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ArticleYalnız künye2026/r/ebyu/11482
Removal of Malachite Green from Aqueous Solutions by Using Bi4V2O11 Nanosheet
Yavuz, Emre; Dundar, Ozan Ali; Yavuz, Sevtap Caglar; Yilmaz, Erkan; Ozdokur, Kemal Volkan; Cirak, Burcu Bozkurt; Caglar, Bulent; Cirak, Cagri; Arar, Özgür
Introduction: Transition metal oxides are highly effective for dye removal due to their adaptable surface chemistry, availability, and robust mechanical and thermal properties. Among them, Bi4V2O11 nanosheets offer potential due to their morphology and high surface area. To synthesize Bi4V2O11 nanosheets and evaluate their performance in removing malachite green, focusing on the impact of adsorbent amount, contact time, and interfering ions on dye removal efficiency. This study aims to evaluate the performance of Bi4V2O11 nanosheets in removing malachite green dye from aqueous solutions and to examine the effects of adsorbent dosage, contact time, and the presence of coexisting ions on the sorption process. Methods: Bi4V2O11 nanosheets were synthesized and used to remove the malachite green dye. Batch experiments were conducted to study the effects of adsorbent dose, contact time, and coexisting ions (Na+, Ca2+, and tannic acid). Equilibrium data were fitted to the Langmuir isotherm model to determine the maximum sorption capacity. Results: Malachite green sorption using Bi4V2O11 nanosheets reached equilibrium within 240 minutes, demonstrating efficient dye removal under the tested conditions. The sorption process conformed to the Langmuir isotherm model, indicating monolayer adsorption with a maximum capacity of 47.27 mg/g. While the presence of Na+ and tannic acid had negligible effects on dye removal efficiency, the coexistence of Ca2+ significantly reduced the removal efficiency from 95% to 58%, highlighting the potential impact of divalent ions on the adsorption process. Discussion: Bi4V2O11 nanosheets are effective adsorbents for malachite green removal, demonstrating high sorption capacity and resilience to Na+ and tannic acid interference. However, the presence of Ca2+ significantly reduces removal efficiency, highlighting the need for further optimization in practical applications. Conclusion: Bi4V2O11 nanosheets offer high sorption capacity and structural stability, environment friendly synthesis procedure, making them feasible for small-scale or pre-treatment applications. 2026, Bentham Science Publishers
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ArticleYalnız künye2026/r/ebyu/10859
Beyond Conventional Methods: Predicting Novel Modified Groundwater Drought Indices with Advanced Computing Paradigms
Katipoğlu, Okan Mert; Kartal, Veysi; Aktürk, Gaye; Kilinc, Huseyin Cagan; Elshaboury, Nehal; Yamany, Mohamed S.; Pande, Chaitanya Baliram
To achieve accurate and reliable predictions of groundwater drought, it is essential to incorporate relevant meteorological variables and apply advanced predictive methodologies capable of modeling their complex interrelationships. In this study, groundwater drought prediction models were developed based on the seven modified drought indices, which are obtained from groundwater levels and multiple meteorological parameters. The analysis focuses on four districts in Türkiye (Tekirdağ-Çorlu, Tekirdağ-Çerkezköy, Amasya-Merzifon, and Amasya-Centre), which are hereafter referred to collectively as the study areas. Three machine learning models were applied such as a standalone Artificial Neural Network (ANN) and two hybrid models, ANN integrated with Shuffled Frog Leaping Algorithm (SFLA-ANN) and Firefly Algorithm (FFA-ANN). The hybrid metaheuristic algorithms were chosen for their ability to model nonlinear and complex relationships between meteorological variables and groundwater levels, which traditional statistical methods cannot fully capture. Modified drought indices were employed to represent the groundwater drought better. The findings indicate that the hybrid FFA-ANN model generally provides the highest predictive accuracy for most indices, with coefficient of determination values frequently exceeding 0.90. In contrast, the ANN model occasionally outperforms the hybrid FFA-ANN model in terms of root mean square error (RMSE) for specific stations within the study areas. These results highlight the robustness of hybrid ANN approaches, especially FFA-ANN, in predicting groundwater drought under changing climate conditions. The novelty of this study is the modification of meteorological drought indices widely used to represent groundwater drought and the application of hybrid metaheuristic ANN models to estimate these indices in Türkiye. The results demonstrated that such models provide a reliable framework for predicting groundwater drought and provide decision-makers with advanced tools for water resources management, irrigation planning, and climate adaptation strategies.
