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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Dayal, K.S., Deo, R.C., Apan, A.A. (2021). Intelligent Data Analytics for Time Series, Trend Analysis and Drought Indices Comparison. In: Deo, R., Samui, P., Kisi, O., Yaseen, Z. (eds) Intelligent Data Analytics for Decision-Support Systems in Hazard Mitigation. Springer Transactions in Civil and Environmental Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-5772-9_8
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