Dalkilic, Huseyin Yildirim; Baran, Ahmet; Yilmaz, Baris; Yildiz, Eyyup

Global warming impacts water resources, altering the hydrological cycle, availability, and quality, posing challenges for sustainable use. This study applied hydrological modelling to evaluate future scenarios in the Fırat Basin using flow data. Gated recurrent unit (GRU) and long short-term memory (LSTM) models were assessed using conventional performance indicators. A Siamese neural network (SNN) with graphical similarity rate (GSR) calculation was introduced to simplify evaluation and reduce parameter complexity. Results showed high model performance, with SNN providing statistically equivalent outcomes to traditional metrics, offering a novel approach to hydrological modelling challenges.

Dalkilic, Huseyin Yildirim; Bayçinar, Metin; Samui, Pijush

The climate covers a series of events that deeply affect human life. Today, one of the most effective of these is climate change. As drought is cited as the most important one of the consequences of climate change, it is important to examine it with various. In this study, the drought status of the study area are located statistically calculated and evaluated 4 drought indices. At the same time, factor analysis and validity-reliability analysis were conducted to test the computability of the indices used in the study as a single index and to determine the reliability of the operations. While using exploratory factor analysis, Kaiser-Meyer-Olkin (KMO) test and Bartlett test for factor analysis; Cronbach's alpha coefficient was used for reliability analysis. In our study, K-means cluster analysis method was performed to determine the cut-off values of indices. According to the cluster analysis, new clusters were created and ANOVA test was conducted to determine the difference between clusters.