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ArticleYalnız künye2026/r/ebyu/10802
Assessment 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.
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ArticleAçık Erişim2026/r/ebyu/10765
Machine Learning-Based Hydrological Drought Prediction Integrating Teleconnections and Hydrological Memory in a Semi-Arid Basin, Algeria
Katipoğlu, Okan Mert; Achite, Mohammed; Kartal, Veysi; Çelik, Mehmet Ali; Pandey, Kusum
Hydrological drought forecasting in semi-arid basins is challenging due to the combined influence of meteorological forcing, large-scale atmospheric teleconnections, and basin memory processes, which are rarely jointly analysed within a leakage-free predictive framework. This study addresses this gap by evaluating gradient-boosted trees and neural forecasting models for one-month-ahead prediction of the Standardized Runoff Index (SRI) in two sub-basins of the Wadi Sahaouat Basin, Algeria. The models include gradient-boosted regression trees (GBRT), A-N-BEATS, A-N-HiTS, and TiDE, representing distinct forecasting architectures. Predictors consist of the Standardised Precipitation Index (SPI), seven teleconnection indices (NAO, AO, EAWR, SCAND, MEI, SOI, WeMO), and their one- to three-month lags. Two scenarios are tested: Scenario 1 uses SPI and teleconnection lags only, while Scenario 2 additionally includes lagged SRI values (SRI_lag1–3) to represent hydrological memory. A train-only Variance Inflation Factor (VIF > 10) procedure is applied to remove multicollinearity without data leakage. In Basin 1, SRI lags were excluded due to strong collinearity with SPI lags (r = 0.984), resulting in identical inputs for both scenarios. In Basin 2, SRI lags were retained to assess their predictive contribution. GBRT achieved the best overall performance across both basins and scenarios, with mean RMSE, NSE, and KGE values of 0.0682, 0.9907, and 0.8945, respectively. TiDE ranked second overall, with a mean RMSE of 0.1166, followed by A-N-HiTS in third place with a mean RMSE of 0.1203 and A-N-BEATS with the weakest overall performance, with a mean RMSE of 0.2159. These results indicate that gradient-boosted trees remain highly competitive with neural models for small monthly hydrological datasets and that the value of hydrological memory is basin-dependent and varies according to its independence from concurrent meteorological forcing.
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ArticleAçık Erişim2026/r/ebyu/10713
Assessment of Population Parameters for Fisheries Management in a Pressured Ecosystem: A Case Study of Sapanca Lake (Türkiye)
Şen Özdemir, Nurgül; Aydın, Erdinç; Sökmen, Teoman Özgür
Sapanca Lake is a tectonic freshwater lake ecosystem whose water balance and ecological integrity are increasingly threatened by urbanization, pollution, climate change, and declining water levels. To assess recent changes in the fish community, length–weight relationships (LWRs), condition factors (CFs), sex ratios (F/M), and standing stock biomass estimates were determined for eight fish species (Blicca bjoerkna, Scardinius erythrophthalmus, Carassius gibelio, Esox lucius, Silurus glanis, Perca fluviatilis, Abramis brama and Cyprinus carpio) sampled between December 2024 and April 2025. According to standing stock biomass assessments, Cyprinus carpio showed the highest biomass (297.67 tons), while Abramis brama had the lowest (22.72 tons). Most species showed positive allometric growth (b > 3), suggesting generally favorable feeding conditions. In contrast, the main predatory species, E. lucius (b = 2.89) and Silurus glanis (b = 0.21), exhibited negative allometric growth, likely due to limitations in food availability and prey abundance. The CF values were generally >1, indicating good physiological status; however, lower CF values in A. brama (1.18) and B. bjoerkna (1.19) suggest species-specific ecological limitations. Sex ratio analysis revealed pronounced female dominance across species, ranging from complete female dominance (F/M = 1/0) in Carassius gibelio to a female-biased ratio of 1/0.04 in Scardinius erythrophthalmus, likely driven by seasonal sampling effects, sex-specific behavior, and species-specific reproductive strategies. Overall, the results indicate increasing trophic imbalance and ecological stress in Sapanca Lake, emphasizing the need for standing stock biomass assessments and ecosystem-focused fisheries management in tectonic lakes under hydrological pressure.
