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ArticleAçık Erişim2024/r/ebyu/12117
Mandarin biochar-CO-TETA was utilized for Acid Red 73 dye adsorption from water, and its isotherm and kinetic studies were investigated
Eleryan, Ahmed; Güner, Eda Keleş; Hassaan, Mohamed; El-Nemr, Mohamed A.; Ragab, Safaa; El Nemr, Ahmed
Environmental pollution is a major issue today due to the release of dyestuff waste into the environment through industrial wastewater. There is a need for affordable and effective adsorbents to remove harmful dyes from industrial waste. In this study, Mandarin biochar-CO-TETA (MBCOT) adsorbent was prepared and used to remove Acid Red 73 (AR73) dye from aqueous solutions. The efficiency of dye removal was influenced by various factors such as solution pH, contact time, initial AR73 dye concentration, and MBCOT dosage. All experiments were conducted at 25 ± 2 °C, and the optimal pH was determined to be 1.5. The optimal conditions for dye removal were found to be an AR73 dye concentration of 100 mg/L, an MBCOT dosage of 1.5 g/L, and a contact time of 150 min, resulting in a 98.08% removal rate. Various models such as pseudo-first-order (PFO), pseudo-second-order (PSO), film diffusion (FD), and intraparticle diffusion (IPD) were used to determine the adsorption kinetics of AR73 dye onto MBCOT. The results showed that the PSO model best explains the AR73 dye adsorption. Furthermore, Langmuir and Freundlich's isotherm models were studied to explain the adsorption mechanism using experimental data. The adsorption capacities at equilibrium (qe) in eliminating AR73 dye varied from 92.05 to 32.15, 128.9 to 65.39, 129.25 to 91.69, 123.73 to 111.77, and 130.54 to 125.01 mg/g. The maximum adsorption capacity (Qm) was found to be 140.85 mg/g. In conclusion, this study demonstrates that biochar produced from mandarin peels has the potential to be an effective and promising adsorbent for removing AR73 dye from water.
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ArticleYalnız künye2024/r/ebyu/12114
Applying Data-Driven Modeling for Streamflow Prediction in Semi-Arid Watersheds: A Comparative Evaluation of Machine Learning and Deep Learning Methodologies
Sarıgöl, Metin; Katipoğlu, Okan Mert; Dalkilic, Hüseyin Yildirim
Modeling monthly stream flows most accurately is of vital importance for water resource management, agricultural irrigation and efficient hydroelectric energy production, especially in semi-arid areas. Soft computing approaches have recently taken an important place in estimating streamflow time series. The potential of various data-driven approaches to predict streamflow in challenging climate conditions was evaluated. The study used machine and deep learning algorithms to model average monthly stream flows in two stream gauging stations in semi-arid region of the Konya closed basin where agriculture is at the forefront, accurate and reliable estimation of the stream flows is the basis of the study. For this, the performances of emotional neural network algorithm (EmNN), long-short term memory (LSTM), Elman neural network (ENN), nonlinear autoregressive exogenous model (NARX), recurrent neural network (RNN), group method of data handling (GMDH) were compared. The study’s unique contribution lies in its comprehensive comparison of these diverse algorithms, including newer approaches like EmNN, in the specific context of semi-arid hydrology. Partial autocorrelation analysis was applied to select input combinations, and lagged values exceeding 95% confidence limits were presented to the models as the most essential features. Artificial intelligence (AI) models use lagged stream flows to predict the streamflow time series. Statistical parameters, scatter diagrams and a time series approach are used to compare model performance. The GMDH model produced the following test results for 1604 no station: KGE: 0.656, R2: 0.608, NSE: 0.343, RMSE: 27.021, MAE: 3.834, MAPE: 0.662, MBE: −0.217, BF: 0.972. Similarly, for 1623 no station, the GMDH model yielded the following test results: KGE: 0.770, R2: 0.615, NSE: 0.531, RMSE: 0.006, MAE: 0.047, MAPE: 0.217, MBE: −0.012, BF: 0.956. In addition, the EmNN algorithm was the approach with second prediction accuracy. The findings of the study are important resources for optimizing the selection of AI models for streamflow prediction in semi-regional areas. The study also provides critical information for policymakers and decision-makers in similar climate zones worldwide for water resource management.
