Sürdürülebilir Kalkınma Amaçları
Yayınlar
- ArticleAçık Erişim2026Physically constrained air–sea heat flux driven modeling of sea surface temperature using atmospheric and oceanographic variables with explainable AI
Katipoğlu, Okan Mert; Bazrafshan, Ommolbanin; Mohammadi, Mojtaba; Alsumaiei, Abdullah A.
This study aims to develop a robust multivariate regression framework for accurately predicting monthly Sea Surface Temperature (SST) using a core set of ten primary atmospheric and oceanographic variables, including relative humidity, specific humidity, temperature, mean square slope of waves, mean wave direction, 2-meter air temperature, top net solar radiation, total precipitation, mean evaporation rate, and potential evaporation. The originality of this research lies in the simultaneous integration of meteorological and oceanographic parameters into multiple machine learning architectures, coupled with a detailed interpretability analysis using SHAP values, which provide a comprehensive understanding of the influence of variables on SST prediction. The dataset was split into 60 % for training, 20 % for testing, and 20 % for validation. Four machine learning models: Random Forest, CatBoost, one-dimensional Convolutional Neural Network, and Support Vector Machine were optimized using RandomizedSearchCV, with MinMaxScaler applied to ensure consistent feature scaling across models. Model performance was evaluated using Mean Squared Error, Root Mean Squared Error, Mean Absolute Error, Coefficient of Determination, and Explained Variance metrics. Meanwhile, SHAP analysis identified the most influential features as 2-meter air temperature, mean evaporation rate, mean square slope of waves, air temperature, and specific humidity. Results revealed that the Support Vector Machine achieved the highest accuracy overall, followed by CatBoost, confirming that combining meteorological and oceanographic predictors significantly enhances SST forecasting performance. The physical consistency analyses, supported by the bulk air–sea heat flux formulations for sensible and latent heat fluxes, confirm that the machine-learning model’s most influential predictors: near-surface air temperature, specific humidity, and evaporation-related variables are fully consistent with the fundamental thermodynamic controls governing SST. This research not only advances SST prediction accuracy but also provides a transparent interpretability framework, making it a valuable reference for climate modeling, marine resource management, and environmental monitoring applications.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2026Assessment of future hydrometeorological variability in the Konya Basin, Türkiye using bias-corrected CMIP6 projections
Katipoğlu, Okan Mert; Kartal, Veysi; Akiner, Muhammed Ernur; Bazrafshan, Ommolbanin; Spor, Pinar; Çelik, Mehmet Ali
This study presents bias-corrected projections of soil moisture, precipitation, Temperature and evapotranspiration in the Konya Basin, derived using CMIP6 data under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios. Bias correction methods, including Delta, Quantile Mapping, and Empirical Quantile Mapping, were applied to the raw model outputs. Historical data from CMIP6 (1950–2014) and future projections (2015–2100) were corrected using ERA5 reanalysis data (1940–2024) to improve accuracy. Nine global climate models ACCESS-CM2, CESM2, CMCC-ESM2, EC-Earth3-CC, MIROC6, GFDL-ESM4, MPI-ESM1-2-LR, MRI-ESM2-0, and NorESM2-LM were evaluated, and ACCESS-CM2 provided the most reliable results for all three SSP scenarios. The data were divided into four time periods: the reference period (2015–2024), near future (2025–2049), mid-future (2050–2074), and far future (2075–2100). Relative errors were calculated as (Observed - Predicted)/Observed × 100, with temperature trends incorporated for comparative assessments. For spatial analysis, Universal Co-Kriging was employed to generate predictive maps for each future period, using elevation as a secondary variable. Therefore, the Universal Co-Kriging parameters determined through a trial-and-error approach namely, the use of a Gaussian kernel, standard neighborhood structure with 5 maximum and 2 minimum neighbors, and a 45-degree sectoral division in four directions proved to be the most effective combination for accurately mapping the spatial distribution of soil moisture, precipitation, temperature, and evapotranspiration in the Konya Basin. The study provides valuable insights into the future variability of hydrometeorological variables in the Konya Basin, offering critical information for water resource management and climate adaptation strategies.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2025Harnessing artificial ıntelligence for streamflow predictions under climate change scenarios in arid region
Abbaszadeh, Maryam; Bazrafshan, Ommolbanin; Katipoğlu, Okan Mert; Jamshid, Sajad