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ArticleYalnız künye2026/r/ebyu/10677
Change-point detection and trend magnitude estimation in long-term streamflow records of a transboundary river basin under climate change
Tanas Savaş, Gülçin; Ertugay, Neşe; Katipoğlu, Okan Mert
Global climate change is reshaping basin hydrology by altering precipitation timing, evaporative demand, runoff generation, and groundwater recharge, which makes long-term streamflow trend detection essential for sustainable water resources management and climate adaptation. In this study, monthly, seasonal, and annual streamflow trends from three gauging stations in the upper Euphrates basin (D21A001, E21A019, and E21A051) were evaluated using an integrated non-parametric framework that combines Mann–Kendall (MK), Spearman’s rank correlation (SRC), Sequential Mann–Kendall (SMK), innovative trend analysis (ITA), and ITA slope (ITAS). This multi-method design allowed consistent identification of trend direction, magnitude, and temporal transition behavior, while also improving interpretive robustness across stations with different streamflow characteristics. The results show a clear common signal: streamflow weakening is most pronounced in the spring to early summer period, although the strength and timing vary by station. E21A019 shows a relatively coherent declining trend across methods, particularly during April–June and at seasonal and annual scales, indicating a stronger tendency toward streamflow reduction compared to the other stations. E21A051 shows a seasonally contrasting regime, with increasing tendencies in winter and late summer/early autumn (notably January–February and August–September), but decreasing trends during spring and early summer, implying intra-annual redistribution rather than uniform change. D21A001 presents weaker trend magnitudes overall, yet decreasing tendencies are still evident in late spring, early summer, and autumn. Across methods, MK and SRC consistently support the trend directions, while ITA/ITAS better highlight magnitude and seasonal contrast. Overall, the findings provide robust evidence of long-term streamflow change and hydrological pressure in the upper Euphrates basin. They can support adaptive and transboundary water management planning, while attribution to specific climatic or anthropogenic drivers requires direct analysis of the relevant forcing variables.
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ArticleYalnız künye2026/r/ebyu/10607
ZnBi2O4/Ag2WO4 nanocomposite for efficient photocatalytic removal of dye and pharmaceutical pollutants
Yavuz, Sevtap Caglar; Emul, Zeynep; Icer, Fatih; Yavuz, Emre; Ozdokur, Volkan Kemal; Cirak, Burcu Bozkurt
This paper targets to synthesize a nano-composite with high photocatalytic performance and research the possibility of utilizing it for dye and drug removal from wastewater. Herein, ZnBi2O4/Ag2WO4 nanocomposite photocatalyst was successfully synthesized via two step hydrothermal/precipitation method. The photocatalytic activity of the synthesized nanocomposite was evaluated by its effect on the degradation of methylene blue dye and sulfamerazine drug under UV light. The results revealed that the ZnBi2O4/Ag2WO4 nanocomposite achieved a significantly higher photocatalytic efficiency (96% degradation of methylene blue in 1 h) compared to ZnBi2O4 (41%) and Ag2WO4 (76%) within only 60 min. The exceptional photocatalytic degradation efficiency of the ZnBi2O4/Ag2WO4 nanocomposite against methylene blue and sulfamerazine can be attributed to the effects of reduced charge carrier recombination resulting from the synergistic effect of surface adsorption-photocatalytic degradation interaction of ZnBi2O4 and Ag2WO4.