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ArticleYalnız künye2024/r/ebyu/12043
An evolutionary hybrid method based on particle swarm optimization algorithm and extreme gradient boosting for short-term streamflow forecasting
Kilinc, Huseyin Cagan; Haznedar, Bulent; Ozkan, Furkan; Katipoğlu, Okan Mert
Streamflow estimation is necessary to develop sustainable water management strategies that balance the needs of various water users while protecting the ecological health of the watershed. This study presents a comprehensive analysis of river flow data in Turkey's Meriç basin using long-term daily data spanning the years 2001–2011. Within the scope of forecasting of daily river flows, 80% of dataset was used for training stage and the 20% of dataset was used for testing. The performance of four different models, including Extreme Gradient Boosting (XGBoost) benchmark model, Linear Regression, Long Short-Term Memory Network (LSTM), and proposed Particle Swarm Optimization (PSO)—XGBoost hybrid model, was evaluated using several statistical evaluation criteria such as RMSE, MAPE, MAE, SD, NSE, KGE, and model success was assessed based on determination coefficient (R2) values. The forecasting results of the models were evaluated with statistical metrics. The proposed PSO-XGBoost model outperformed all models with R2 values of 0.7460 at the Babaeski FMS station, 0.9582 at the Meriç FMS station, and 0.8116 at the Hayrabolu FMS station, respectively. It has been found that when the parameters of the XGBoost model are optimized with the meta-heuristic PSO algorithm, the daily flow prediction accuracy is significantly increased in the Meriç River basin, which has a pluviozonal River regime. The results demonstrate that the proposed hybrid model produces precise and reliable estimations for daily river flow analysis in the Meriç basin. The findings derived from the proposed model demonstrate its promising potential for water resource management and decision-making in the region.
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ArticleAçık Erişim2024/r/ebyu/12030
Advanced long-term actual evapotranspiration estimation in humid climates for 1958–2021 based on machine learning models enhanced by the RReliefF algorithm
Elbeltagi, Ahmed; Heddam, Salim; Katipoğlu, Okan Mert; Alsumaiei, Abdullah A.; Al-Mukhtar, Mustafa
Study region: Chengdu, Wuhan, Chongqing, and Kunming regions in China. Study focus: Accurate estimation of crop water use or actual evapotranspiration (AET) remains a key obstacle in the effective design of irrigation schedules, plans, and design. This is due to the non-linear nature of this phenomenon. To address this issue and guarantee more accurate ET predictions, this study attempts the following: i) to assess the performance of five machine learning (ML) models optimized by the RReliefF algorithm in estimating actual ET values for each month in four Chinese provinces under various agroclimatic conditions; and ii) to select the optimal model based on statistical metrics while minimizing discrepancies between the estimated and actual ET values. AET was estimated using support vector machine (SVM), ensemble bagged and boosted trees, robust linear regression (RLR), and Matern 5/2 Gaussian process regression (M-GPR) models. New hydrological insights for the region: The M-GPR model outperformed the other models and generated the best values for all statistical measures for training and testing stages: R2 (0.979, 0.982), RMSE (5.56, 5.09), MAE (3.29,3.16). In comparison, the RLR model exhibited the lowest training and testing performances metrics. The results of this study demonstrate the capacity of the M-GPR model to accurately predict long-term AET values. This model is best suited for further research on AET prediction at the stations under investigation, which could improve irrigation and boost agricultural productivity.
