Support vector machine applications in the field of hydrology: A review

• Published in: Applied soft computing Vol. 19; pp. 372 - 386
• Main Authors: Raghavendra. N, Sujay, Deka, Paresh Chandra
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.06.2014
• Subjects: Hydrological models; Optimization theory; Statistical learning; Support vector machines; Support vector machines; Optimization theory; Hydrological models; Statistical learning
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2014.02.002

[Display omitted] •Basics of SVMs theory are discussed.•Applications of SVMs in various hydrological problems are reviewed.•Hybrid SVM models are also dealt.•Advantages and disadvantages of SVMs are surveyed.•Future directions of research using SVMs are suggested. In the recent few decades there has been very significant developments in the theoretical understanding of Support vector machines (SVMs) as well as algorithmic strategies for implementing them, and applications of the approach to practical problems. SVMs introduced by Vapnik and others in the early 1990s are machine learning systems that utilize a hypothesis space of linear functions in a high dimensional feature space, trained with optimization algorithms that implements a learning bias derived from statistical learning theory. This paper reviews the state-of-the-art and focuses over a wide range of applications of SVMs in the field of hydrology. To use SVM aided hydrological models, which have increasingly extended during the last years; comprehensive knowledge about their theory and modelling approaches seems to be necessary. Furthermore, this review provides a brief synopsis of the techniques of SVMs and other emerging ones (hybrid models), which have proven useful in the analysis of the various hydrological parameters. Moreover, various examples of successful applications of SVMs for modelling different hydrological processes are also provided.