Yazarlar
- Katipoğlu, Okan Mert(57)
- Akın, Fatih(15)
- Manga, Muge(12)
- Achite, Mohammed(11)
- Balli, Esra(11)
- Kartal, Veysi(10)
- Yilmaz, Harun(10)
- Destek, Mehmet Akif(9)
- Elshaboury, Nehal(8)
- Demirci, Oğuz Kürşat(7)
- Ganjehsarabi, Hadi(7)
- Yilmaz, Ilker(7)
- Cam, Omer(6)
- Cengiz, Orhan(6)
- Dinçer, Selin(6)
- Elbeltagi, Ahmed(6)
- Manga, Müge(6)
- Seyhan, Aslıhan Kurnuç(5)
- Sigeze, Ciler(5)
- Çatık, Abdurrahman Nazif(5)
- Abdallah, Mohammed(4)
- Acar, Reşat(4)
- Ballı, Esra(4)
- Bazrafshan, Ommolbanin(4)
- Berhail, Sabri(4)
- Dogan, Ahmet(4)
- Kilic, Ugur(4)
- Kuziboev, Bekhzod(4)
- Makhmudov, Samariddin(4)
- Mohammadi, Babak(4)
- Pande, Chaitanya Baliram(4)
- Pandey, Kusum(4)
- Sari, İdris(4)
- Sarıgöl, Metin(4)
- Uzun, Sinem(4)
- Çatik, Abdurrahman Nazif(4)
- Acar, Resat(3)
- Bakirci, Kadir(3)
- Dalkilic, Huseyin Yildirim(3)
- Ekici, Selcuk(3)
Yayınlar
- ArticleAçık Erişim2025A coupled extreme gradient boosting-MPA approach for estimating daily reference evapotranspiration
Achite, Mohammed; Nasiri, Hamid; Katipoğlu, Okan Mert; Abdallah, Mohammed; Moazenzadeh, Roozbeh; Mohammadi, Babak
Reference evapotranspiration (ET0) modeling is pivotal for irrigation scheduling and water resources planning. This study presents a hybrid approach integrating Extreme Gradient Boosting (XGB) with Marine Predators Algorithm (MPA) for daily ET0 estimation in northern Algeria. The proposed XGB-MPA model was evaluated against traditional empirical models and assessed using statistical methods. Shapley Additive Explanations (SHAP) was employed to enhance model interpretability. Various combinations of meteorological variables were tested as inputs, including air temperature, relative humidity, sunshine hours, wind speed, and extraterrestrial solar radiation. The XGB-MPA hybrid model achieved superior prediction accuracy during testing (R2 = 0.9958, RMSE = 0.1713 mm/day) compared to traditional empirical models and the standard XGB model. The study demonstrated that ET0 prediction accuracy increased with the number of meteorological inputs used. Our findings highlight the XGB-MPA hybrid model's potential for accurate ET0 estimation in northern Algeria, which can be used for water resource management and irrigation planning.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2025Advanced Soft Computing Techniques for Monthly Streamflow Prediction in Seasonal Rivers
Achite, Mohammed; Katipoğlu, Okan Mert; Kartal, Veysi; Sarıgöl, Metin; Jehanzaib, Muhammad; Gül, Enes
The rising incidence of droughts in specific global regions in recent years, primarily attributed to global warming, has markedly increased the demand for reliable and accurate streamflow estimation. Streamflow estimation is essential for the effective management and utilization of water resources, as well as for the design of hydraulic infrastructure. Furthermore, research on streamflow estimation has gained heightened importance because water is essential not only for the survival of all living organisms but also for determining the quality of life on Earth. In this study, advanced soft computing techniques, including long short-term memory (LSTM), convolutional neural network–recurrent neural network (CNN-RNN), and group method of data handling (GMDH) algorithms, were employed to forecast monthly streamflow time series at two different stations in the Wadi Mina basin. The performance of each technique was evaluated using statistical criteria such as mean square error (MSE), mean bias error (MBE), mean absolute error (MAE), and the correlation coefficient (R). The results of this study demonstrated that the GMDH algorithm produced the most accurate forecasts at the Sidi AEK Djillali station, with metrics of MSE: 0.132, MAE: 0.185, MBE: −0.008, and R: 0.636. Similarly, the CNN-RNN algorithm achieved the best performance at the Kef Mehboula station, with metrics of MSE: 0.298, MAE: 0.335, MBE: −0.018, and R: 0.597.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2025Sustainable aviation fuel supplier evaluation for airlines through LOPCOW and MARCOS approaches with interval-valued fuzzy neutrosophic information
Ecer, Fatih; Tanrıverdi, Gökhan; Yaşar, Mehmet; Görçün, Ömer Faruk