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ArticleYalnız künye2024/r/ebyu/11998
Understanding run theory for evaluating hydrologic drought in the Wadi Mina Basin (Algeria): A historical analysis
Achite, Mohammed; Katipoğlu, Okan Mert; Jehanzaib, Muhammad; Kartal, Veysi; Mansour, Hamidi
Drought is a natural disaster characterised as precipitation much lower than the precipitation reported in actual periods. Many studies characterized drought as meteorological, hydrological, agricultural, or socioeconomic. When there is a long-term shortage of precipitation, deficits in surface and ground waters occur. In this study, a hydrological drought analysis has been performed for Wadi Mina Basin (4900 km2), which is the biggest sub-basin in Cheliff Basin, using the Streamflow Drought Index (SDI) for the time scales of 3, 6, 9, and 12-month. Monthly mean streamflow records for 05 stations are obtained from the National Water Resources Agency. The obtained SDI values were subjected to Run analysis and drought duration and severity values were calculated. According to the analysis, it has been observed that the maximum (duration: 70 months, severity: 92.78) and average (duration: 31 months, severity: 31.28) droughts occurred at the Sidi AEK Djillali station on a 12-month time scale. The average drought severity was 6.34, with a maximum value of 56.71 on a monthly time scale. However, on a 12-month time scale, the average drought severity increased to 31.28, with a maximum value of 92.78. Therefore, it can be said that the drought severity has increased with the increase in the time scale. When the temporal changes of drought indices are evaluated, it is noteworthy that extraordinary droughts prevailed in the basin in 2000 and 2007. When the scatter diagrams of drought characteristics were examined, it was seen that there was a significant linear relationship between drought duration and severity. In addition, the highest correlation was observed at the 9-month time scale at Ain Hamara (R:0.964) and Oued Abtal (R:0.904) stations. In contrast, the highest correlation was observed at the 12-month time scale at Sidi AEK Djillali (R:0.980) and Takhmaret (R:0.969) stations.
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ArticleAçık Erişim2024/r/ebyu/11994
Sediment load forecasting from a biomimetic optimization perspective: Firefly and Artificial Bee Colony algorithms empowered neural network modeling in Çoruh River
Katipoğlu, Okan Mert; Kartal, Veysi; Pande, Chaitanya Baliram
The service life of downstream dams, river hydraulics, waterworks construction, and reservoir management is significantly affected by the amount of sediment load (SL). This study combined models such as the artificial neural network (ANN) algorithm with the Firefly algorithm (FA) and Artificial Bee Colony (ABC) optimization techniques for the estimation of monthly SL values in the Çoruh River in Northeastern Turkey. The estimation of SL values was achieved using inputs of previous SL and streamflow values provided to the models. Various statistical metrics were used to evaluate the accuracy of the established hybrid and stand-alone models. The hybrid model is a novel approach for estimating sediment load based on various input variables. The results of the analysis determined that the ABC-ANN hybrid approach outperformed others in SL estimation. In this study, two combinations, M1 and M2, with different input variables, were used to assess the model's accuracy, and the best-performing model for monthly SL estimation was identified. Two scenarios, Q(t) and Q(t − 1), were coupled with the ABC-ANN algorithm, resulting in a highly effective hybrid approach with the best accuracy results (R2 = 0.90, RMSE = 1406.730, MAE = 769.545, MAPE = 5.861, MBE = − 251.090, Bias Factor = − 4.457, and KGE = 0.737) compared to other models. Furthermore, the utilization of FA and ABC optimization techniques facilitated the optimization of the ANN model parameters. The significant results demonstrated that the optimization and hybrid techniques provided the most effective outcomes in forecasting SL for both combination scenarios. As a result, the prediction outputs achieved higher accuracy than those of a stand-alone ANN model. The findings of this study can provide essential resources to various managers and policymakers for the management of water resources.
