The modeling of rainfall (P _i) and runoff (Q _i) represents a significant challenge currently facing the field of hydrology. Numerous methodologies can be employed in this regard, spanning the spectrum from conceptual approaches to those that are entirely data-driven and physically based. This paper presents a method for estimating rainfall values at nine observation stations in the Tigris River Basin using four machine learning algorithms: the adaptive neuro-fuzzy inference system (ANFIS), the long short-term memory (LSTM) algorithm, the support vector machine (SVM) algorithm, and the Gaussian process regression (GPR) algorithm. The methodology is founded upon rainfall data obtained from seven meteorological observation stations within the basin. Thiessen polygons were employed to associate rainfall and runoff stations. In the study region, 11 models were constructed using the input parameters P _i, P _i−1, P _i−2, P _i−3, and Q _i−1 to ascertain the rainfall–runoff relationship. The efficacy of the estimation methods was evaluated using the mean absolute error (MAE), root mean square error (RMSE), determination coefficient (R ²), Nash–Sutcliffe efficiency coefficient (NSE), Kling–Gupta efficiency (KGE), and percent bias (PBIAS) criteria. The study’s findings indicated that the LSTM method demonstrated superior performance compared to the other models in all cases. In the LSTM method, the average MAE, RMSE, R ², NSE, and PBIAS criteria for all models (from Model 1 to Model 11) were obtained as 7.14, 9.99, 0.97, 0.96, and 7.38 for training and 6.46, 9.06, 0.96, 0.91, and −2.59 for testing, respectively. The analysis of variance (ANOVA) test results indicated the efficacy of the methods, except for Models 9, 10, and 11, which employed the ANFIS method. Moreover, the exceptional predictive performance of the LSTM model is clearly illustrated in the graphical representation of the results, as demonstrated in violin plots and Taylor diagrams.

Drought, earthquake, flood, and fire are disasters whose effects occur after a more extended period than other disasters. Meteorological drought is called the beginning of drought types. In this study, trend analyses and temporal changes in temperature, precipitation, and drought index values were carried out between 1981 and 2022 at three meteorological observation stations in the Southeastern Anatolia region of Türkiye. Standardized Precipitation Index, Standardized Precipitation Evapotranspiration Index, China-Z Index, and Effective Drought Index methods were used for drought analysis, while Sen's slope, Mann–Kendall, and innovative trend analysis methods were used to detect the trend in precipitation. It was determined that precipitation generally had tended to decrease, and drought increased since 1996. Although every type of dry and wet periods has occurred, normal dry periods were observed more. In the spatial distribution of drought, the inverse distance weighted method gives larger areas with more extreme drought and wet values than the Kriging method. The increase in extreme values in the region indicates that the severity of drought will increase. It has been determined that the region's water resources and agricultural activities are under pressure due to climate change and drought.