In line with the 2050 net zero emission target, sustainable aviation fuel (SAF) is recognized as one of the most effective decarbonization solutions for the aviation industry, which has been identified among the critical areas for mitigating climate change. However, although sustainability issues and decarbonization have attracted scholars' attention in various terms for the airline industry, we identified some significant theoretical and managerial gaps as follows: (i) the number of studies evaluating sustainable suppliers by airlines via multi-criteria decision-making (MCDM) approaches are very few, (ii) the extant literature has no paper addressing airlines' SAF supplier selection process, and (iii) no widely established criteria set in the literature to evaluate the SAF suppliers for airlines. We propose a novel model for the decision-making process of airlines' sustainable SAF supplier selection, including 39 criteria from 5 aspects considering the triple bottom of sustainability. The proposed model involves the combination of the logarithmic percentage change-driven objective weighting (LOPCOW) and measurement alternatives and ranking according to the compromise solution (MARCOS) approaches' extended forms based on the interval-valued fuzzy neutrosophic numbers (IVFNN). A comprehensive sensitivity and comparison control is further exploited to display the developed framework's robustness and practicality. Our results suggest that airlines prioritize the green initiatives of SAF suppliers over the economic aspect in the process of sustainable SAF supplier selection. We provide some managerial and policy insights for practitioners and policy-makers in the airline industry and some directions for further research.
RDL Kalıcı Kaydıİncele - Conference ObjectYalnız künye2025Modeling of Meteorological Drought Under Climate Variability Using the Standardizes Precipitation Evaporation Index (SPEI) and Machines Learning Models for Wadi Ouahrane Basin, Algeria
Achite, Mohammed; Emami, Somayeh; Katipoğlu, Okan Mert; Toubal, Abderrezak Kamel; Zhang, Z.; Chenchouni, H.; Sarikaya, M.A.; Ciner, A.; Knight, J.; Gentilucci, M.
Machine learning algorithms are an important tool in capturing complex relationships and modeling high-dimensional climate data and drought severities. The main innovation of this study is the evaluation of the accuracy and applicability of augmented tree and kernel-based machine learning algorithms in standardized precipitation evapotranspiration index (SPEI) prediction. Rainfall and temperature data obtained from the ONM Department of Chlef City in the Wadi Ouahrane basin (270 km2), from 1973 to 2017 were employed to predict SPEI at a monthly time scale. In the model’s setup, two different models, Gradient Boosted Trees (GBT) and Support vector machine (SVM), were employed. The determination coefficients (R2), root mean square error (RMSE), mean absolute error (MAE) were employed to evaluate the model performances. In terms of accuracy and efficiency, the SVM model (R2 = 0.957, RMSE = 0.216, and MAE = 0.142) exhibits superiority over the GBT (R2 = 0.870, RMSE = 0.369, and MAE = 0.293) for SPEI estimation. Therefore, SVM model has been proposed for drought prediction at various time scales. The results of the study contribute to the understanding of drought dynamics and decision-making by policy makers in water resources management.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2025Nature’s Guidance: Employing Bio-inspired Algorithm and Data-Driven Model for Simulating Monthly Maximum and Average Temperature Time Series in the Middle Black Sea Region of Türkiye
Katipoğlu, Okan Mert; Terzioğlu, Zeynep Özge; Zerouali, Bilel
This study compares the performance of various models in predicting monthly maximum and average temperatures across three distinct regions: Samsun, Amasya, and Çorum. The evaluated models include Artificial Neural Network (ANN), Shuffled Frog Leaping Algorithm coupled with ANN (SFLA-ANN), Firefly Algorithm coupled with ANN (FFA-ANN), and Genetic Algorithm coupled with ANN (GA-ANN). In setting up the models, the dataset was divided into 70% for training and 30% for testing, and the outputs of the models were evaluated using various graphical and statistical indicators. The model with the smallest root mean square error (RMSE) value was selected for the maximum and average temperature predictions. Accordingly, for maximum and average temperature predictions, SFLA-ANN (RMSE of 2.93) and GA-ANN (RMSE of 3.55) in Samsun, GA-ANN (RMSE of 2.91) and GA-ANN (RMSE of 2.50) in Amasya and GA-ANN (RMSE of 2.97) and GA-ANN (RMSE of 2.50) in Çorum performed better than the other models, respectively. In addition, for the maximum temperature prediction with the highest accuracy, the R2 value of the SFLA-ANN model in Samsun was 0.89. In contrast, the R2 values of the GA-ANN model in Amasya and Çorum were determined as 0.91 and 0.91, respectively. Similarly, it was observed that the R2 values of the GA-ANN model for the average temperature prediction with the highest accuracy at Samsun, Amasya and Çorum stations were 0.78, 0.92 and 0.92, respectively. Overall, the GA-ANN consistently demonstrated superior performance in predicting both maximum and average temperatures across all three regions, as evidenced by its consistently low RMSE values. These findings provide valuable insights into selecting effective models for temperature prediction tasks in different geographical regions.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2025Model performance evaluation with Siamese neural network in hydrologic modelling