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ArticleAçık Erişim2024/r/ebyu/11976
Synthesis and characterization of iron oxide-commercial activated carbon nanocomposite for removal of hexavalent chromium (Cr6+) ions and Mordant Violet 40 (MV40) dye
Mohamed, Soha Mahrous Ismail; Yılmaz, Murat; Güner, Eda Keleş; El Nemr, Ahmed
Iron Oxide-commercial activated carbon nanocomposite (CAC-IO) was prepared from commercial activated carbon (CAC) by the co-precipitation method, and the resulting nanocomposite was used as an adsorbent to remove hexavalent chromium (Cr6+) ions and Mordant Violet 40 (MV40) dye from wastewater. The produced materials (CAC, CAC after oxidation, and CAC-IO) were comparatively characterized using FTIR, BET, SEM, EDX TEM, VSM, and XRD techniques. The adsorption mechanism of Cr6+ ions and MV40 dye on CAC-IO was examined using Langmuir and Freundlich isotherm models. Different models were applied to know the adsorption mechanism and it was obtained that Pseudo-second order fits the experimental data better. This means that the adsorption of the adsorbate on the nanocomposite was chemisorption. The maximum removal percent of Cr6+ ions by CAC-IO nanocomposite was 98.6% determined as 2 g L–1 adsorbent concentration, 100 mg L–1 initial pollutant concentration, solution pH = 1.6, the contact time was 3 h and the temperature was room temperature. The maximum removal percentage of Mordant Violet 40 dye (C.I. 14,745) from its solutions by CAC-IO nanocomposite was 99.92% in 100 mg L–1 of initial dye concentrations, 1.0 g L–1 of adsorbent concentration, solution pH = 2.07, the contact time was 3 h. The MV40 dye adsorption on CAC-IO was the most fitted to the Freundlich isotherm model. The maximum adsorption capacity was calculated according to the Langmuir model as 833.3 mg g–1 at 2 g L–1 of adsorbent concentration and 400 mg L–1 of initial MV40 dye concentration. The Cr6+ ions adsorption on CAC-IO was more fitted to the Freundlich model with Qmax, equal to 312.50 mg g–1 at 1 g L–1 adsorbent concentration and 400 mg L–1 of Cr6+ ions initial concentrations.
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ArticleYalnız künye2024/r/ebyu/11944
Removal of manganese (Mn2+) from water samples using a biocomposite sorbent
Çalışır, Aykut; Çağlar Yavuz, Sevtap; Yavuz, Emre; Arar, Özgür; Arda, Müşerref
Herein, tannic acid–tethered cellulose was developed as an efficient and selective sorbent for Mn2⁺ removal from aqueous solutions. The modified cellulose was characterized through scanning electron microscopy, infrared spectroscopy, and elemental analyses. Sorption performance was evaluated using various parameters, including pH, initial Mn2⁺ concentration, contact time, and the presence of interfering ions. Results indicated that Mn2⁺ removal was highly pH-dependent, with removal efficiency increasing from 8% at pH 2 to99% at pH 9, achieving a remarkable 99% removal rate within only 30 min, highlighting the rapidity of the cellulose sorption kinetics. The results of isotherm studies confirmed that the sorption conformed to the Langmuir model with a monolayer sorption mechanism. Using a sorbent dose of 0.05 g, 99% of Mn2⁺ could be effectively eliminated from water, achieving a maximum sorption capacity of 32.2 mg/g dry-sorbent. The modified cellulose could be effectively regenerated using 0.5-M HCl or 0.1-M H₂SO₄, with no considerable deterioration in sorption performance after three sorption–regeneration cycles. The presence of Na⁺ and K⁺ had minimal impact on Mn2⁺ removal, whereas the presence of Ca2⁺ and Mg2⁺ at low concentrations facilitated moderate-level Mn2⁺ removal.
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ArticleYalnız künye2024/r/ebyu/11929
Bee-inspired insights: Unleashing the potential of artificial bee colony optimized hybrid neural networks for enhanced groundwater level time series prediction
Katipoğlu, Okan Mert; Mohammadi, Babak; Keblouti, Mehdi
Analysis of the change in groundwater used as a drinking and irrigation water source is of critical importance in terms of monitoring aquifers, planning water resources, energy production, combating climate change, and agricultural production. Therefore, it is necessary to model groundwater level (GWL) fluctuations to monitor and predict groundwater storage. Artificial intelligence-based models in water resource management have become prevalent due to their proven success in hydrological studies. This study proposed a hybrid model that combines the artificial neural network (ANN) and the artificial bee colony optimization (ABC) algorithm, along with the ensemble empirical mode decomposition (EEMD) and the local mean decomposition (LMD) techniques, to model groundwater levels in Erzurum province, Türkiye. GWL estimation results were evaluated with mean square error (MSE), coefficient of determination (R2), and residual sum of squares (RSS) and visually with violin, scatter, and time series plot. The study results indicated that the EEMD-ABC-ANN hybrid model was superior to other models in estimating GWL, with R2 values ranging from 0.91 to 0.99 and MSE values ranging from 0.004 to 0.07. It has also been revealed that promising GWL predictions can be made with previous GWL data.
