By Robert J. Abrahart, Linda M. See, Dimitri P. Solomatine
Hydroinformatics is an rising topic that's anticipated to assemble pace, momentum and significant mass during the coming near near a long time of the twenty first century. This ebook offers a extensive account of diverse advances in that box - a speedily constructing self-discipline overlaying the appliance of data and verbal exchange applied sciences, modelling and computational intelligence in aquatic environments. a scientific survey, labeled in accordance with the tools used (neural networks, fuzzy common sense and evolutionary optimization, particularly) is obtainable, including illustrated sensible purposes for fixing quite a few water-related matters. ...
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Additional info for Practical Hydroinformatics: Computational Intelligence and Technological Developments in Water Applications (Water Science and Technology Library)
G. filtering, smoothing, compression. Zhang et al. (2004b) highlight several advantages related to the use of such tools in environmental science and engineering. The power to represent a set of complex nonlinear relationships that are contained in an input–output data set, but without the need for a priori knowledge on the exact nature of such relationships, is a major factor. The power of such tools to generalise to unseen data sets is another. Several other properties distinguish neural solutions from standard algorithmic or rulebased approaches.
It is based on statistical learning theory initiated by V. Vapnik in the 1970s (Vapnik, 1998). This classification method has also been extended to solving prediction problems, and in this capacity was used in hydrology-related tasks. Dibike et al. (2001) and Liong and Sivapragasam (2002) reported using SVMs for flood management and in prediction of water flows and stages. Chapter 26 by Yu et al. provides a recent example of flood stage forecasting using SVM. 1 Modular Models Since natural processes are complex, it is sometimes not possible to build a single global model that adequately captures the system behaviour.
Journal of American Water Resources Association 38(1): 173–186. Lobbrecht AH, Solomatine DP (1999) Control of water levels in polder areas using neural networks and fuzzy adaptive systems. In: Savic D, Walters G (eds) Water Industry Systems: Modelling and Optimization Applications. , Baldock, pp. 509–518. Minns AW, Hall MJ (1996) Artificial neural network as rainfall-runoff model. Hydrological Sciences Journal 41(3): 399–417. Mitchell TM (1997) Machine Learning. McGraw-Hill: New York. Pesti G, Shrestha BP, Duckstein L, Bog´ardi I (1996) A fuzzy rule-based approach to drought assessment.