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ArticleAçık Erişim2026/r/ebyu/10439
Robust Monthly Lake Water Level Forecasting Using an Ensemble of Singular Spectrum Analysis Based Deep Learning and Categorical Boosting Models
Katipoğlu, Okan Mert; Kartal, Veysi; Tuğrul, Türker; Yavuz, Veysel Süleyman; Ariman, Sema
This study developed an innovative hybrid modelling approach to predict one-month-ahead water levels (LWL (T + 1)) for two major lakes in Türkiye (Eber and Eğirdir), aiming to model complex dynamics of hydrological systems and optimize sustainable water resources management. In the proposed methodological framework, Singular Spectrum Analysis (SSA) was employed to decompose hydrometeorological time series into sub-components, while Mutual Information (MI) and Partial Autocorrelation Function (PACF) techniques were utilized to determine optimal input variables. The hybrid model architecture, incorporating Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and CatBoost algorithms, was optimized using 70% of the data for training and 30% for testing, and its performance was evaluated through deterministic and probabilistic metrics. The analyses revealed that SSA-based data preprocessing significantly improved the performance of prediction models, and depending on lake characteristics, CNN-based models demonstrated superior performance for Lake Eber, while DNN architecture excelled for Lake Eğirdir. The developed hybrid approach presents an original methodology that can be integrated into decision support systems for water resources management, contributing to the development of sustainable water management strategies and adaptability to similar hydrological systems.
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ArticleAçık Erişim2026/r/ebyu/10350
Contrastive self-supervised learning for streamflow forecasting: a multi-station comparison with supervised models
Dalkilic, H. Yıldırım; Kilinc, H. Cagan; Yurtsever, Adem; Katipoğlu, O. Mert; Yeşilyurt, S. Nur
Daily streamflow forecasting is complicated by the fact that the predictive value of recent observations shifts considerably depending on the hydrological character of the catchment. This study develops a regime-aware, window-adaptive framework to compare conventional supervised learning algorithms — XGBoost, Random Forest, LSTM, and GRU — against four contrastive self-supervised (SSL) approaches: SimCLR, TS2Vec, CPC, and TempContrast. Experiments were conducted at four gauging stations in Türkiye, each representing a distinct hydroclimatic regime. The SSL stage employs a shared LSTM encoder pretrained with hydrology-informed augmentations and is evaluated across window lengths of 7, 14, 30, and 60 days, allowing a direct assessment of how context length interacts with catchment memory. A bias-regularised loss function is applied during fine-tuning to reduce systematic prediction errors — an aspect that has received limited attention in prior SSL-based hydrological studies. Model performance was quantified using R2, NSE, KGE, Willmott’s d, RMSE, MAE, MBE, and PBIAS. No single model emerged as universally superior. At the strongly seasonal Demirkapı station, supervised models reached Test R2 = 0.96–0.97, while SSL peaked near 0.91. At the low-flow, repetitive Beyşehir station, short-window SSL outperformed all supervised baselines, achieving Test R2 ≈ 0.93–0.94 against 0.89–0.90. At the noisier Akçay and Karadere stations, short-lag supervised models proved more reliable. These results point to a consistent pattern: the effectiveness of any given approach depends heavily on the memory structure and signal regularity of the basin in question, rather than on algorithmic complexity alone.
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ArticleAçık Erişim2026/r/ebyu/10291
Composition of Commercial Gillnet Catches in a Groundwater-Fed Lake: Mesh-Specific Patterns and High Representation of Non-Indigenous Carassius gibelio
Sökmen, Teoman Özgür
Small inland lakes support substantial freshwater biodiversity, but independent fish surveys may be constrained by specialised equipment, trained personnel, seasonal replication, and sustained funding. This study evaluated whether repeated observations of routine commercial gillnet catches could characterise the gillnet-catchable fish subcommunity of Çaltıcak (Taşkısığı) Lake, a shallow, approximately 90-ha groundwater-fed lake in Sakarya Province, Türkiye, and support surveillance of the non-indigenous Carassius gibelio. Fish were obtained over 17 sampling nights from 12 February to 3 June 2026 using five separate single-mesh monofilament gillnets (40, 50, 60, 75, and 80 mm mesh-bar length). Total design-based nominal mounted-area effort was 22,451.7086 m2-night, and catch was expressed as area-standardised numerical and biomass catch per unit effort (NPUE and BPUE). A total of 863 individuals from eight species were examined. Blicca bjoerkna, C. gibelio, and Scardinius erythrophthalmus were the most numerically represented species. C. gibelio constituted 26.42% of the examined catch and had a strongly female-biased catch composition (95.2% female). Smaller-bodied cyprinids occurred mainly in 40–50 mm meshes, whereas Cyprinus carpio and Silurus glanis occurred mainly in 80 and 60 mm meshes, respectively; Esox lucius was an exception, occurring mainly in 40–50 mm meshes. The combined catch corresponded to 0.038438 individuals m−2 night−1 and 0.015831 kg m−2 night−1. Approximate overnight soak duration varied seasonally from 9 to 16 h, but exact set and retrieval clock times were not recorded. Deployment coordinates were also not recorded, and sampling was confined to routine commercial fishing zones during February–June. Accordingly, the findings apply only to the gillnet-catchable fish subcommunity and must not be interpreted as a lake-wide fish-community or stock estimate. Repeated monitoring with georeferenced deployment and exact effort records would strengthen this catch-based baseline.
