Yazarlar
- Katipoğlu, Okan Mert(75)
- Achite, Mohammed(21)
- Kartal, Veysi(15)
- Elshaboury, Nehal(12)
- Sarıgöl, Metin(12)
- Ertugay, Neşe(9)
- Katipoglu, Okan Mert(7)
- Pande, Chaitanya Baliram(6)
- Saroughi, Mohsen(6)
- Yavuz, Emre(6)
- Aktürk, Gaye(5)
- Caglar, Bulent(5)
- Elbeltagi, Ahmed(5)
- Gürses, Ahmet(5)
- Keblouti, Mehdi(5)
- Kilinc, Huseyin Cagan(5)
- Pandey, Kusum(5)
- Samui, Pijush(5)
- Acar, Reşat(4)
- Bazrafshan, Ommolbanin(4)
- Dalkilic, Huseyin Yildirim(4)
- Dalkılıç, Hüseyin Yıldırım(4)
- Emami, Somayeh(4)
- Ertugay, Nese(4)
- Jehanzaib, Muhammad(4)
- Kocakaplan, Nihal(4)
- Malkoç, Emine(4)
- Mirzania, Ehsan(4)
- Mohammadi, Babak(4)
- Simsek, Oguz(4)
- Abdallah, Mohammed(3)
- Bailek, Nadjem(3)
- Berhail, Sabri(3)
- El Nemr, Ahmed(3)
- Güner, Eda Keleş(3)
- Karakoyun, Erkan(3)
- Ozkan, Furkan(3)
- Santos, Celso Augusto Guimarães(3)
- Sökmen, Teoman Özgür(3)
- Uzun, Sinem(3)
Yayınlar
- 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 Erişim2025Children’s attitudes towards water: a scale development study
Aysu, Burçin; Aral, Neriman; Aydoğdu, Fatih; Gürsoy, Figen
Introduction: In this study, it was aimed to develop a scale to determine children’s attitudes towards water. Methods: The study included 865 children studying in the fourth to eighth grades. The items of the scale were formed based on the theoretical structure, a comprehensive review of existing scales related to attitudes towards water and expert evaluations. Content validity, exploratory factor analysis, confirmatory factor analysis and reliability analyses were performed from psychometric analyses to determine the validity and reliability features. Results: As a result of the study, a scale consisting of three factors (water cycle, importance of water, water saving) and 24 items with acceptable validity and reliability values was obtained. Discussion: The scale has reliable psychometric properties and can be used as an important assessment tool in evaluating children’s attitudes towards water.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2025Superior photocatalytic performance of ZnMoO4/Ag2WO4 for degradation of trimethoprim and methylene blue
Yavuz, Sevtap Caglar; Yavuz, Emre; Ozdokur, Kemal Volkan
In the present work, ZnMoO4/Ag2WO4 nanocomposite was synthesized via facile hydrothermal and chemical precipitation method for determination of photocatalytic performance on organic contaminants such as methylene blue and trimethoprim. The synthesized nano-photocatalyst was characterized using various instrumental techniques. Photocatalytic degradation of methylene blue and trimethoprim by the samples was realized under UV light irradiation. The obtained results demonstrated that photocatalytic degradation of methylene blue by ZnMoO4/Ag2WO4 was 99.6% in 30 min. The bare ZnMoO4 produced 57.8% and Ag2WO4 produced 76.7% photocatalytic degradation of methylene blue in 60 min. The synthesized ZnMoO4/Ag2WO4 nanocomposite, about which there is little information on remediation of organic pollutants, had possessed the outstanding photocatalytic degradation performance on methylene blue and trimethoprim due to pseudo first order kinetic.
