The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Data-driven modelling: some past experiences and new approaches
Open accessAbstract
Physically based (process) models based on mathematical descriptions of water motion are widely used in river basin management. During the last decade the so-called data-driven models are becoming more and more common. These models rely upon the methods of computational intelligence and machine learning, and thus assume the presence of a considerable amount of data describing the modelled system's physics (i.e. hydraulic and/or hydrologic phenomena). This paper is a preface to the special issue on Data Driven Modelling and Evolutionary Optimization for River Basin Management, and presents a brief overview of the most popular techniques and some of the experiences of the authors in data-driven modelling relevant to river basin management. It also identifies the current trends and common pitfalls, provides some examples of successful applications and mentions the research challenges.
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Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
Classification And Regression Trees
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (2017) · doi:10.1201/9781315139470 · Cited by 11,417
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Tyralis, H., Papacharalampous, G., & Langousis, A. (2019) · Water · doi:10.3390/w11050910 · Cited by 771
A survey on river water quality modelling using artificial intelligence models: 2000–2020
Tiyasha, Tung, T. M., & Yaseen, Z. M. (2020) · Journal of Hydrology · doi:10.1016/j.jhydrol.2020.124670 · Cited by 650
Jiang, S., Zheng, Y., & Solomatine, D. (2020) · Geophysical Research Letters · doi:10.1029/2020gl088229 · Cited by 365
Self-citationMachine learning in natural and engineered water systems
Huang, R., Ma, C., Ma, J., Huangfu, X., & He, Q. (2021) · Water Research · doi:10.1016/j.watres.2021.117666 · Cited by 319
Prediction of rainfall time series using modular soft computingmethods
Wu, C. L., & Chau, K. W. (2013) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2012.05.023 · Cited by 310
Metadata
- Authors
- Solomatine, D. P., & Ostfeld, A.
- Venue
- Journal of Hydroinformatics
- Publisher
- IWA Publishing
- Published
- 2008-01-01
- Volume
- 10
- Issue
- 1
- Pages
- 3-22
- ISSN
- 1464-7141 (ISSN-L)
- 1465-1734
- OpenAlex
- W2082484980
- Cited by
- 723
- References
- 4
- FWCI
- 9.34
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Solomatine, D. P., & Ostfeld, A. (2008). Data-driven modelling: some past experiences and new approaches. Journal of Hydroinformatics. https://doi.org/10.2166/hydro.2008.015
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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.
