Application of hybrid artificial neural network algorithm for the prediction of Standardized Precipitation Index
Paper
Paper/Presentation Title | Application of hybrid artificial neural network algorithm for the prediction of Standardized Precipitation Index |
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Presentation Type | Paper |
Authors | Dayal, Kavina S. (Author), Deo, Ravinesh C. (Author) and Apan, Armando A. (Author) |
Journal or Proceedings Title | 2016 IEEE Region 10 International Conference Proceedings (TENCON 2016) |
ERA Conference ID | 43057 |
Number of Pages | 5 |
Year | 2016 |
Place of Publication | Singapore |
Digital Object Identifier (DOI) | https://doi.org/10.1109/TENCON.2016.7848588 |
Web Address (URL) of Paper | http://ieeexplore.ieee.org/document/7848588/ |
Conference/Event | 2016 IEEE Region 10 International Conference: Technologies for Smart Nation (TENCON 2016) |
IEEE Tencon (IEEE Region 10 Conference) | |
Event Details | IEEE Tencon (IEEE Region 10 Conference) Tencon IEEE Region 10 Conference Rank C C C C C C C C C C C C C C C |
Event Details | 2016 IEEE Region 10 International Conference: Technologies for Smart Nation (TENCON 2016) Event Date 22 to end of 25 Nov 2016 Event Location Singapore |
Abstract | The application of wavelet transformation has become a popular area of interest in hydrological modeling as it enables the use of spectral and temporal information contained in input data. Drought modeling is one such area that is still far from complete, considering the stochastic nature of drought characteristics per every drought events. This study therefore aims to predict a drought index, i.e. the Standardized Precipitation Index (SPI), using artificial neural network (ANN) and a hybrid ANN with wavelet analysis (WA-ANN) using four main inputs: precipitation, potential evapotranspiration, Southern Oscillation Index, and Nino 4 index for Brisbane, Australia. For WA-ANN, the four inputs were decomposed into three detail and one approximation levels using Daubechies-4 (db4) orthogonal mother wavelet. The evaluation of prediction performance showed that WA-ANN outperformed ANN model with an increased accuracy by 49.89% based on Root Mean Squared Error values. |
Keywords | Standardized Precipitation Index; drought; artificial neural networks; wavelet analysis; hybrid models |
ANZSRC Field of Research 2020 | 370108. Meteorology |
469999. Other information and computing sciences not elsewhere classified | |
460207. Modelling and simulation | |
370202. Climatology | |
410404. Environmental management | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | School of Agricultural, Computational and Environmental Sciences |
School of Civil Engineering and Surveying | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q3x25/application-of-hybrid-artificial-neural-network-algorithm-for-the-prediction-of-standardized-precipitation-index
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