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 künye2025Developing sediment concentration prediction in the Euphrates River catchment, Türkiye, with a honey badger and coati optimization-based hybrid algorithm
Saroughi, Mohsen; Katipoğlu, Okan Mert; Kartal, Veysi; Simsek, Oguz; Kilinc, Huseyin Cagan; Pande, Chaitanya Baliram
Estimation of sediment concentration (SC) is of vital importance in terms of siltation and economic life of dams, lakes and aqueducts, reservoir operations, design of water resource structures, monitoring and control of water pollution, and flood management. Direct measurement of SC is a challenging and expensive task. For these reasons, it was used to estimate the SC values at a station in the Euphrates River. New hybrid models were established by combining the CatBoost regressor (CBR) and artificial neural network (ANN) models with the honey badger optimization algorithm (HBA) and coati optimization algorithm (COA). The performance of the new model was compared with stand-alone model of ANN and CBR, and their accuracy was evaluated. In the setup of the models, 4 different input combinations of lagged sediment and discharge values for up to 3 months were evaluated. It is noteworthy that as the number of input variables, i.e., lagged data input, increases, the prediction accuracy of the models generally increases. HBA and COA algorithms often improve the accuracy of sediment prediction by optimizing the parameters of the single machine learning model. In addition, according to the AIC performance metric, the HBA algorithm is generally slightly better capable of optimization than the COA. The best model outputs were obtained according to the HBA-CBR hybrid approach of scenario 4 (RMSE = 59.78, AIC = 785.03, R2 = 0.32, PBIAS = 0.016, SI = 0.48, and MBE = − 2.05 in test phase), which consists of discharge and sediment with a delay of up to 3 months. The results of the study are valuable for decision-makers and planners in terms of practical reservoir and flood management and protection of coasts and river beds.
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 künye2025Daily prediction of Urmia Lake water level using remote sensing data and honey badger optimization-based data-driven models
Saroughi, Mohsen; Katipoğlu, Okan Mert; Aktürk, Gaye; Gul, Enes; Simsek, Oguz; Citakoglu, Hatice
Artificial neural networks (ANNs), support vector regression (SVR) and CatBoost regression (CBR) machine learning methods have been combined with the honey badger optimization algorithm (HBA) and metaheuristic optimization algorithm to accurately and reliably predict lake water level (LWL), which is of great importance for the management and planning of water resources. In this study, meteorological and hydrological parameters, including temperature (T), precipitation (P), date (D), surface soil moisture (SSW), root zone moisture (RZW) and water level (WL), were employed as input data for predicting the LWL of Urmia Lake. The input data were employed to develop six different prediction scenarios. This study not only examined the impact of meteorological and hydrological parameters on LWL prediction but also compared the performance of individual models and hybrid models. The Akaike information criterion (AIC) index was used to ascertain the optimal machine learning model and to evaluate the six prediction scenarios. The results of the study indicate that, according to the AIC index, the data regarding the water level (WL) were significant in the prediction models. However, it should be noted that satisfactory results could also be obtained without using the WL data in certain scenarios. In scenario 4 (input data: D, T, P, SSW, RZW), where the WL variable was not included, the HBA-CBR hybrid model was the best model with the lowest AIC value (Train: -63,735, Test:-4693). In prediction scenario 6 (input data: D, T, P, SSW, RZW, WL), which included the WL data, the HBA-SVR hybrid model demonstrated high performance with the lowest AIC value (Train: -102,358, Test:-27,233). Accordingly, it was recommended to use lagged WL values as input in WL prediction because the prediction accuracy of the models significantly improved. Furthermore, hybrid models were found to perform better than individual models due to their more consistent results.
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 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 - 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.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2025Comparative study of different estimation approaches to quantify suspended sediment transport at hydrometric stations: case of the wadi Mina basin, Northwest Algeria
Achite, M.; Toubal, K.A.; Katipoğlu, O.M.; Elshaboury, N.
