Predicting water allocation trade prices using a hybrid Artificial Neural Network-Bayesian modelling approach
Article
Nguyen-Ky, Tai, Mushtaq, Shahbaz, Loch, Adam, Reardon-Smith, Kate, An-Vo, Duc-Anh, Ngo-Cong, Duc and Tran-Cong, Thanh. 2018. "Predicting water allocation trade prices using a hybrid Artificial Neural Network-Bayesian modelling approach." Journal of Hydrology. 567, pp. 781-791. https://doi.org/10.1016/j.jhydrol.2017.11.049
| Article Title | Predicting water allocation trade prices using a hybrid Artificial Neural Network-Bayesian modelling approach |
|---|---|
| ERA Journal ID | 1949 |
| Article Category | Article |
| Authors | Nguyen-Ky, Tai (Author), Mushtaq, Shahbaz (Author), Loch, Adam (Author), Reardon-Smith, Kate (Author), An-Vo, Duc-Anh (Author), Ngo-Cong, Duc (Author) and Tran-Cong, Thanh (Author) |
| Journal Title | Journal of Hydrology |
| Journal Citation | 567, pp. 781-791 |
| Number of Pages | 11 |
| Year | 2018 |
| Publisher | Elsevier |
| Place of Publication | Netherlands |
| ISSN | 0022-1694 |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.jhydrol.2017.11.049 |
| Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0022169417308144 |
| Abstract | This paper proposes an integrated (hybrid) Artificial Neural Network-Bayesian (ANN-B) modelling approach to improve the accuracy of predicting seasonal water allocation prices in Australia’s Murry Irrigation Area, which is part of one of the world’s largest interconnected water markets. Three models (basic, intermediate and full), accommodating different levels of data availability, were considered. Data were analyzed using both ANN and hybrid ANN-B approaches. Using the ANN-B modelling approach, which can simulate complex and non-linear processes, water allocation prices were predicted with a high degree of accuracy (RBASIC = 0.93, RINTER. = 0.96 and RFULL = 0.99); this was a higher level of accuracy than realized using ANN. This approach can potentially be integrated with online data systems to predict water allocation prices, enable better water allocation trade decisions, and improve the productivity and profitability of irrigated agriculture. |
| Keywords | water allocation prices; Artificial Neural Network model; Hybrid Artificial Neural Network-Bayesian model; water trade; price prediction |
| ANZSRC Field of Research 2020 | 300299. Agriculture, land and farm management not elsewhere classified |
| 380203. Economic models and forecasting | |
| Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
| Byline Affiliations | Computational Engineering and Science Research Centre |
| International Centre for Applied Climate Science | |
| University of Adelaide | |
| Institution of Origin | University of Southern Queensland |
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