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Drought Monitoring Using MOWCATL Data Mining Algorithm in Aras Basin, Turkey
Monitoreo de la sequía a través del algoritmo MOWCATL de minería de datos para la cuenca de Aras, Turquía
DOI:
https://doi.org/10.15446/esrj.v26n2.94786Keywords:
data mining, drought index, drought, oceanic index (en)mineria de datos; índice de sequía; sequía; índice oceánico (es)
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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 Doğubeyazıt 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.
La sequía es un fenómeno natural que ocurre muy frecuentemente y que tiene efectos negativos en los ecosistemas y en la humanidad. La definición de la sequía, de antemano, es especialmente necesarua para la administración óptima de los recursos de agua. Muchos métodos se han desarrollado para detectar la sequía. Uno de estos métodos es el análisis de asociación secuencial que se usa para el análisis de series de datos con información de tiempo. Se puede establecer una correlación entre los valores tomados en diferentes períodos cuando se determinan las reglas asociativas con este método. El propósito principal de este estudio es determinar los patrones de asociación secuencial entre precipitación y el índice de oscilación climática para la cuenca de Aras, en Turquía. Esta cuenca es una región donde la irrigación y la agricultura son comunes. Al día de hoy, muchas presas e hidroeléctricas, junto con el incremento de la población, demandan estos recursos hidrológicos. Un evento de sequía en la región afectaría a estos seres que dependen de la cuenca. Por esta razón, el estudio se enfoca en la cuenca de Aras. Encontrar las asociaciones secuenciales entre precipitación y el índice de oscilación climática puede determinar las correlaciones temporales entre estos parámetros y, específicamente, detectar la sequía. En este trabajo se usó el algoritmo MOWCATL (ocurrencias mínimas con restricciones y retrasos, literal del inglés Minimal Occurrences with Constraints and Time Lags) para detectar las asociaciones secuenciales y la medición J se usó para evaluar estos patrones. Los patrones de asociación secuencial se determinaron al aplicar este método a la información de precipitación obtenida de seis estaciones meteorológicas en la cuenca de Basin. Los índices de Oscilación Ártica, ENSO Multivariado, Oscilación del Atlántico Norte, Oceánico del Niño, Oscilación Decadal del Pacífico, Pacífico/Norte América, y de Oscilación del Sur, seguidos por el Índice de Precipitación Agrícola Estandarizado (a-SPI) en los meses 1, 3, 6 y 12 se utlizaron en la asociación secuencial. Los resultados del estudio revelan que estos parámetros no son efectivos para detectar las condiciones áridas en las estaciones de Ardahan y Doğubeyazıt pero si son efectivos en las condiciones de sequía, especialmente en los períodos a-SPI-3 y a-SPI-12 en otras estaciones. A pesar de que las condiciones de altitud y geográficas son diferetes, patrones climáticos se han detectado en algunas estaciones. Como resultado se determinó que las oscilaciones climáticas generalemente provocan situaciones normales de sequía en la cuenca de Aras.
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1. Milad Sabamehr, Cansu Çiğdem Ekin. (2023). AI-Driven Drought Management System: A Turkish Case Study. 2023 4th International Informatics and Software Engineering Conference (IISEC). , p.1. https://doi.org/10.1109/IISEC59749.2023.10391008.
2. Emre Topçu, Fatih Karaçor. (2023). A comparative investigation on the applicability of the actual precipitation index (API) with the standardized precipitation index (SPI): the case study of Aras Basin, Turkey. Theoretical and Applied Climatology, 154(1-2), p.29. https://doi.org/10.1007/s00704-023-04499-w.
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