The lack of sediment transport concentration measurements at the hydrometric stations justifies the choice of the methodological approach adopted. This is based essentially on statistical models for the homogenization of data and their extension to quantify the water erosion of the basin area. The Oued Abtal and Sidi Abdelkader Djillali hydrometric stations, each controlling the wadi Mina and wadi Haddad sub-basins upstream of the Sidi M’hamed Ben Aouda dam, were the subject of this evaluation methodology. This article aims to find the best approach among the seven proposed. Model results were tested using the Pearson correlation coefficient, root mean square error, index of agreement, and mean absolute error. Based on the comparison of statistical metrics, it is evident that the 6th and 7th approaches stand out as the top performers, providing results that closely resemble reality. The approach that employs homogenization and extension of data from instantaneous water level measurements to liquid flows, utilizing the solid flow-liquid flow relationship developed at flood scale, seems to outperform the alternative approaches. Moreover, we found that these approaches fall into three categories: one that gives values close to the reference approach and two others that underestimate and overestimate the suspended solids transport values. The difference between approaches is strongly related to the variation of the flood ratio (FR), the ratio of observed (gauged) floods to recorded floods.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2025A novel integrated approach to predict the sodium absorption ratio (SAR) of groundwater sustainability using deep learning models and SHAP approach
Moharir, Kanak N.; Pande, Chaitanya Baliram; Chakrabortty, Rabin; Pramanik, Malay; Paneerselvam, Balamurugan; Katipoğlu, Okan Mert; Pal, Subodh Chandra; Scholz, Miklas; Yadav, Krishna Kumar; Mansour, Lamjed
Agriculture is a crucial factor in improving the country economic growth. With the massive impact of environment variation, supplying and measuring the quality of water for irrigation usage is a crucial task for water resource management authority. The demand for good quality water for both drinking and irrigation uses is increasing day by day in recent years. The main aim of the present study is to identify the suitability of surface water for irrigation uses in the Man River basin of Maharashtra, India using advanced techniques. These region first time is used such kind of models i.e. boosted tree, AdaBoost, decision tree, extremely randomized tree model, and feed-forward neural network (deep learning) models, and these models are better to analyze and understand the groundwater quality datasets in the saline area. The prediction results are evaluated with the use of different performance metrics, mean absolute error (MAE), mean absolute relative error (MARE), Nash Sutcliffe efficiency (NSE), root mean squared error (RMSE), and coefficient of determination (R2). The study identified that the boosted tree model is more appropriate for the SAR prediction value, this model with high accuracy compared with other models. The result of this study shows that boosted tree model is very suitable for prediction of SAR and also provided the accurate information for agriculture purposes. In the first scenario, the boosted tree model shows lower value of mean squared error (MSE) of 0.26 and higher R2 of 0.88, the second scenario shows lower value of MSE of 0.11 and higher value of R2 of 0.91. Overall, in both the scenarios, the result of boosted tree model is more favorable for SAR% prediction. This work shows that machine and deep learning models can improve and better prediction of the groundwater quality in the study area. It is essential to understand the water quality and improve the sustainable agriculture system and development. These advanced modeling methods help to stakeholders make better water management and irrigation decisions, boosting agricultural sustainability and productivity.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2025Analysis of drought patterns and categories based on the Standard Precipitation-Temperature Index (SPTI): application of fuzzy c-means clustering and principal component analysis
Berhail, Sabri; Katipoğlu, Okan Mert
This study deals with climate variability issues in the Kebir Rhumel basin, situated in northeastern Algeria. This region is experiencing significant changes in climate parameters, which will affect the region’s water resources, agriculture, and overall ecosystem health. The main goal of this study is to examine the severity and frequency of drought from 1981 to 2019. It used the Standard Precipitation-Temperature Index (SPTI), which was recently developed, and clustering methodologies such as c-means and k-means to examine the variation in rainfall patterns characterized by intervals of intense drought and heavy precipitation. These methods help assess the impact of these disturbances in water availability, which result in challenges for agricultural productivity and water management. Additionally, this study points out the significant trend in temperatures over the reported timeline, which aligns with global warming patterns; this intensifies the effect of drought and impacts the region’s water resources. The