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ArticleYalnız künye2024/r/ebyu/11842
Modeling of irrigation water quality parameter (sodium adsorption ratio) using hybrid swarm intelligence-based neural networks in a semi-arid environment at SMBA dam, Algeria
Achite, Mohammed; Katipoğlu, Okan Mert; Elshaboury, Nehal; Kartal, Veysi; Aktürk, Gaye; Ertugay, Neşe
Sodium adsorption rate (SAR), which significantly affects soil and plant health, is determined according to the concentration of sodium ions, calcium, and magnesium in irrigation water. Accurate estimation of SAR values is vital for agricultural production and irrigation. In this study, hybrid swarm intelligence-based neural networks are used to model sodium adsorption ratio in irrigation water quality parameters in a semi-arid environment at Sidi M’Hamed Ben Aouda (SMBA) dam, Algeria. For this, the nature-inspired optimization techniques of particle swarm optimization (PSO), genetic algorithm (GA), Jaya algorithm (JA), artificial bee colony (ABC), and firefly algorithm (FFA) and the signal processing technique of variational mode decomposition (VMD) have been combined with artificial neural networks (ANN). Correlation matrices were used to select the data entry structure in the established models. Water quality parameters with a statistically significant and medium to high relationship with SAR values were presented as input to the model. The overall performance was measured using various statistical metrics, scatter diagrams, Taylor diagrams, correlograms, boxplots, and line plots. In addition, the effect of input parameters on model estimation was evaluated according to Sobol sensitivity analysis. As a result, the GA-ANN algorithm demonstrated superior performance (MSE = 0.073, MAE = 0.193, MAPE = 0.048, MBE=-0.16, R2 = 0.934, WI = 0.968, and KGE = 0.866) based on the statistical indicators, indicating better results compared to other models. The second-best model, ABC-ANN (MSE = 0.084, MAE = 0.233, MAPE = 0.066, MBE=-0.135, R2 = 0.897, WI = 0.965, and KGE = 0.920) was also selected. The weakest prediction outputs were obtained from the VMD-ANN model. The accurate and reliable estimation of SAR in irrigation water has the potential to facilitate improvements in agricultural irrigation management and agricultural production efficiency for farmers, agricultural practitioners, and policymakers.
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ArticleYalnız künye2024/r/ebyu/11765
Enhancing groundwater potential zone mapping with a hybrid analytical method: The case of semiarid basin
Zerouali, Bilel; Bailek, Nadjem; Islam, Abu Reza Md Towfiqul; Katipoğlu, Okan Mert; Ayek, Almustafa Abd Elkader; Santos, Celso Augusto Guimarães; Rajput, Jitendra; Wong, Yong Jie; Abda, Zaki; Chettih, Mohamed
Groundwater resource management is a critical component of sustainable water use, necessitating accurate and nuanced mapping of groundwater potential zones. This study analyzed the groundwater potential of Algeria's 43,750 km2 Chelif Basin (more than 17% of the area of Northern Algeria) using a combination of both subjective and objective mapping techniques. The adopted approaches encompass the benchmark analytical Fuzzy Analytic Hierarchy Process (Fuzzy-AHP) and the DEcision MAking Trial and Evaluation Laboratory (DEMATEL) methodologies to quantify interdependencies of criteria related to groundwater potential. The analysis focused on ten criteria related to groundwater potential, including core moisture availability and key hydrological factors like distance to river, topographic wetness index, and hydrological soils. Fuzzy-AHP achieved slightly higher groundwater prospecting accuracy (AUC = 0.730) than classic AHP (0.716), with benchmarking against 15 related studies indicating robust performance. Instead of the most commonly used criteria in groundwater literature such as lineament, stream order, recharge rate, and drainage density, this study employed alternative factors to challenge and validate the efficacy of the proposed methodology. The decision to omit certain criteria facilitated a more focused and manageable analysis, yet still delivered a robust evaluation of groundwater potential in the studied area. Moreover, this approach underscores the adaptability of the proposed methodology to accommodate varying sets of criteria, tailored according to the availability of data and specific research objectives. Additionally, the DEMATEL evaluation reveals new insights into subtle prioritization divergences, specifically between domain specialist opinions and analytical assessments of the criteria. The integration of fuzzy logic and causal relationship mapping through DEMATEL provides a comprehensive and robust foundation for groundwater potential modeling.
