Improving SPI-derived drought forecasts incorporating synoptic-scale climate indices in multi-phase multivariate empirical mode decomposition model hybridized with simulated annealing and kernel ridge regression algorithms

Ali, Mumtaz and Deo, Ravinesh C. and Maraseni, Tek and Downs, Nathan J. (2019) Improving SPI-derived drought forecasts incorporating synoptic-scale climate indices in multi-phase multivariate empirical mode decomposition model hybridized with simulated annealing and kernel ridge regression algorithms. Journal of Hydrology, 576. pp. 164-184. ISSN 0022-1694

Abstract

New and improved drought models based on the World Meteorological Organization approved Standardized Precipitation Index, principally at multiple timescale horizons, are providing significant benefits to the hydrological community, by its widespread acceptance in the sub-field of water resources management, sustainable water use and precision agriculture. In this research paper, the existing challenges faced by a drought forecasting model trained at multiple time-scales are resolved where a new multi-phase, multivariate empirical mode decomposition model integrated with simulated annealing and Kernel ridge regression algorithms (i.e., MEMD-SA-KRR) is designed to attain significantly accurate drought forecasts for 3 agricultural sites (i.e., Faisalabad, Islamabad and Jhelum, located in Pakistan). Utilizing the multi-scalar Standardized Precipitation Index (SPI) time series as a target variable for characterization of drought, twelve multivariate datasets (derived from statistically significant lagged combinations of precipitation, temperature & humidity), that are enriched with eight synoptic-scale climate mode indices and periodicity, are utilized in designing a new drought model. The study constructs a hybrid MEMD-SA-KRR model, where firstly, the data are partitioned into their respective training and testing subsets after creating historically lagged SPI at timescale (t – 1). Secondly, the MEMD algorithm is conditioned to demarcate multivariate climate indices from their training and testing sets, separately, into their decomposed intrinsic mode functions (IMFs) and residues. Thirdly, the SA method is employed to decide the most suitable IMFs. Finally, the KRR algorithm is applied to the selected IMFs to forecast multi-scaler SPI, at 1-, 3-, 6- and 12-monthly forecast horizon. The results are benchmarked with Random Forest, integrated with MEMD and SA to develop the MEMD-SA-RF equivalent model. The multi-phase MEMD-SA-KRR model is tested geographically in Pakistan, revealing that the MEMD-SA-KRR hybrid model generates reliable performance in forecasting multi-scaler SPI series, relative to the comparative models based on error analysis metrics. The hybrid drought model incorporating the most pertinent synoptic-scale climate drivers, as the model inputs has significant implications for hydrological applications and water resources management including its potential use in drought policy and drought recovery plans.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to published version, in accordance with the copyright policy of the publisher.
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences (1 July 2013 - 5 Sept 2019)
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences (1 July 2013 - 5 Sept 2019)
Date Deposited: 25 Jun 2019 07:30
Last Modified: 27 Jun 2019 05:25
Uncontrolled Keywords: hybrid drought; forecast model; multivariate empirical mode decomposition; simulated annealing; Kernel ridge regression
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080105 Expert Systems
05 Environmental Sciences > 0502 Environmental Science and Management > 050205 Environmental Management
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970105 Expanding Knowledge in the Environmental Sciences
URI: http://eprints.usq.edu.au/id/eprint/36622

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