Intelligent data analytics for time series, trend analysis and drought indices comparison

Dayal, Kavina S. and Deo, Ravinesh C. ORCID: https://orcid.org/0000-0002-2290-6749 and Apan, Armando A. ORCID: https://orcid.org/0000-0002-5412-8881 (2021) Intelligent data analytics for time series, trend analysis and drought indices comparison. In: Intelligent data analytics for decision-support systems in hazard mitigation: theory and practice of hazard mitigation. Springer Transactions in Civil and Environmental Engineering. Springer, Singapore, pp. 151-169. ISBN 978-981-15-5771-2


Abstract

This chapter develops intelligent data analytics approaches to compare the frequently used drought-monitoring indices and applies the change-point analysis technique to detect subtle changes in the drought index trends for natural hazard and disaster risk mitigation. The Standardised Precipitation-Evapotranspiration Index (SPEI), used in this chapter, is able to identify extreme drought events better than the Standardised Precipitation Index (SPI). SPEI highly correlates with Precipitation-based Drought Indices (DIs), especially with SPI and Rainfall Decile-based Drought Index (RDDI) but can additionally provide complementary information about hydrological effects of drought. Illustrated by the wavelet analysis, the SPEI concurs with all major drought events largely, significant at 95% confidence interval, compared to SPI, RDDI and Rainfall Anomaly Index (RAI). The change-point analysis is able to detect changes in the SPEI trend with associated confidence levels and confidence intervals. The study found the location R4 (in arid/semi-arid region) to have undergone 26 changes in SPEI trend compared to R1, R2 and R3 with 0, 9 and 6, respectively. The location of study matters where inland from the coastline experiences more variability in the environmental parameters that define the SPEI. The methods proposed this chapter can be useful for disaster risk mitigation, particularly, quantifying drought events for decision-making processes.


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Item Type: Book Chapter (Commonwealth Reporting Category B)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to Published chapter in accordance with the copyright policy of the publisher.
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 -)
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Civil Engineering and Surveying (1 Jul 2013 -)
Date Deposited: 02 Sep 2020 01:50
Last Modified: 13 Sep 2020 22:18
Uncontrolled Keywords: intelligent data analytics; drought monitoring indices; drought index trends; Standardised Precipitation-Evapotranspiration Index
Fields of Research (2008): 05 Environmental Sciences > 0502 Environmental Science and Management > 050204 Environmental Impact Assessment
Socio-Economic Objectives (2008): E Expanding Knowledge > 97 Expanding Knowledge > 970105 Expanding Knowledge in the Environmental Sciences
Identification Number or DOI: https://doi.org/10.1007/978-981-15-5772-9_8
URI: http://eprints.usq.edu.au/id/eprint/39215

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