Natural to Artificial Systems
Bonabeau, E., Dorigo, M., & Theraulaz, G. (1999) · Oxford University Press eBooks · doi:10.1093/oso/9780195131581.001.0001 · Cited by 6,435
Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches
Aamodt, A., & Plaza, E. (1994) · AI Communications · doi:10.3233/aic-1994-7104 · Cited by 5,624
Cited by
Machine learning and deep learning
Janiesch, C., Zschech, P., & Heinrich, K. (2021) · Electronic Markets · doi:10.1007/s12525-021-00475-2 · Cited by 2,959
Artificial Intelligence in Supply Chain Management: A Systematic Literature Review
Toorajipour, R., Sohrabpour, V., Nazarpour, A., Oghazi, P., & Fischl, M. (2021) · Journal of Business Research · doi:10.1016/j.jbusres.2020.09.009 · Cited by 1,020
“This is mainly due to their capability of solving data-intensive problems in which the rules or algorithms for solving the problem are unknown or difficult to express (Chen et al., 2008).”
Good practice in Bayesian network modelling
Chen, S. H., & Pollino, C. A. (2012) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2012.03.012 · Cited by 657
Self-citationSousa, W. G. D., Melo, E. R. P. D., Bermejo, P. H. D. S., Farias, R. A. S., & Gomes, A. O. (2019) · Government Information Quarterly · doi:10.1016/j.giq.2019.07.004 · Cited by 501
A review of artificial neural network models for ambient air pollution prediction
Cabaneros, S. M., Calautit, J. K., & Hughes, B. R. (2019) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2019.06.014 · Cited by 459
Metadata
- Authors
- Chen, S. H., Jakeman, A. J., & Norton, J. P.
- Venue
- Mathematics and Computers in Simulation
- Publisher
- Elsevier BV
- Published
- 2008-07
- Volume
- 78
- Issue
- 2-3
- Pages
- 379-400
- ISSN
- 0378-4754 (ISSN-L)
- OpenAlex
- W2106577732
- Cited by
- 323
- References
- 92
- FWCI
- 22.26
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Artificial Intelligence techniques: An introduction to their use for modelling environmental systems. https://app.cochat.ai/papers/10.1016/j.matcom.2008.01.028
- The paper
- Chen, S. H., Jakeman, A. J., & Norton, J. P. (2008). Artificial Intelligence techniques: An introduction to their use for modelling environmental systems. Mathematics and Computers in Simulation. https://doi.org/10.1016/j.matcom.2008.01.028
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