southeastern and western parts of the basin are areas that experience continuous extreme drought, and different levels of severe drought have been observed over decades. When comparing the results with existing methods, this research has revealed unique climatic trends in this basin area through clustering techniques applied to SPTI results. In particular, 45% of the stations indicate moderate wetness, 35% of the stations experience extreme wetness, and 20% of the stations are extremely dry. Each of these climatic patterns poses distinct challenges for water resource management. These findings demonstrate the complex interplay between rainfall variability, temperature increases, and drought severity. They underscore the urgent need for adaptive water management strategies and plans to address climate change impacts.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2025Enhancing hydrological time series forecasting with a hybrid Bayesian-ConvLSTM model optimized by particle swarm optimization
Kilinc, Huseyin Cagan; Apak, Sina; Ergin, Mahmut Esad; Ozkan, Furkan; Katipoğlu, Okan Mert; Yurtsever, Adem
Hydrological time series forecasting often relies on addressing the inherent uncertainties and complex temporal dependencies embedded in the data. This study presents an innovative hybrid framework, the Bayesian-ConvLSTM-PSO model, specifically designed to tackle these challenges. The framework synergistically combines 1D convolutional neural networks (CNNs), a convolutional Bayesian network, multi-head attention, and long short-term memory (LSTM) networks, with parameters optimized through particle swarm optimization (PSO). The fusion of the convolutional Bayesian network and 1D convolutional neural networks enhances feature robustness by capturing both probabilistic uncertainties and spatial patterns effectively. The multi-head attention model further amplifies this by focusing on the most relevant features, improving the learning process and ensuring better representation of complex temporal dependencies. The proposed model is rigorously tested on daily streamflow data from three flow measurement stations (FMS): Ahullu (D14A014), Kızıllı (D14A080), and Erenkaya (D14A127). Experimental results reveal that the Bayesian-ConvLSTM-PSO model achieves significant performance gains across various evaluation metrics, including root mean square error (RMSE), mean absolute error (MAE), determination coefficient (R2), Kling–Gupta efficiency (KGE), and bias factor (BF). Notably, the model demonstrates exceptional accuracy with an R2 of 0.9950, a KGE of 0.9950, and a bias factor of 0.0003, surpassing the results of PSO-1D CNN-LSTM and benchmark models, such as DNN, DNN-LSTM, and 1D ConvLSTM. These compelling findings underscore the potential of the Bayesian-ConvLSTM-PSO framework as a robust and effective tool for applications in river engineering and hydrological time series forecasting.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2025Comparative analysis of Brazil-Russia-India-China-SouthAfrica-Turkiye in terms of environmental pollution indicators using multi criteria decision making methods
Karabulut, Turgut; Manga, Müge
This study aims to determine the ranking of criteria contributing to environmental pollution for Brazil-Russia-India-China-South Africa-Turkiye countries using multi-criteria decision-making methods based on average data for 2023. It also aims to rank the selected countries according to their performance. Environmental pollution criteria include carbon dioxide emissions, ecological footprint, per capita national income, renewable energy in primary energy consumption, trade, net foreign direct investment inflows, population, environmental policy stringency index and environmentally related technologies. The findings were determined according to the expert opinion-based method as follows: fossil energy use (0.123), carbon dioxide emissions (0.118), population (0.108), per capita national income (0.104), trade (0.099), environmental technologies (0.095), net foreign direct investment inflows (0.094), ecological footprint (0.093), environmental policy stringency index (0.090) and renewable energy in primary energy consumption (0.075). Accordingly, the ranking of the countries—from best to worst—was Turkiye, India, Brazil, South Africa, China and Russia. However, according to the objective method, the variable weights were determined as population (0.152), net foreign direct investment inflows (0.121), environmental technologies (0.118), national income per capita (0.108), fossil energy use (0.101), trade (0.095), ecological footprint (0.094), renewable energy in primary energy consumption (0.089), carbon dioxide emissions (0.062), environmental policy stringency index (0.055). Accordingly, the ranking of the countries—from best to worst—was Turkiye, India, South Africa, Brazil, China and Russia.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2025Prediction of the monthly river water level by using ensemble decomposition modeling
Pande, Chaitanya Baliram; Sidek, Lariyah Mohd; Halder, Bijay; Katipoğlu, Okan Mert; Rajput, Jitendra; Alshehri, Fahad; Chakrabortty, Rabin; Pal, Subodh Chandra; Dom, Norlida Mohd; Scholz, Miklas