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.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2025Enhancing streamflow drought prediction: integrating wavelet decomposition with deep learning and quantile regression neural network models
Mohammadi, Babak; Abdallah, Mohammed; Oucheikh, Rachid; Katipoğlu, Okan Mert; Cheraghalizadeh, Majid
Drought is a significant natural hazard that severely challenges water resource management and agricultural sustainability. This study aims to propose a novel approach for predicting streamflow drought indices (SDI-3, SDI-6, and SDI-12) in humid continental (Stockholm) and semi-arid (ELdiem) climates at different time-steps. The approach utilizes a Quantile Regression Neural Network (QRNN) coupled with wavelet decomposition (WD) techniques. Six mother wavelets (haar, sym8, coif5, bior6.8, demy, and db10) were used to decompose the SDI time series into different frequency bands, helping to identify patterns and trends in drought signals. The QRNN was compared with a tree-based machine learning (ML) model and two deep learning models: Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN). Results from stand-alone models showed that the LSTM model outperformed others in predicting SDI-3, while the QRNN model performed best in predicting SDI-6 and SDI-12 in both study regions. In the Stockholm station, the hybrid models achieved acceptable accuracy with bior6.8-LSTM2 (Nash–Sutcliffe efficiency (NSE) = 0.927), bior6.8-QRNN2 (NSE = 0.962), and demy-QRNN2 (NSE = 0.984) performing best for SDI-3, SDI-6, and SDI-12 predictions during the test phase, respectively. For the ELdiem station, the db10-QRNN3 (NSE = 0.926), demy-QRNN3 (NSE = 0.934), and demy-QRNN2 (NSE = 0.981) models demonstrated superior performance during the test phase in predicting SDI-3, SDI-6, and SDI-12, highlighting the robust capability of hybrid models across two case studies. The results indicate that combining WD with ML models can produce more accurate hydrological drought predictions than traditional models.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2025Explaining building energy efficiency prediction through architectural and engineering solutions considering environmental impacts using a hybrid model
Aslay, Semi Emrah
The aim of this study is to investigate building energy efficiency by integrating hybrid modelling approaches and interpretable models into architectural design processes and engineering solutions while considering environmental impacts. At the same time, it is aimed to achieve the most efficient building design possible. Separate analyses were carried out using a total of 6,913 data from Rutland (185) and Salford (6718) cities. The data were grouped into carbon emission information, architectural information, lighting information, personal heating information, and main heating system information to form a dataset. Light Gradient Boosting Machine (LightGBM) was preferred as the base model and Particle Swarm Optimisation (PSO) method was applied for hyperparameter optimisation. The hybrid model created in this way is called PSO-LightGBM. The optimization process was carried out using software in both R Studio and Python environments, utilizing seven different hyperparameters. Apart from the hybrid model used as a method, 2 different SHAP analyses, neural network based, and tree based, were performed to clearly explain the parameter relationships. The PSO-LightGBM hybrid model provided more successful predictions compared to the basic LightGBM model. While R2 values improved between 0.82 and 0.90 in the Rutland dataset, this value increased from 0.8687 to 0.8901 for test data and from 0.8538 to 0.9091 for training data in the Salford dataset. R2 values show an improvement of 7% in the Rutland dataset and maximum 6% in the Salford dataset. When the reduction in error rates is evaluated, it is found that the greatest improvement is in the Mean Squared Error (MSE) metric. MSE decreased by 17% in the Rutland dataset and by 4% in the Salford dataset. According to the SHAP Analysis results, CO2 emissions have the largest impact on energy consumption, while primary fuel types, number heated rooms and individual heating systems are other important parameters. While the tree-based SHAP model is more sensitive to physical parameters, the neural network model is more sensitive to indirect relationships. In both analyses, the communal heating system type has the lowest impact. In order to improve building energy efficiency, high efficiency individual