Drought forecasting using an aggregated drought index and artificial neural networks

Full text for this resource is not available from the Research Repository.

Barua, Shishutosh, Perera, B. J. C, 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

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.

Dimensions Badge

Altmetric Badge

Item type Article
URI https://vuir.vu.edu.au/id/eprint/6748
DOI https://doi.org/10.2166/wcc.2010.000
Official URL http://dx.doi.org/10.2166/wcc.2010.000
Subjects Historical > Faculty/School/Research Centre/Department > School of Engineering and Science
Historical > FOR Classification > 0905 Civil Engineering
Historical > SEO Classification > 9004 Water and Waste Services
Keywords ResPubID21193. aggregated drought index, artificial neural network, drought, drought forecasting, water resources management, Yarra River catchment
Citations in Scopus 24 - View on Scopus
Download/View statistics View download statistics for this item

Search Google Scholar

Repository staff login