Standard Precipitation Index Drought Forecasting Using Neural Networks, Wavelet Neural Networks, and Support Vector Regression
Open Access
- 26 September 2012
- journal article
- research article
- Published by Hindawi Limited in Applied Computational Intelligence and Soft Computing
- Vol. 2012, 1-13
- https://doi.org/10.1155/2012/794061
Abstract
Drought forecasts can be an effective tool for mitigating some of the more adverse consequences of drought. Data-driven models are suitable forecasting tools due to their rapid development times, as well as minimal information requirements compared to the information required for physically based models. This study compares the effectiveness of three data-driven models for forecasting drought conditions in the Awash River Basin of Ethiopia. The Standard Precipitation Index (SPI) is forecast and compared using artificial neural networks (ANNs), support vector regression (SVR), and wavelet neural networks (WN). SPI 3 and SPI 12 were the SPI values that were forecasted. These SPI values were forecast over lead times of 1 and 6 months. The performance of all the models was compared using RMSE, MAE, and . The forecast results indicate that the coupled wavelet neural network (WN) models were the best models for forecasting SPI values over multiple lead times in the Awash River Basin in Ethiopia.Keywords
Funding Information
- Natural Sciences and Engineering Research Council of Canada
This publication has 40 references indexed in Scilit:
- A wavelet-support vector machine conjunction model for monthly streamflow forecastingJournal of Hydrology, 2011
- Development of an accurate and reliable hourly flood forecasting model using wavelet–bootstrap–ANN (WBANN) hybrid approachJournal of Hydrology, 2010
- Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watershedsJournal of Hydrology, 2010
- A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time seriesJournal of Hydrology, 2009
- Toward long-lead operational forecasts of drought: An experimental study in the Murray-Darling River BasinJournal of Hydrology, 2008
- Development of a short-term river flood forecasting method for snowmelt driven floods based on wavelet and cross-wavelet analysisJournal of Hydrology, 2008
- Wavelet and neuro-fuzzy conjunction model for precipitation forecastingJournal of Hydrology, 2007
- Drought forecasting using feed-forward recursive neural networkEcological Modelling, 2006
- Multi-time scale stream flow predictions: The support vector machines approachJournal of Hydrology, 2005
- Daily reservoir inflow forecasting using artificial neural networks with stopped training approachJournal of Hydrology, 2000