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ArticleYalnız künye2026/r/ebyu/10182
Fabrication of a high electrochemical performance MTOAC sensor and its use in perchlorate measurement
Kanberoglu, Gulsah Saydan; Kaya, Nurcan; Calimli, Mehmet Harbi; Coldur, Fatih
The detection of perchlorate in some environmental and medical samples has been investigated by various research groups. However, due to its low detection limit and wide operating range, sensitive and selective perchlorate detection presents several challenges and requires costly analytical methods. In this study, methyltrioctylammonium chloride (MTOAC) was successfully used as an ionophore for the determination of perchlorate (ClO4−). The MTOAC ionophore-containing sensor demonstrated superior potentiometric properties, including a fast response time of 15 s, a wide linear operating range of 1.0 × 10− 2 to 7.5 × 10− 8 M, a low detection limit of 6.8 × 10− 8 M, a wide pH operating range of 3.36–9.65, and high repeatability at perchlorate concentrations of 10− 4 to 10− 6 M. Additionally, the MTOAC sensor showed high recovery (99.4-102.3%) in different water samples obtained from tap water, wastewater, spring water and Van Lake water. Compared to the sensors reported in the literature, the MTOAC sensor exhibited a wider linear working range and a lower detection limit. The low production cost, short response time, the absence of pretreatment, ease of use without requiring expertise, and the ability to operate even in turbid solutions make the MTOAC ionophore-containing membrane-based sensor a promising alternative for practical analytical applications.
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CorrectionAçık Erişim2026/r/ebyu/10023
Correction: Estimation of the pan evaporation coefficient in semi-arid climate conditions via machines learning models (Applied Water Science, (2026), 16, 2, (47), 10.1007/s13201-025-02709-6)
Achite, Mohammed; Katipoğlu, Okan Mert; Vishwakarma, Dinesh Kumar; Pandey, Kusum; Salem, Ali; Elbeltagi, Ahmed
In this article, the figures 3 and 5 appeared incorrectly and have now been corrected in the original publication. For completeness and transparency, the old incorrect and correct versions are displayed below: Simple Artificial Neural Networks (ANN) structure Typical architecture of Bagging algorithm The original article has been corrected.
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ArticleYalnız künye2025/r/ebyu/12169
Intercomparison of sediment transport curve and novel deep learning techniques in simulating sediment transport in the Wadi Mina Basin, Algeria
Achite, Mohammed; Katipoğlu, Okan Mert; Elshaboury, Nehal; Tuğrul, Türker; Pandey, Kusum
The accurate estimation of sediment discharge is crucial for the design and operation of engineering structures such as dams, water treatment facilities, and erosion control systems. This study evaluates the performance of various machine learning (ML) and deep learning (DL) models in predicting sediment transport in the Mina Basin, Algeria, at two stations: Oued Abtal and Sidi Abdelkader Djillali. The models include the sediment rating curve, category boosting, convolutional neural network, deep neural network (DNN), gated recurrent unit, and multilayer perceptron. Among these, the DNN model consistently demonstrated superior performance. For Oued Abtal station, the DNN achieved RMSE = 243.72 kg/s, MAE = 102.17 kg/s, NSE = 0.99, and PBIAS = 6.81%. At Sidi Abdelkader Djillali station, it recorded RMSE = 91.27 kg/s, MAE = 46.51 kg/s, NSE = 0.99, and PBIAS = 38.06%. Error analysis revealed that the DNN model offers the most reliable predictions, outperforming both traditional and other ML/DL methods. This study underscores the potential of deep learning models in advancing sediment transport prediction, particularly in semi-arid regions, and highlights their implications for sustainable water resource management.
