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Drought forecasting using an aggregated drought index and artificial neural networks

Barua, Shishutosh and Perera, B. J. C and Ng, A. W. M and Tran, D. H (2010) Drought forecasting using an aggregated drought index and artificial neural networks. Journal of water and climate change, 1 (3). pp. 193-206. ISSN 2040-2244

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Abstract

Early forecasting of future drought conditions during continuing dry periods can improve water resources management strategies. In this study, a drought forecasting approach is developed and presented using an aggregated drought index (ADI) and artificial neural network (ANN) using a monthly time step. The use of ADI forecasts the overall availability of water resources beyond the traditional forecasting of rainfall deficiency to represent future drought conditions. The paper compares two types of ANN; namely, recursive multi-step neural networks (RMSNN) and direct multi-step neural networks (DMSNN). The results show that the RMSNN approach is slightly better than the DMSNN approach for forecasts with lead time up to 3 months. The DMSNN approach gives slightly better results than the RMSNN approach when forecast lead time is over 3 months, and can give reasonable results up to 6 months ahead of forecasts.

Item Type: Article
Uncontrolled Keywords: ResPubID21193. aggregated drought index, artificial neural network, drought, drought forecasting, water resources management, Yarra River catchment
Subjects: Faculty/School/Research Centre/Department > School of Engineering and Science
FOR Classification > 0905 Civil Engineering
SEO Classification > 9004 Water and Waste Services
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Depositing User: VUIR
Date Deposited: 16 May 2011 05:20
Last Modified: 19 May 2014 02:27
URI: http://vuir.vu.edu.au/id/eprint/6748
DOI: 10.2166/wcc.2010.000
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Citations in Scopus: 3 - View on Scopus

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