Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model
Article
Article Title | Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model |
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ERA Journal ID | 864 |
Article Category | Article |
Authors | Deo, Ravinesh C. (Author), Tiwari, Mukesh K. (Author), Adamowski, Jan F. (Author) and Quilty, John M. (Author) |
Journal Title | Stochastic Environmental Research and Risk Assessment |
Journal Citation | 31 (5), pp. 1211-1240 |
Number of Pages | 30 |
Year | 2017 |
Publisher | Springer |
Place of Publication | Germany |
ISSN | 1436-3240 |
1436-3259 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/s00477-016-1265-z |
Web Address (URL) | http://link.springer.com/article/10.1007/s00477-016-1265-z |
Abstract | A drought forecasting model is a practical tool for drought-risk management. Drought models are used to forecast drought indices (DIs) that quantify drought by its onset, termination, and subsequent properties such as the severity, duration, and peak intensity in order to monitor and evaluate the impacts of future drought. In this study, a wavelet-based drought model using the extreme learning machine (W-ELM) algorithm where the input data are first screened through the wavelet pre-processing technique for better accuracy is developed to forecast the monthly effective DI (EDI). The EDI is an intensive index that considers water accumulation with a weighting function applied to rainfall data with the passage of time in order to analyze the drought-risk. Determined by the autocorrelation function (ACF) and partial ACFs, the lagged EDI signals for the current and past months are used as significant inputs for 1 month lead-time EDI forecasting. For drought model development, 97 years of data for three hydrological stations (Bathurst Agricultural, Wilsons Promontory and Merredin in Australia) are partitioned in approximately 90:5:5 ratios for training, cross-validation and test purposes, respectively. The discrete wavelet transformation (DWT) is applied to the predictor datasets to decompose inputs into their time–frequency components that capture important information on periodicities. DWT sub-series are used to develop new EDI sub-series as inputs for the W-ELM model. The forecasting capability of W-ELM is benchmarked with ELM, artificial neural network (ANN), least squares support vector regression (LSSVR) and their wavelet-equivalent (W-ANN, W-LSSVR) models. Statistical metrics based on agreement between the forecasted and observed EDI, including the coefficient of determination, Willmott’s index, Nash–Sutcliffe coefficient, percentage peak deviation, root-mean-square error, mean absolute error, and model execution time are used to assess the effectiveness of the models. The results demonstrate enhanced forecast skill of the drought models that use wavelet pre-processing of the predictor dataset. Based on statistical measures, W-ELM outperformed traditional ELM, LSSVR, ANN and their wavelet-equivalent counterparts (W-ANN, W-LSSVR). It is found that the W-ELM model is computationally efficient as shown by a faster running time with the majority of forecasting errors in lower frequency bands. The results demonstrate the usefulness of W-ELM over W-ANN and W-LSSVR models and the benefits of wavelet transformation of input data to improve the performance of drought forecasting models. |
Keywords | wavelet; extreme learning machine; drought model; effective drought index; forecasting |
ANZSRC Field of Research 2020 | 490510. Stochastic analysis and modelling |
370108. Meteorology | |
370105. Atmospheric dynamics | |
460207. Modelling and simulation | |
370202. Climatology | |
469999. Other information and computing sciences not elsewhere classified | |
490199. Applied mathematics not elsewhere classified | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | School of Agricultural, Computational and Environmental Sciences |
Anand Agricultural University, India | |
McGill University, Canada | |
Institution of Origin | University of Southern Queensland |
Funding source | Grant ID Research Activation Incentive Scheme (RAIS, July–September 2015 |
Funding source | Grant ID Australian Government Endeavor Executive Fellowship |
https://research.usq.edu.au/item/q37wq/forecasting-effective-drought-index-using-a-wavelet-extreme-learning-machine-w-elm-model
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