Predicting water allocation trade prices using a hybrid Artificial Neural Network-Bayesian modelling approach

Nguyen-Ky, Tai and Mushtaq, Shahbaz and Loch, Adam and Reardon-Smith, Kate and An-Vo, Duc-Anh and 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. ISSN 0022-1694

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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.

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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Accepted version deposited in accordance with the copyright policy of the publisher.
Faculty / Department / School: No Faculty
Date Deposited: 13 Dec 2017 03:05
Last Modified: 12 Mar 2019 02:41
Uncontrolled Keywords: water allocation prices; Artificial Neural Network model; Hybrid Artificial Neural Network-Bayesian model; water trade; price prediction
Fields of Research : 07 Agricultural and Veterinary Sciences > 0701 Agriculture, Land and Farm Management > 070199 Agriculture, Land and Farm Management not elsewhere classified
14 Economics > 1403 Econometrics > 140303 Economic Models and Forecasting
Identification Number or DOI: 10.1016/j.jhydrol.2017.11.049

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