Study on orthogonal basis NN-based storage modelling for Lake Hume of Upper Murray River, Australia

Li, Ying and Li, Yan and Wang, Xiaofen (2014) Study on orthogonal basis NN-based storage modelling for Lake Hume of Upper Murray River, Australia. In: 13th International Conference on Machine Learning and Cybernetics , July, 13-16, 2014, Langzhou, China.

Abstract

The Murray-Darling Basin is Australia's most iconic and the largest catchment. It is also one of the largest river systems in the world and one of the driest. For managing the sustainable use of the Basin's water, hydrological modelling plays important role. The main models in use are the mathematical represented models which are difficult of containing full relationship between rainfall runoff, flow routing, upstream storage, evaporation and other water losses. Hume Reservoir is the main supply storage and one of the two major headwater storages for the River Murray system. It is crucial in managing flows and securing water supplies along the entire River Murray System, including Adelaide. In this paper, two Orthogonal Basis NN-Based storage models for Hume Reservoir are developed by using flow data from upstream gauge stations. One is only considering flow data from upstream gauge stations. Another is considering both upstream flow data and rainfall. The Neural Network (NN) learning algorithm is based on Ying Li's previous research outcome. The modelling results proved that the approach has high accuracy, good adaptability and extensive applicability.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: © Springer-Verlag Berlin Heidelberg 2014. Permanent restricted access to published version due to publisher copyright policy.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 07 May 2015 04:46
Last Modified: 01 Aug 2017 23:51
Uncontrolled Keywords: neural network; modelling; orthogonal basis transfer function; water storage; Murray River; Australia
Fields of Research : 09 Engineering > 0907 Environmental Engineering > 090702 Environmental Engineering Modelling
04 Earth Sciences > 0406 Physical Geography and Environmental Geoscience > 040608 Surfacewater Hydrology
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080110 Simulation and Modelling
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: 10.1007/978-3-662-45652-1_43
URI: http://eprints.usq.edu.au/id/eprint/26753

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