Study on orthogonal basis NN-based storage modelling for Lake Hume of Upper Murray River, Australia
Paper
Paper/Presentation Title | Study on orthogonal basis NN-based storage modelling for Lake Hume of Upper Murray River, Australia |
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Presentation Type | Paper |
Authors | Li, Ying (Author), Li, Yan (Author) and Wang, Xiaofen (Author) |
Editors | Wang, Xizhao, Pedrycz, Witold, Chan, Patrick and He, Qiang |
Journal or Proceedings Title | Communications in Computer and Information Science |
ERA Conference ID | 43446 |
Journal Citation | 481, pp. 431-441 |
Number of Pages | 11 |
Year | 2014 |
Place of Publication | Berlin, Germany |
ISBN | 9783662456514 |
9783662456521 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-662-45652-1_43 |
Web Address (URL) of Paper | http://www.springer.com/computer/ai/book/978-3-662-45651-4 |
Conference/Event | 13th International Conference on Machine Learning and Cybernetics |
International Conference on Machine Learning and Cybernetics | |
Event Details | International Conference on Machine Learning and Cybernetics ICMLC Rank C C C C C C C C C C |
Event Details | 13th International Conference on Machine Learning and Cybernetics Event Location 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. |
Keywords | neural network; modelling; orthogonal basis transfer function; water storage; Murray River; Australia |
ANZSRC Field of Research 2020 | 401199. Environmental engineering not elsewhere classified |
370704. Surface water hydrology | |
460207. Modelling and simulation | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | School of Agricultural, Computational and Environmental Sciences |
Wuhan Sports University, China | |
Series | Communications in Computer and Information Science |
Institution of Origin | University of Southern Queensland |
Book Title | Machine learning and cybernetics: proceedings of the 13th International Conference Machine Learning and Cybernetics, Lanzhou, China, July 13-16, 2014. |
Chapter Number | 43 |
https://research.usq.edu.au/item/q2x58/study-on-orthogonal-basis-nn-based-storage-modelling-for-lake-hume-of-upper-murray-river-australia
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