The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
An evaluation framework for input variable selection algorithms for environmental data-driven models
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Cites
Approximation by superpositions of a sigmoidal function
Cybenko, G. (1989) · Mathematics of Control, Signals, and Systems · doi:10.1007/bf02551274 · Cited by 13,883
Ridge regression: Biased estimation for nonorthogonal problems
Hoerl, A. E., & Kennard, R. W. (1970) · Technometrics · doi:10.1080/00401706.1970.10488634 · Cited by 9,648
The Relationship Between Variable Selection and Data Agumentation and a Method for Prediction
Allen, D. M. (1974) · Technometrics · doi:10.1080/00401706.1974.10489157 · Cited by 1,469
Mac Nally, R. (2000) · Biodiversity & Conservation · doi:10.1023/a:1008985925162 · Cited by 938
Cited by
Maier, H. R., Kapelan, Z., Kasprzyk, J., Kollat, J., Matott, L. S., Cunha, M. C., Dandy, G. C., Gibbs, M. S., Keedwell, E., Marchi, A., et al. (2014) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2014.09.013 · Cited by 657
Self-citationThe Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support
Razavi, S., Jakeman, A., Saltelli, A., Prieur, C., Iooss, B., Borgonovo, E., Plischke, E., Lo Piano, S., Iwanaga, T., Becker, W., et al. (2021) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2020.104954 · Cited by 628
Self-citationA 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
Battle of the Attack Detection Algorithms: Disclosing Cyber Attacks on Water Distribution Networks
Taormina, R., Galelli, S., Tippenhauer, N. O., Salomons, E., Ostfeld, A., Eliades, D. G., Aghashahi, M., Sundararajan, R., Pourahmadi, M., Banks, M. K., et al. (2018) · Journal of Water Resources Planning and Management · doi:10.1061/(asce)wr.1943-5452.0000969 · Cited by 277
Self-citationMachine learning for streaming data: state of the art, challenges, and opportunities
Gomes, H. M., Read, J., Bifet, A., Barddal, J. P., & Gama, J. (2019) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3373464.3373470 · Cited by 267
Metadata
- Authors
- Galelli, S., Humphrey, G. B., Maier, H. R., Castelletti, A., Dandy, G. C., & Gibbs, M. S.
- Venue
- Environmental Modelling & Software
- Publisher
- Elsevier BV
- Published
- 2014-12
- Volume
- 62
- Pages
- 33-51
- ISSN
- 1364-8152 (ISSN-L)
- OpenAlex
- W2046935534
- Cited by
- 207
- References
- 4
- FWCI
- 12.37
- Type
- journal article
- Language
- en
- Open access
- green
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- The paper
- Galelli, S., Humphrey, G. B., Maier, H. R., Castelletti, A., Dandy, G. C., & Gibbs, M. S. (2014). An evaluation framework for input variable selection algorithms for environmental data-driven models. Environmental Modelling & Software. https://doi.org/10.1016/j.envsoft.2014.08.015
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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.