boiler systems should be preferred, architectural approaches that optimise the number of heated rooms and smart heating solutions should be used, and central heating systems should be modernised. The results highlight the effectiveness of hybrid modelling approaches with SHAP analyses based on different baselines to ensure the integration of environmental impacts, architectural design processes and engineering solutions in terms of building energy efficiency. Furthermore, the findings contribute to the importance of interdisciplinary work in buildings to improve energy efficiency. Future studies can focus on the development of building energy performance prediction models that can be explained by integrating various learning algorithms and different optimisation techniques, using regions with high cooling demand, large datasets and different building types.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2025Machine learning-driven flood susceptibility mapping: comparing model performances and feature influences in a coastal watershed of the Eastern mediterranean
Abdo, Hazem Ghassan; Richi, Sahar Mohammed; Bindajam, Ahmed Ali; Zerouali, Bilel; Katipoğlu, Okan Mert; Prasad, Pankaj; Alharbi, Maged Muteb; Ramadan, Mona S.; Mallick, Javed; Ghribi, Mounir
Flood susceptibility mapping is crucial for effective disaster management and mitigation in regions prone to extreme weather events. Despite advances in the use of conventional models for flood mapping, coastal basins in the Eastern Mediterranean still lack studies that employ machine learning (ML) techniques to accurately estimate flood risk. Most research focuses on quantitative prediction without systematically elucidating the influencing environmental factors, limiting the effectiveness of the results in applied contexts. Hence, the need to develop machine learning-based explanatory models that not only increase prediction accuracy but also reveal the most important determinants of flooding in complex coastal environments. This study assesses flood susceptibility using three ML algorithms, including, Light Gradient Boosting Machine (LGBM), Artificial neural networks (ANN), and eXtreme Gradient Boosting (XGBoost), with feature importance analysis conducted using SHapley Additive exPlanations (SHAP). The XGBoost model demonstrated superior performance in identifying flood susceptibility areas, compared to ANN, and LGBM. Feature importance analysis revealed that Aspect, Distance from River, and Slope were the most influential features, with Aspect being the most dominant predictor of flood susceptibility. The methodological integration of XGBoost with SHAP not only achieved high performance, but also provided interpretable insights into environmental drivers of flood risk. This combination represents a significant advancement over prior approaches, as it bridges predictive power with explainability, thereby enhancing the reliability of flood susceptibility mapping. The findings contribute to improved flood risk management strategies and lay the foundation for future research on interpretable and robust ML models for flood prediction.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2025Building energy prediction model with AI-based PSO-ANN approach integrating architectural and HVAC processes
Aslay, Semi Emrah
Nowadays, due to the increasing energy demand, the importance of research on energy consumption estimation in buildings is increasing. This study aims to estimate and interpret energy consumption in buildings by integrating the processes of architecture and heating-ventilation-air conditioning (HVAC) components with an approach that combines environmental impact. For this purpose, the data were obtained from residential buildings in Nottingham and City of London, and the hybrid artificial neural network algorithm and Shapley Additive Explanations (SHAP) analyses were used as methods. To improve the performance of the Artificial Neural Network (ANN) model, a hybrid method was created with Particle Swarm Optimisation (PSO) (PSO-ANN). In order to ensure the interpretability of the model, both tree-based and neural network-based SHAP method were used. The outputs of this AI-supported method were compared and interpreted. The hybrid model developed in this study achieved an R-squared value of 0.99 in the test data sets in both data groups, which is a significant improvement compared to standard ANN models (0.95–0.97). In addition, when the training data is also included, the overall improvement rate is calculated as 3–5 %. According to the results of the analyses, CO₂ emission rate and total area are the factors that affect the energy consumption the most. In addition, the positive effects of heating control systems and lighting factors on energy efficiency are evaluated, and their importance is emphasized. These results provide an effective prediction model that combines building energy consumption and environmental performance and integrates the processes of architecture and HVAC components.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2025Deep learning and adaptive boosting for hydroelectric power prediction using hydro-meteorological data: Insights and feature importance analysis