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ArticleAçık Erişim2025/r/ebyu/12105
The assessment of rice cultivars widely produced in Turkey in terms of arsenic, cadmium, aluminum and copper: effect of soaking methods and household cooking
Özdokur, Kemal Volkan; Özdokur, Seyhan; Güneş, Abdulaziz; Ertugay, Neşe; Ertugay, Mustafa Fatih
Industrialization-driven heavy metal contamination is a growing concern, as residues are increasingly found in foods and tap water due to soil absorption by plants. This study aimed to assess the amount of heavy metals including arsenic, cadmium, aluminum, and copper in widely produced domestic rice cultivars in Türkiye, and to evaluate the effect of pretreatments and domestic cooking on the concentration of heavy metals. Six different rice cultivars were analyzed using inductively coupled plasma-mass spectrometry to determine the levels of heavy metals. Among these six cultivars, the highest heavy metal contamination and also the most consumed was selected to evaluate the effect of soaking processes accompanied by acetic acid (0.25, 0.50, 0.75, and 1%) and salt (1%) at different soaking times (2, 4, 8, and 12 h) and domestic cooking. The concentrations of heavy metals in the primary rice cultivar with the highest contamination were 64.9, 1.3, 2476.7 and 3185.2 μgkg−1 dry weight, respectively. The results showed that soaking in salt (1%) and acetic acid (1%) before cooking was the most effective treatment for removing heavy metals (As = 57.8%, Cd = 100%, Al = 98%, and Cu = 85.7%) at a soaking time of 12 h.
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ArticleYalnız künye2025/r/ebyu/12066
Advanced forecasting of the reconnaissance drought index in the Aras Basin, Turkey using ABC-optimised ANN with VMD and REMD integration
Mert Katipoğlu, Okan; Achite, Mohammed; Elshaboury, Nehal; Abdelkader, Eslam Mohammed
The Aras Basin in Turkey is highly susceptible to meteorological and hydrological droughts because of the reduced rainfall due to climate change and higher water demand for agriculture. In this context, the main objective of this work was to model the Reconnaissance Drought Index (RDI) over 6- and 12-month periods in two meteorological observation stations in the Aras basin. The prediction performance was evaluated by merging the Artificial Neural Network (ANN) model optimised using the Artificial Bee Colony (ABC) algorithm, namely the ABC-ANN model, with the Variational Mode Decomposition (VMD) and Robust Empirical Mode Decomposition (REMD) methodologies. Some statistical metrics and graphical plots were used to assess the model performance. According to graphical and statistical indicators, the hybrid ABC-ANN (R2 = 0.668-0.967) and VMD-ABC-ANN (R2 = 0.758-0.905) models achieved satisfactory results in estimating RDI values at 6- and 12-month time scales. To evaluate the predictive performance applied models, the Friedman test was employed and revealed statistically significant differences among the models (p < 0.0001). The subsequent Nemenyi post-hoc test identified that ABC-ANN performed significantly better than others. Furthermore, when the temporal variations in forecasts were studied, it was established that the ABC-ANN model was superior to other models in estimating extreme drought values.
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ArticleYalnız künye2025/r/ebyu/12015
Optimizing river flow rate predictions: integrating cognitive approaches and meteorological insights
Kartal, Veysi; Karakoyun, Erkan; Akiner, Muhammed Ernur; Katipoğlu, Okan Mert; Kuriqi, Alban
The models used in this study make it possible to make more accurate predictions about river discharge. These results can influence flood protection strategies, water resource management, and hydropower generation. Due to their ability to capture the underlying temporal relationships in the data, time series forecasts have become increasingly popular in recent years. This study examines the critical processes in river forecasting for the Kizilirmak River basin. We begin with a look at data collection and preparation, followed by an overview of time series forecasting models. Finally, we look at the process of model testing and selection. Seven techniques were used to predict streamflow from meteorological data: Artificial Neural Network (ANN), Firefly-based ANN (FFA-ANN), Random Forest (RF), K-Nearest Neighbors (KNN), Generalized Linear Regression (GLR), Support Vector Machines (SVM), Least Squares Boosted Trees (LSBT). The performance of the models was evaluated using the statistical indicators. The LSBT, RF, and ANN models provided the best results for Kayseri, Kırşehir, and Gemerek stations, respectively. The RF, ANN and GLR models provided second best results for these stations, respectively.
