Application of hybrid artificial neural network algorithm for the prediction of Standardized Precipitation Index

Dayal, Kavina S. and Deo, Ravinesh C. and Apan, Armando A. (2016) Application of hybrid artificial neural network algorithm for the prediction of Standardized Precipitation Index. In: 2016 IEEE Region 10 International Conference: Technologies for Smart Nation (TENCON 2016), 22-25 Nov 2016, 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.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Copyright 2016 IEEE.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 14 Feb 2017 00:02
Last Modified: 05 Jun 2017 04:24
Uncontrolled Keywords: Standardized Precipitation Index; drought; artificial neural networks; wavelet analysis; hybrid models
Fields of Research : 04 Earth Sciences > 0401 Atmospheric Sciences > 040107 Meteorology
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080110 Simulation and Modelling
04 Earth Sciences > 0401 Atmospheric Sciences > 040105 Climatology (excl.Climate Change Processes)
05 Environmental Sciences > 0502 Environmental Science and Management > 050206 Environmental Monitoring
Socio-Economic Objective: D Environment > 96 Environment > 9603 Climate and Climate Change > 960303 Climate Change Models
D Environment > 96 Environment > 9602 Atmosphere and Weather > 960202 Atmospheric Processes and Dynamics
Identification Number or DOI: 10.1109/TENCON.2016.7848588
URI: http://eprints.usq.edu.au/id/eprint/30614

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