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Ensemble prediction of regional droughts using climate inputs and the SVM-copula approach

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Title Ensemble prediction of regional droughts using climate inputs and the SVM-copula approach
 
Creator GANGULI, P
REDDY, MJ
 
Subject drought prediction
copulas
support vector regression
climate teleconnection
ensembles
SUPPORT VECTOR MACHINES
NEURAL-NETWORKS
SOIL-MOISTURE
INDIAN-OCEAN
RAINFALL
MODEL
INDEX
MONSOON
DIPOLE
IMPACT
 
Description In this study, the climate teleconnections with meteorological droughts are analysed and used to develop ensemble drought prediction models using a support vector machine (SVM)-copula approach over Western Rajasthan (India). The meteorological droughts are identified using the Standardized Precipitation Index (SPI). In the analysis of large-scale climate forcing represented by climate indices such as El Nino Southern Oscillation, Indian Ocean Dipole Mode and Atlantic Multidecadal Oscillation on regional droughts, it is found that regional droughts exhibits interannual as well as interdecadal variability. On the basis of potential teleconnections between regional droughts and climate indices, SPI-based drought forecasting models are developed with up to 3months' lead time. As traditional statistical forecast models are unable to capture nonlinearity and nonstationarity associated with drought forecasts, a machine learning technique, namely, support vector regression (SVR), is adopted to forecast the drought index, and the copula method is used to model the joint distribution of observed and predicted drought index. The copula-based conditional distribution of an observed drought index conditioned on predicted drought index is utilized to simulate ensembles of drought forecasts. Two variants of drought forecast models are developed, namely a single model for all the periods in a year and separate models for each of the four seasons in a year. The performance of developed models is validated for predicting drought time series for 10 years' data. Improvement in ensemble prediction of drought indices is observed for combined seasonal model over the single model without seasonal partitions. The results show that the proposed SVM-copula approach improves the drought prediction capability and provides estimation of uncertainty associated with drought predictions. Copyright (C) 2013 John Wiley & Sons, Ltd.
 
Publisher WILEY-BLACKWELL
 
Date 2014-12-28T11:23:55Z
2014-12-28T11:23:55Z
2014
 
Type Article
 
Identifier HYDROLOGICAL PROCESSES, 28(19)4989-5009
0885-6087
1099-1085
http://dx.doi.org/10.1002/hyp.9966
http://dspace.library.iitb.ac.in/jspui/handle/100/16350
 
Language English