Author(s): Sefa Nur Yesilyurt; Eyyup Yildiz; Gulay Onusluel Gul; Huseyin Yildirim Dalkilic
Linked Author(s):
Keywords: No Keywords
Abstract: Data availability is vital for hydrology and water resources engineering studies. The measurement of hydrological variables is subject to systematic and random errors. Therefore, hydrologists often deal with the problem of missing data. Due to the pivotal role of precipitation data as a fundamental input in hydrological research, ensuring the availability of thorough and precise precipitation datasets takes on utmost importance. To this end, this paper proposes to integrate the KNN (K-Nearest Neighbors Classification) algorithm with the K-Means algorithm. This innovative approach effectively augments precipitation data for the Konya Closed Basin using a method that has not been explored so far in this geographical context. The model firstly clusters stations based on location, altitude, annual mean, maximum and minimum temperature values using the K-Means method. It then applies the KNN method for the clustered data sets and completes the missing data. The resulting model shows a performance reaching a maximum value of 0.775. The findings of this study not only confirmed the utility of the proposed model in imputing missing data, but also demonstrated its potential as a viable alternative to traditional imputation methods in hydrological research. The comprehensive data obtained through this innovative model was used to assess drought status of the basin using the Standard Precipitation Index (SPI) based on severity, duration and magnitude of droughts. For the 1-month SPI, the most severe drought occurred in the period 1988-1998; for the 3-month SPI in the period 2010-2019; for the 6-month and 12-month SPI in the period 1966-1976.
Year: 2024