Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Advanced Hybrid Machine Learning for Precise Short-Term Drought Prediction: A Comparative Study of SPI and SPEI Indices in Iran's Arid and Semi-Arid Regions(Birkhauser, 2025) Talebi, H.; Citakoglu, H.; Samadianfard, S.; Erol, A.Drought has been viewed as a climatic event of significant importance that hampers agricultural productivity, efficient management of water resources, and socio-economic development, especially in arid, semi-arid, and arid-semiarid regions. Even though improved approaches to modeling dry spells have been reported, there remains a substantial disparity in the forecasting ability of the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) for different climatic conditions. In response to the observed disparity, the current study utilized the Tuned Q-factor Wavelet Transform (TQWT), Variational Mode Decomposition, Empirical Mode Decomposition, and Empirical Wavelet Transform (EWT), together with Gaussian Process Regression (GPR), Support Vector Machines, and Adaptive Neuro-Fuzzy Inference System (ANFIS) models. The dataset included precipitation and temperature data collected from four synoptic instrument-equipped meteorological stations from 1990 to 2022—Tabriz and Shiraz corresponding to semi-arid, and Kerman and Yazd corresponding to arid regions—and included SPI and SPEI index predictions for temporal periods of 1, 3, and 6 months. Through the use of autocorrelation diagnostics, it was possible to identify the optimal input lags (t-1, t-2, and t-3) specifically allocated for the model development process, derived from 75% of the available dataset. For the case of the 1-month temporal period, the models using the TQWT revealed the best forecasting effectiveness; most importantly, the TQWT-ANFIS model recorded the highest accuracy at the Tabriz station, while the TQWT-GPR model showed the highest accuracy values at Shiraz, Kerman, and Yazd (R2≈0.996–0.997; RMSE≈0.05–0.07). For the 3- and 6-month temporal evaluations, the EWT-ANFIS model recorded the best performance among all allocated stations, marked by the lowest error metrics (RMSE≈0.01–0.03) together with nearly perfect goodness-of-fit values (R2 and NSE≈0.999). The Shiraz and Kerman observation stations showed the best performance indices, reaching a Kling-Gupta Efficiency (KGE) of 0.99. By comparison, the report from Tabriz indicated a poorer KGE of about 0.93, while the Yazd station showed volatility in the 6-month Standardized Precipitation Index, reaching a KGE of about 0.60, suggesting a rising aridity trend. Overall, results demonstrate that while TQWT-based models dominate short-term drought prediction, EWT-ANFIS is the most robust for medium- and long-term forecasts. These findings emphasize the potential of hybrid decomposition–machine learning frameworks to improve drought monitoring and strengthen water resource management strategies in water-scarce regions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.Editorial Preface(Birkhauser, 2019) Inam, I.; Büyükaşık, E.Erratum Correction To: Assessing the Spatial and Temporal Characteristics of Meteorological Drought in Afghanistan (Pure and Applied Geophysics, (2024), 10.1007/S00024-024-03578-x)(Birkhauser, 2025) Tayfur, G.; Hayat, E.; Safari, M.J.S.Correct affiliations of Mir Jafar Sadegh Safari should only include the following: Department of Geography and Environmental Studies, Toronto Metropolitan University, Toronto, Ontario, Canada Department of Civil Engineering, Yaşar University, Izmir, Turkey Department of Geography and Environmental Studies, Toronto Metropolitan University, Toronto, Ontario, Canada Department of Civil Engineering, Yaşar University, Izmir, Turkey The original article has been corrected. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.Article Citation - WoS: 3Citation - Scopus: 2Assessing the Spatial and Temporal Characteristics of Meteorological Drought in Afghanistan(Birkhauser, 2025) Tayfur, G.; Hayat, E.; Safari, M.J.S.Afghanistan is suffering from periodic events of drought, which has exacerbated in recent years due to extreme climate events in the region. Having an arid to semi-arid climate, the country faces significant challenges of water resources management, especially for irrigation as reliance on agriculture is cumbersome. This study is undertaken to characterize historical meteorological drought in Afghanistan to provide an insight on where and when meteorological drought events happened in different River Basins (RBs). The study mainly employs the gamma-Standardized Precipitation Index (gamma-SPI) to analyze historical meteorological droughts across Afghanistan from 1979 to 2019. Monthly precipitation data is obtained from the Ministry of Energy and Water (MEW) of Afghanistan, which is a combination of observed data from ground stations and gap-filled data by the MEW for the study period. Gridded gamma-SPI values are interpolated and mapped to visualize patterns of spatial drought across the entire country. The results indicate that countrywide extreme drought events occurred in 1999, 2000, 2001, 2010, 2016, 2017, and 2019, particularly affecting southern, western, and southwestern regions. Decreasing rainfall occurred in all five RBs, with the most considerable decline observed in the 1999–2008 period. The study reveals the increasing frequency and severity of meteorological droughts in Afghanistan. It also emphasizes on the vulnerability of agriculture and water sectors due to the drought events. The findings of the study suggest the need for better drought monitoring, preparedness, awareness, and adaptation of strategies to ensure water security and agricultural sustainability in the face of climate change. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.Article Spatial Graphoids(Birkhauser, 2024) Gügümcü,N.; Kauffman,L.H.; Pongtanapaisan,P.To study knotted graphs with open ends arising in proteins, we introduce virtual graphoids, which are virtual spatial graph diagrams with two distinguished degree-one vertices modulo graph Reidemeister moves applied away from the distinguished vertices. Generalizing previously known results, we give topological interpretations of graphoids. By analyzing the Yamada polynomial, we provide bounds for the crossing numbers. As an application, we can produce nontrivial graphoids by verifying that they satisfy adequacy conditions in the same spirit as Lickorish and Thistlethwaite’s notion of adequate links. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.Article Spatial Graphoids(Birkhauser, 2023) Gugumcu, Neslihan; Kauffman, Louis H.; Pongtanapaisan, PuttipongTo study knotted graphs with open ends arising in proteins, we introduce virtual graphoids, which are virtual spatial graph diagrams with two distinguished degree-one vertices modulo graph Reidemeister moves applied away from the distinguished vertices. Generalizing previously known results, we give topological interpretations of graphoids. By analyzing the Yamada polynomial, we provide bounds for the crossing numbers. As an application, we can produce nontrivial graphoids by verifying that they satisfy adequacy conditions in the same spirit as Lickorish and Thistlethwaite’s notion of adequate links. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.