Abstract

TOPCU, Emre. Drought Monitoring Using MOWCATL Data Mining Algorithm in Aras Basin, Turkey. Earth Sci. Res. J. [online]. 2022, vol.26, n.2, pp.183-196.  Epub Feb 20, 2023. ISSN 1794-6190.  https://doi.org/10.15446/esrj.v26n2.94786.

Drought is a natural phenomenon that occurs frequently and has some adverse effects on the ecosystem and humanity.

Determination of drought beforehand is vital for optimal management of water resources. Many different methods have been developed to detect drought. Sequential association analysis is used for the data series analysis containing time information and is one of the methods used to determine the drought. A correlation can be established between the values taken by the data at different times when determining association rules with this method. The primary purpose of this study is to determine the sequential association patterns between precipitation and climate oscillation index for Aras Basin. The Aras basin is a region where irrigation and animal husbandry are common. Today, many dams and hydroelectric power plants, together with the increasing population, meet the water and energy needs. A possible drought event in this region will adversely affect the living things in the basin. Therefore, the study focused on this basin. Finding sequential associations between precipitation and climate oscillation index can determine the temporal correlations between these parameters and specifically detect drought. The MOWCATL (Minimal Occurrences with Constraints and Time Lags) algorithm was used to detect sequential associations, and the J-measure was used to evaluate the patterns in the study. Sequential association patterns were determined by applying this method to the precipitation data obtained from 6 meteorology stations in the Aras basin. AO (Arctic Oscillation) Index, MEI (Multivariate ENSO) Index, NAO (North Atlantic Oscillation) Index, Oceanic Niño Index (ONI), PDO (Pacific Decadal Oscillation) Index, PNA (Pacific/North American), and SOI (Southern Oscillation Index), followed by the 1, 3, 6 and 12-month Agricultural Standardized Precipitation Index (a-SPI) were used in sequential association. The study results revealed that the antecedent parameters were ineffective in detecting arid conditions in Ardahan and Dogubeyazit stations, and they were influential on drought conditions, especially in a-SPI-3 and a-SPI-12 month periods at other stations. Although the altitude and geographical features are different, similar climatic patterns have been detected in some stations. As a result, it has been determined that climatic oscillations generally bring about typical situations in terms of drought for the Aras Basin.

Keywords : data mining; drought index; drought; oceanic index.

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