Understanding the streamflow fluctuation of the Minab River basin in the south of Iran is of great importance due to the presence of the Esteghlal Reservoir Dam at its outlet and agricultural activities that produce food for the residents and support neighboring cities. Water resources of this basin have been affected by climate change and it is expected to observe more effects in the future. Therefore, this study was conducted to evaluate the impact of climate change on river flow levels in the Minab River basin, comparing the future period (2020–2050) with the baseline period (1985–2014). Using the CanESM5 model, future climate scenarios SSP1-2.6, SSP3-7.0, and SSP5-8.5 were simulated to project changes in precipitation and maximum, and minimum temperatures. For streamflow simulation, five machine learning models including Generalized Linear Regression (GLR), K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Decision Trees (DT), and Random Forest (RF) were employed, with KNN showing superior performance. The findings indicate max and min temperatures could rise by 4.9℃ and 5.2℃ respectively under the SSP3-7.0 scenario until 2050. Mean annual streamflow will experience an increase of 33% and the highest values will probably be observed in fall, which can be the trigger for more floods and decreasing streamflow during warmer seasons can cause many challenges in the availability of water resources. These insights are crucial for water resource management by local authorities and organizations.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2023Evaluation of hydro-meteorological drought indices for characterizing historical droughts in the Mediterranean climate of Algeria
Achite, Mohammed; Bazrafshan, Ommolbanin; Katipoğlu, Okan Mert; Azhdari, Zahra
Determining drought indices and characteristics in Algeria is crucial because droughts significantly impact water resources and agricultural production. Additionally, identifying the most suitable drought indicator for the region facilitates effective monitoring of droughts. The study’s main objective is to compare hydro-meteorological droughts, determine their distribution, and assess drought risk. Various drought indices, which are continuous functions of rainfall and other hydro-meteorological variables, are typically used for this purpose. This study calculates seven indices' effectiveness for drought monitoring and assessment in Algeria's Wadi Ouhrane basin. For this purpose, the temporal variation of drought indicators, distribution graphs, weighted Cohen's kappa (Kw), and correlation coefficient values was compared. Among the indices used in the study, four indices, namely SPI, CZI, ZSI, and MCZI, showed high similarity in their behavior. As the time scale increases from 1 to 24 months, the correlation coefficient exceeds 0.97, and Kw becomes greater than 0.57. Furthermore, there is a weak correlation (R < 0.4) between the meteorological and hydrological-based SRI indicators, and the highest correlation was found between the RDI and SRI indices. Therefore, these indices indicate that the precipitation and ET (temperature) ratio is more suitable for hydrological drought studies.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2023Modeling of meteorological, agricultural, and hydrological droughts in semi-arid environments with various machine learning and discrete wavelet transform
Achite, Mohammed; Katipoglu, Okan Mert; Şenocak, Serkan; Elshaboury, Nehal; Bazrafshan, Ommolbanin; Dalkılıç, Hüseyin Yıldırım
Recent meteorological, hydrological, and agricultural droughts in the Mediterranean regions have raised concerns about the impact of climate change. In this study, the meteorological, agricultural, and hydrological droughts were modeled in the Wadi Ouahrane Basin using various machine learning (ML) models and standardized indices of rainfall, evapotranspiration, and runoff (SPI, SPEI, and SRI) at different times scales (1, 3, 6, 9, 12, and 24 months). The applied ML models were the linear support vector machine (SVM), quadratic SVM, cubic SVM, fine gaussian SVM, medium gaussian SVM, coarse Gaussian SVM, rational quadratic Gaussian process regression (GPR), squared exponential GPR, Matern 5/2 GPR, exponential GPR, bagged tree, and boosted tree. Moreover, the hybrid models acquired by combining these ML models with wavelet transform were evaluated. The performance of the models was analyzed using statistical criteria such as root mean square error, determination coefficient, and mean absolute error. As a result, wavelet-GPR models showed the most promising results in estimating SPI, SPEI, and SRI values. The values for SPI (R 2 of train: 0.393; and test: 0.351), SPEI (R 2 of train: 0.809; test: 0.746), and SRI (R 2 of train: 0.999; test: 0.808) indicate monthly time scale. Additionally, for the time periods of 3, 6, 9, 12, and 24 months, the predictions for SPI, SPEI, and SRI generally obtain R 2 of train 0.99 and test 0.95 values. Moreover, it was determined that wavelet-based ML models, established with inputs divided into three subcomponents with Daubechies mother wavelet, showed superior results than standalone ML models. The study results could guide decision-makers and planners in developing drought risk management and mitigation strategies.
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