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- ArticleAçık Erişim2026Hybrid VMD-EMD Enhanced Machine Learning Models for Daily Reference Evapotranspiration Forecasting
Acar, Reşat; Katipoğlu, Okan Mert; Çırağ, Burak
Getting accurate daily estimates of reference evapotranspiration (ET₀) is essential for hydrological modeling, irrigation planning, and climate research. This study evaluated the performance of four machine learning models: Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Deep Neural Network (DNN), and Light Gradient Boosting Machine (LGBM) for daily ET₀ prediction. Feature selection was implemented using Partial Autocorrelation (PACF) and Autocorrelation (ACF) analysis on the basis of an 8-day lag structure, while the CatBoost feature importance framework was implemented to identify the most important inputs. Moreover, the methodologies of decomposition-based preprocessing, Empirical Mode Decomposition (EMD) and Variational Mode Decomposition (VMD) were implemented on the lagged inputs to build the hybrid models augmented with intrinsic constituents. These augmented hybrid models were created to improve the data representation as well as predictive capability. Model performance was evaluated using key statistical metrics, including RMSE, MAE, NSE, KGE, R2, and bias-related indicators. CatBoost produced the lowest RMSE (1.57 mm/day) and MAE (1.13 mm/day), and the highest NSE (0.67), KGE (0.78), and R2 (0.67), along with the lowest AIC score (627.15), indicating strong and reliable predictive performance. Furthermore, the VMD based CatBoost model yielded even greater improvements, highlighting the added value of decomposition enhanced learning. On the contrary, DNN showed the weakest results, with the highest RMSE (2.39 mm/day) and lowest NSE (0.23). These findings suggest that CatBoost and its hybrid variants are highly effective and reliable tools for daily ET₀ prediction, particularly in data limited environments and mountainous regions.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2025Reconstructing Missing Streamflow Data in Mountainous, Snow-Dominated Regions Using Optimized Hybrid Machine Learning
Çırağ, Burak; Acar, Reşat; Katipoğlu, Okan Mert; Yağanoğlu, Mete; Şengül, Selim
Accurate streamflow estimation is pivotal for hydrological research and water resource management, particularly in mountainous, snow-dominated regions where missing data can significantly impair watershed modeling and water budget evaluations. This study presents an advanced hybrid machine learning framework to impute missing streamflow records at a strategically located observation station within a snow-abundant sub-basin of the Euphrates-Karasu Basin in Erzurum Province, Eastern Turkey. Leveraging daily meteorological and hydrological data collected over four decades (1983–2023), we developed and rigorously evaluated several predictive models using comprehensive performance metrics and rank-based analysis. In addition to conventional approaches-including a single Artificial Neural Network (ANN), CatBoost, and Deep Neural Network (DNN)-the study also compares innovative hybrid models in which ANN parameters were optimized using the Whale Optimization Algorithm (WOA) and the Hydrologic Cycle Algorithm (HCA). Notably, the hybrid models demonstrated superior predictive accuracy over traditional methods, thereby underscoring their potential to bridge data gaps in challenging terrains. The results affirm the efficacy of the proposed framework and its promise as a robust methodological foundation for future investigations in watershed modeling, drought assessment, and water budget analysis.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2022Space-time variations of hydrological drought severities and trends in the semi-arid Euphrates Basin, Turkey
Katipoğlu, Okan Mert; Acar, Reşat
The main purpose of this study is to map the spatio-temporal variation of hydrological drought severities in the Euphrates Basin by using Kriging, Radial Based Function (RBF), Inverse Distance Weighting (IDW), Local Polynomial Interpolation (LPI), and Global Polynomial Interpolation (GPI) methods and to determine the distribution of hydrological drought trends. For this purpose, 18 streamflow observation stations and grid-based ERA5 reanalysis data were employed in and around the Euphrates Basin. The standardized runoff index (SRI) was calculated on monthly, 3-month, and 12-month time scales to determine hydrological droughts. Hydrological drought trends were determined by Mann Kendall (MK) and Modified Mann Kendall (MMK) tests. According to SRI, spatial-temporal distributions of droughts were mapped by Kriging, RBF, IDW, GPI, and LPI methods in the Geographical Information Systems environment. The LPI and Kriging method was selected as the most effective methods to predict hydrological droughts based on the cross-validation error. As a result of the trend analysis, the predominance of decreasing SRI trends in May (61.1%), June (72.2%), July (55.6%), August (61.1%) indicates that the basin is at risk of hydrological drought, especially in summer. According to the maps of spatial droughts, the widespread distribution of droughts in the basin indicated that the basin was greatly affected by hydrological droughts. For this reason, it has been emphasized that transboundary water effective management between riparian countries, planning of water structures, and effective implementation of the drought management plan is of great importance.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2021Spatio-temporal analysis of meteorological and hydrological droughts in the Euphrates Basin, Turkey
Katipoglu, Okan Mert; Acar, Reşat; Şenocak, Serkan
In this study, the aim was to measure changes in the spatio-temporal distribution of a potential drought hazard area and determine the risk status of various meteorological and hydrological droughts by using the kriging, radial basis function (RBF), and inverse distance weighting (IDW) interpolation methods. With that goal, in monthly, three-month, and 12-month time periods drought indices were calculated. Spatio-temporal distributions of the droughts were determined with each drought index for the years in which the most severe droughts were experienced. According to the results, the basin is under risk of meteorological drought due to the occurrence of severe and extreme droughts in most of the area, and especially in the north, during the monthly and three-month time periods. During the 12-month period, it was found that most of the basin is under risk of hydrological drought due to the occurrence of severe and extreme droughts, especially in the southern parts. The most effective interpolation method for the prediction of meteorological and hydrological droughts was determined as kriging according to the results of the cross-validation test. It was concluded that a drought management plan should be made, and early warnings and precautions should be applied in the study area.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2020Comparison of meteorological indices for drought monitoring and evaluating: A case study from euphrates basin, Turkey
Katipoglu, Okan Mert; Acar, Reşat; Şengül, Selim
Drought incidents occur due to the fact that precipitation values are below average for many years. Drought causes serious effects in many sectors, such as agriculture, economy, health, and energy. Therefore, the determination of drought and water scarcity, monitoring, management, and planning of drought and taking early measures are important issues. In order to solve these issues, the advantages and disadvantages of five different meteorological drought indices were compared, and the most effective drought index was determined for monitoring drought. Accordingly, in the monthly, 3-month, and 12-month time period, covering the years between 1966 and 2017 (52 years), Standardized Precipitation Index (SPI), Statistical Z-Score Index (ZSI), Rainfall Anomaly Index (RAI), Standardized Precipitation Evapotranspiration Index (SPEI), and Reconnaissance Drought Index (RDI) were used. It was concluded that precipitation-based SPI and ZSI are similar patterns and precipitation, and temperature-based SPEI and RDI are similar patterns. Also, it has been determined that RAI is more effective than other indices in determining the periods of extreme drought or wet. Furthermore, SPEI and RDI have been found to be superior to other indices as they take into account the water consumption and climate effects caused by evapotranspiration.
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