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ArticleAçık Erişim2025/r/ebyu/12014
A 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.
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ArticleAçık Erişim2025/r/ebyu/11947
Advanced 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.
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ArticleYalnız künye2025/r/ebyu/11922
Adsorption of reactive red 2 from aqueous solutions by microwave activated carbon from Thuja Plicata cone powder: kinetic, isotherm, thermodynamic studies and characterisation
Ertugay, Neşe; Keleş Güner, Eda
This study investigates, for the first time, the potential of Thuja plicata (TP) cone-derived activated carbon for the removal of the azo dye Reactive Red 2 (RR2) from aqueous solutions. Activated carbon was produced via microwave irradiation to enhance the adsorption capacity of the raw material. The prepared activated carbon (TPAC), synthesised using a chemical activating agent, was characterised by Scanning Electron Microscopy (SEM), Energy Dispersive X-ray Spectroscopy (EDX), and Fourier Transform Infrared Spectroscopy (FTIR). SEM analysis demonstrated that TPAC exhibited increased surface porosity and an irregular morphology after adsorption at the optimal pH. EDX results confirmed that carbon and oxygen were the dominant elements, with elevated contents post-activation. BET analysis indicated a decrease in the specific surface area from 66.64 to 20.35 m2 g−1 after RR2 adsorption, likely due to pore blockage. Batch adsorption experiments were conducted to evaluate the influence of several parameters, including solution pH, adsorbent dosage, initial dye concentration, and temperature. The maximum removal efficiency (54%) was achieved at pH 3 with an adsorbent dosage of 0.01 g. The adsorption capacity increased with rising dye concentration, reaching a maximum of 309.56 mg g−1 at 60 mg L−1. Adsorption isotherms, kinetics, and thermodynamic properties were also evaluated. The Freundlich isotherm model exhibited the best fit to the equilibrium data, indicating multilayer adsorption on a heterogeneous surface. Kinetic data were best described by the pseudo-second-order model, suggesting that chemisorption was the rate-limiting step. Thermodynamic analysis confirmed that the adsorption of RR2 onto TPAC was a spontaneous and exothermic process.
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ArticleYalnız künye2025/r/ebyu/11885
Irrigation water quality classification using boosting and CNN-BiGRU-Attention models: feature importance assessment using Hellinger distance
Zerouali, Bilel; Derdour, Abdessamed; Almalki, Abdulrahman Seraj; Katipoglu, Okan Mert; Arafat, Ahmed A.; Santos, Celso Augusto Guimarães; Bailek, Nadjem; Hussein, Enas E.
This study addresses a gap in irrigation water quality classification by comparing machine learning (ML) algorithms and deep learning (DL) models, with a focus on feature selection. Feature relevance was assessed using Hellinger distance, mutual information, and Chi-square analysis. Classifiers included boosting algorithms (AdaBoost, XGBoost, CatBoost, LightGBM, GradientBoosting, HistGradientBoosting), Extra Trees, and DL models (CNN-BiGRU and CNN-BiGRU-Attention). Performance was evaluated using standard metrics on 166 samples from Algeria’s Naama region. DL models achieved the highest accuracies (0.82–0.84) and AUC scores (0.98–0.981), followed by XGBoost and CatBoost. Feature analysis identified qiCl as most impactful (Hellinger distance = 0.248), and qiNa as most informative (mutual information= 1.0212). These findings underscore the strength of DL approaches and the value of integrated feature selection. The results offer practical insights for optimizing irrigation strategies, supporting risk assessment, and guiding sustainable water resource management.
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