Karakoyun, Yakup; Katipoğlu, Okan Mert; Dogan, Ahmet
This study explores the use of modern artificial intelligence (AI) models such as Adaptive Boosting (AdaBoost), Autoencoder Based Regression Model (Autoencoder), Deep Neural Network (DNN), Echo State Network (ESN), Light Gradient Boosting Machine (LGBM) Residual Neural Network (ResNet) and feature importance techniques to predict energy production. Daily rainfall, streamflow, temperature and energy production data between 2016 and 2019 were used in the study. According to the feature importance analysis performed using the LGBM model, streamflow, temperature, and precipitation were identified as the most critical parameters influencing energy production. This structured AI-driven approach provided robust model evaluation and reliable performance measurements. As revealed by the analysis, the LGBM model showed the most accurate result with the values of root mean square error (RMSE: 98.27), mean absolute error (MAE: 73.93), Akaike information criterion (AIC: 17,100), Nash–Sutcliffe efficiency coefficient (NSE: 0.69), Kling–Gupta efficiency (KGE: 0.77), coefficient of determination (R2: 0.69), mean bias error (MBE: −5.23), bias factor (BF: 1.02), and percent bias (PBIAS: 1.51), while the DNN model came in with the second-best results. These findings emphasize both the effectiveness of deep learning and ensemble AI models and their practical application in improving hydroelectric power generation forecasting. Furthermore, the study underscores the importance of accurately monitoring and managing streamflow for the optimization of hydroelectric systems.
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 künye2025Validation of the EUMETSAT CMSAF global interpolated gridded rainfall estimation (GIRAFE v1) dataset for Türkiye
Pekin, Muhammet Ali; Katipoğlu, Okan Mert; Şahin Demirbağ, Nurdan
GIRAFE V1 is a satellite-based global precipitation dataset developed by EUMETSAT under the CMSAF program and released in 2024. This dataset was designed to support various applications such as climate analysis, hydrological modeling, and the investigation of long-term precipitation trends. It provides precipitation data with a spatial resolution of 1 × 1 degree and temporal resolutions at daily and monthly scales, covering the period from 2002 to 2022. In this study, the verification of the GIRAFE V1 data set was made according to the correlation coefficient (CC), Nash–Sutcliffe Efficiency, Kling-Gupta Efficiency, Root Mean Squared Error and Mean Error values by using the monthly total precipitation data of 79 observation points in Türkiye. Validation is handled both for each grid separately and for the entire data set. According to the findings obtained, it was seen that the data set was consistent at the rate of CC = 0.73 for the whole of Türkiye and for the 2002–2022 period. While the data set was CC = 0.79 in summer, it decreased to CC = 0.69 in winter and spring. After all, GIRAFE V1; It is emphasized that it can be selected as an alternative data set to traditional precipitation observation data, other satellite-based precipitation data and reanalysis precipitation data. The results of the study contribute to the use of an alternative dataset for flood, drought and environmental management applications for decision makers and planners.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2025Investigating the Performance of Machine Learning Models in Estimating Monthly Dam Evaporation: A Case Study of Sidi Mhamed Ben Aouda Dam, Wadi Mina Basin, Algeria
Achite, Mohammed; Emami, Somayeh; Emami, Hojjat; Katipoğlu, Okan Mert; Pandey, Kusum; Hajimirzajan, Amir; Elshaboury, Nehal
Effective water resource management in arid and semi-arid regions requires accurate estimation of hydrological parameters such as evaporation. This study investigates the monthly evaporation of the Sidi-M’Hamed Ben Aouda dam basin in northwest Algeria using six machine learning models: random forest (RF), extra tree (ET), gradient boosting (GB), category boosting (CatBoost), light gradient boosting machine (LGBM), and multi-layer perceptron (MLP). Climatic inputs (temperature, relative humidity, wind speed, and sunshine hours) from 1978 to 2023 were used to train and test the models. The findings reveal that the ET model achieved the best balance between accuracy and computational speed, while the RF model provided the highest overall accuracy. GB had a faster runtime with slightly reduced accuracy, whereas CatBoost and MLP underperformed. This comparative analysis highlights the suitability of ensemble tree-based models, particularly RF and ET, for accurate and efficient evaporation prediction, supporting water resource planning in data-scarce and climate-sensitive regions.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2025Assessing the influence of green innovation and environmental policy stringency on CO2 emissions in BRICS