The decomposition, artificial intelligence (AI) and machine learning (ML) modeling have been important role in hydrological and river basin related prediction and forecasting to help the flood management and sustainable water resources development. In this paper, developed the hybrid modeling combined with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), along with standalone models support vector machine (SVM-linear), and Random Forest (RF), Random Subspace (RS) for accurate prediction of monthly river water level in Sg Muar at Buloh Kasap, Johor station during 2014 to 2023. In this paper, two combinations of variables such as Lags and IMFS are used for development of different models for river water level prediction. Hence, these models are compared and measured the performance of models based on the various statistics metrics. Therefore hybrid models performance is measured based on the coefficient of determination (R2), hence all models results are shown the CEEMDAN-SVM-LINEAR (R2 = 0.87), CEEMDAN-SVM-RBF (R2 = 0.91), CEEMDAN-RF (R2 = 0.98), and CEEMDAN-RS (R2 = 0.88) in the second combination variables, while standalone models performance are shown SVM-Linear (R2 = 0.84), SVM-RBF (R2 = 0.87), RF (R2 = 0.97), and RS (R2 = 0.86) during the training phase stage in the first combination variables. Similarly, in the testing phase, the best two models performances are very well as a CEEMDAN-RF (R2:0.94) and CEEMDAN-RS (R2:0.90) in second combination variables, and the first combination variables based SVM- Linear (R2:0.93) and RF (R2:0.89) models are performance higher compared with other models. Finally, the CEEMDAN-RF hybrid model is best model based on the lowest observed errors of Root mean square error (RMSE): 0.13, Mean square error (MSE): 0.02 and high R2: 0.94, hence this model is appropriate for prediction of river water level. Hence, the best hybrid model has been concluded that the CEEMDAN data decomposition technique is very useful for improve performance of the prediction model, the complex river water level predictions by separating the data sets into various sub-frequencies, allowing a better understanding of trends, seasonality and fluctuations in the data. Therefore, the CEEMDAN based novel hybrid modeling is effective decomposition modeling for complex field utilized in the sustainable and optimized utilization of the water resources for sustainable development goal (SDG).
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2025Enhanced thermal performance of a flat-plate solar water heating system using nanofluids: a TRNSYS-based analysis
Uzun, Sinem; Seyhan, Aslıhan Kurnuç
This study examines the potential of nanofluids to enhance the thermal efficiency of flat-plate solar water heating systems (SDHW) through numerical modeling using TRNSYS software. Improving the efficiency of such systems is critical for increasing the viability and sustainability of solar thermal technologies, particularly in regions with high solar potential like Türkiye. Three different nanofluids (Al2O3, TiO2, and CuO) were evaluated for their ability to improve heat transfer and system performance. Simulation results revealed that incorporating 0.1% Al2O3 into the working fluid increased the collector outlet temperature from 97 °C (with pure water) to 120 °C, indicating a 23% improvement. Comparable enhancements were observed with TiO2 and CuO nanofluids. Furthermore, collector efficiency, which typically ranges between 35 and 40% under peak solar conditions, was elevated to approximately 45% with the use of nanofluids. These findings suggest that nanofluids significantly improve thermal performance and energy conversion efficiency in SDHW systems. The results highlight the practical potential of nanofluids in advancing solar thermal technology and promoting cleaner, more efficient renewable energy solutions.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2024Photocatalytic application of nickel oxide/zinc oxide nanoparticles decorated kaolinite nanocomposite for the degradation of crystal violet
Keles Güner, Eda; Ertugay, Nese; Ozdemir, Agah Oktay; Doğan, Bilge; Caglar, Bulent
The photocatalysis process, which is effective, economical, and environmentally friendly can overcome rapidly increasing environmental problems, has shown rapid development in recent years. Nontoxic, low-cost natural minerals are often used in photocatalysis processes that remove organic pollution from wastewater. In this study, a selective method for the removal of crystal violet (CV) from aqueous solutions using nanocomposite of nickel oxide/zinc oxide nanoparticles (NiO/ZnO) supported on kaolinite photocatalyst was investigated. The raw kaolinite, bare NiO, bare ZnO and nanocomposite samples were characterized by the XRD, TEM, SEM-EDX, XPS and FTIR techniques. The photocatalytic performances of kaolinite-NiOZnO (K-NiO/ZnO) nanocomposite and bare samples were also investigated and comparatively evaluated by the photodegradation of CV (cationic dye) dye under visible light irradiation. The effects of various parameters such as the dosage of photocatalysis, contact time, and initial dye concentration on the rate of removal of dye were investigated and optimized. The kinetics of decolorization by samples can be well described by the pseudo-first-order model. Finally, it can be used in advanced oxidative processes for the degradation of organic pollutants.
RDL Kalıcı Kaydıİncele