Çetinkaya, Ozlem Akguc; Çatik, Abdurrahman Nazif; Balli, Esra; Manga, Muge; Destek, Mehmet Akif
This article examines the effect of environmental policy stringency and green innovation on CO2 emissions in the BRICS nations, using annual data from 1990 to 2019 utilizing panel FMOLS and DOLS estimators and Method of Moments Quantile Regression (MMQR). To this end, we estimate an equation in which CO2 emissions are explained by GDP, trade openness, nonrenewable and renewable energy consumption, the environmental stringency index, and green innovation, as measured by the number of patent applications for environmentally related inventions. FMOLS and DOLS results reveal that GDP, nonrenewable energy consumption, and trade openness have a positive effect on environmental pollution, whereas improvements in renewable energy consumption and environmental regulations lead to a drop in CO2 emissions. However, green innovation does not have a significant effect on CO2 emissions. MMQR estimates demonstrate that the GDP has a positive effect on CO2 emissions across all quantiles, suggesting that a higher degree of economic growth is associated with higher emissions. Based on findings, empirical evidence suggests that BRICS countries should follow the policies encouraging the reduction of nonrenewable energy consumption in the region without harming the development of the economy. Besides, policymakers should promote renewable energy consumption and enhance investment in green innovation to achieve sustainable development and environmental quality.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2025Hydroclimatic drought trends and change point detection in the Yeşilirmak basin: a comprehensive evaluation using SPI, SPEI, DMI indices and ITA, SQMK, IPTA approaches
Öllükçü, Meva Nur; Katipoğlu, Okan Mert
Drought was defined as water scarcity resulting from below-normal precipitation in a specific region over an extended period. Analysis of droughts was of vital importance in terms of water resources planning and management, hydropower generation, irrigation planning, agricultural productivity, and reservoir planning. In this study, drought status was evaluated according to the Standard Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), and De Martonne Index (DMI) values calculated between 1979 and 2022 for seasonal and annual periods for Amasya, Çorum, Samsun, and Tokat meteorological stations located in the Yeşilırmak Basin, and station-based drought percentages were expressed. In addition, trend and change point analyses were conducted for each drought index according to Şen’s Innovative Trend Analysis (ITA), Sequential Mann-Kendall (SQMK), and Innovative Polygon Trend Analysis (IPTA) methods. The analysis identified the trends and onset years of droughts in the region, revealing that severe droughts were dominant at a 15% level. ITA results indicated a significant decreasing trend of 5–10% in spring, summer, autumn, and annual periods, alongside an increasing drought trend. SQMK analyses confirmed a general decreasing trend at the stations, while IPTA highlighted the dominance of increasing drought intensities in SPI, SPEI, and DMI trends. These findings underscored the need for policymakers to take proactive measures to mitigate drought risks in the Yeşilırmak Basin.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2025Appropriate Intersection Design Type Based on TOPSIS Multiple Criteria Decision Method with AIMSUN Mesoscopic Simulation
Bayata, Halim Ferit; Baş, Fatih İrfan; Demircioğlu, Muhammed Sami; Çolak, Muhammed Ali
The increasing world population has also increased the demand for vehicles and brought along various problems such as traffic delays, accidents, and air pollution. In addition, the increase in the number of vehicles causes an increase in the time spent at intersections, negatively affecting people’s quality of life, distracting drivers, and unsafe driving practices. Moreover, greenhouse gas emissions, including CO2 and NOx from vehicles, cause climate change and deterioration of air quality. To solve these problems, in this study, seven signalized intersections on a corridor in the city center of Erzincan province were examined. Using the Aimsun traffic simulation program, factors such as CO2, VOC, NOx emissions, queue length, delay time, travel time, and speed were evaluated. Using one of the multiple criteria decision methods, the TOPSIS method, the ideal scenario was determined based on the simulation results, considering the construction costs. The study revealed significant improvements in several parameters. CO2 emissions were reduced by 10%, delay time by 35%, VOC emissions by 14%, NOx emissions by 13%, queue length by 35%, delay by 38%, and travel by 19%. Furthermore, the speed was increased by 15%.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2025Air temperature estimation and modeling using data driven techniques based on best subset regression model in Egypt
Elbeltagi, Ahmed; Vishwakarma, Dinesh Kumar; Katipoğlu, Okan Mert; Sushanth, Kallem; Heddam, Salim; Singh, Bhaskar Pratap; Shukla, Abhishek; Gautam, Vinay Kumar; Pande, Chaitanya Baliram; Hussain, Saddam
Air temperature plays a critical role in estimating agricultural water requirements, hydrological processes, and the climate change impacts. This study aims to identify the most accurate forecasting model for daily minimum (Tmin) and maximum (Tmax) temperatures in a semi-arid environment. Five machine learning models—linear regression (LR), additive regression (AR), support vector machine (SVM), random subspace (RSS), and M5 pruned (M5P)—were compared for Tmax and Tmin forecasting in Gharbia Governorate, Egypt, using data from 1979 to 2014. The dataset was divided into 75% for training and 25% for testing. Model input combinations were selected based on best subset regression analysis, result shows the best combination was Tmin(t−1), Tmin(t−3), Tmin(t−4), Tmin(t−5), Tmin(t−6), Tmin(t−7), Tmin(t−8) and Tmax (t−1), Tmax (t−2), Tmax (t−3), Tmax (t−4), Tmax (t−5), Tmax (t−6), Tmax (t−8) for daily minimum maximum air temperature forecasting, respectively. The M5P model outperformed the other models in predicting both Tmax and Tmin. For Tmin, the M5P model achieved the lowest root mean square error (RMSE) of 2.4881 °C, mean absolute error (MAE) of 1.9515, and relative absolute error (RAE) of 40.4887, alongside the highest Nash-Sutcliffe efficiency (NSE) of 0.8048 and Pearson correlation coefficient (PCC) of 0.8971. In Tmax forecasting, M5P showed a lower RMSE of 2.7696 °C, MAE of 1.9867, RAE of 29.5440, and higher NSE of 0.8720 and R² of 0.8720. These results suggest that M5P is a robust and precise model for temperature forecasting, significantly outperforming LR, AR, RSS, and SVM models. The findings provide valuable insights for improving decision-making in areas such as water resource management, irrigation systems, and agricultural productivity, offering a reliable tool for enhancing operational efficiency and sustainability in semi-arid regions. The Friedman ANOVA and Dunn’s test confirm significant differences among temperature forecasting models. Additive Regression overestimates, while Linear Regression and SVM align closely with actual values. Random Subspace and M5P exhibit high variability, with SVM differing significantly. For maximum temperature, Random Subspace and M5P perform similarly, while SVM remains distinct.
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 künye2025Performance enhancement of daily reservoir evaporation rate estimation models using stacking regression by discretization with AI methods
Achite, Mohammed; Katipoglu, Okan Mert; Elbeltagi, Ahmed; Elshaboury, Nehal; Pandey, Kusum; Emami, Somayeh; Yongguo, Chen
Accurate estimation of evaporation from surface water bodies is essential for sustainable water resource planning and operational hydrology. However, direct measurement over reservoirs is complex and often impractical. In this study, an advanced machine learning framework based on Regression by Discretization (RD) was applied to predict daily reservoir evaporation at the Sidi-M’Hamed Ben Aouda Dam Basin in Algeria from 2010 to 2023. Among the models evaluated, the RD-Bagging model demonstrated superior performance with a correlation coefficient (CC) of 0.7849, mean absolute error (MAE) of 0.0139, root mean square error (RMSE) of 0.0172, and percent bias (PBIAS) of -1.42% during the testing period. For the validation period, RD-Bagging yielded CC, MAE, RMSE, and PBIAS values of 0.822, 0.0119, 0.0154, and − 1.18%, respectively, indicating high predictive accuracy and low bias. The results confirm the robustness and generalization ability of the RD-Bagging model, making it a reliable tool for reservoir evaporation prediction. This study provides a foundation for the development of data-driven decision support systems in hydrological modeling and climate resilience planning.
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