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ArticleAçık Erişim2024/r/ebyu/11733
Fine-tuning inflow prediction models: integrating optimization algorithms and TRMM data for enhanced accuracy
Ali, Enas; Zerouali, Bilel; Tariq, Aqil; Katipog, Okan Mert; Bailek, Nadjem; Santos, Celso Augusto Guimarães; Ghoneim, Sherif S.M.; Towfiqul Islam, Abu Reza Md.
This research explores machine learning algorithms for reservoir inflow prediction, including long short-term memory (LSTM), random forest (RF), and metaheuristic-optimized models. The impact of feature engineering techniques such as discrete wavelet transform (DWT) and XGBoost feature selection is investigated. LSTM shows promise, with LSTM-XGBoost exhibiting strong generalization from 179.81 m3/s RMSE (root mean square error) in training to 49.42 m3/s in testing. The RF-XGBoost and models incorporating DWT, like LSTM-DWT and RF-DWT, also perform well, underscoring the significance of feature engineering. Comparisons illustrate enhancements with DWT: LSTM and RF reduce training and testing RMSE substantially when using DWT. Metaheuristic models like MLP-ABC and LSSVR-PSO benefit from DWT as well, with the LSSVR-PSO-DWT model demonstrating excellent predictive accuracy, showing 133.97 m3/s RMSE in training and 47.08 m3/s RMSE in testing. This model synergistically combines LSSVR, PSO, and DWT, emerging as the top performers by effectively capturing intricate reservoir inflow patterns.
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ArticleYalnız künye2024/r/ebyu/11698
A novel metaheuristic optimization and soft computing techniques for improved hydrological drought forecasting
Katipoğlu, Okan Mert; Ertugay, Neşe; Elshaboury, Nehal; Aktürk, Gaye; Kartal, Veysi; Pande, Chaitanya Baliram
Drought is one of the costliest natural disasters worldwide and weakens countries economically by causing negative impacts on hydropower and agricultural production. Therefore, it is necessary to create drought risk management plans by monitoring and predicting droughts. Various drought indicators have been developed to monitor droughts. This study aims to forecast Streamflow Drought Index (SDI) values with various novel metaheuristic optimization-based Artificial Neural Network (ANN) and deep learning models to predict 1-month lead-time hydrological droughts on 1, 3, and 12-month time scales in the Konya closed basin, one of the driest basins in Turkey. To achieve this goal, the ANN model was integrated with the Firefly Algorithm (FFA), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) techniques and compared against long short-term memory (LSTM) networks. While establishing the SDI prediction model, lag values exceeding the 95% confidence intervals in the partial autocorrelation function graphs were used. Model performance was evaluated according to scatter matrix, radar, time series, bee swarm graphs, and statistical performance metrics. As a result of the analysis, the PSO-ANN hybrid model with (R2:0.468–0.931) at station 1611 and the FFA-ANN hybrid model with (R2:0.443–0.916) at station 1612 generally have the highest accuracy.
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ArticleAçık Erişim2023/r/ebyu/20280
Karasu Nehri (Erzincan,Türkiye)’nin Trofik Durumunun Biyoçeşitlilik İndeksleriyle Değerlendirilmesi
Tanır, Özge Zencir; Fakioglu, Ozden; Akyüz, Zeliha; Topal, Muhammet Furkan
Bu çalışma, Karasu Nehri'ndeki fitoplankton kompozisyonunun ve biyolojik çeşitliliğinin mevsimsel ve zamansal değişimlerine odaklanmıştır. Fitoplankton ve su örnekleri Ocak 2019-Temmuz 2019 döneminde iki ayda bir 0.5-8 m derinlikten beş istasyondan toplanmıştır. Dört çeşitlilik indeksi (Shannon-Wiener, Simpson, Margalef ve Menhinick) incelenmiştir. Endeksler arasındaki ilişkiyi yorumlamak için MINITAB 15 yazılımı kullanılmıştır. Ortalama su sıcaklığı, çözünmüş oksijen, pH ve iletkenlik değerleri sırasıyla 19.43 °C, 7.9 mg/L, 8.3 ve 465 mS/cm olarak ölçülmüştür. Bu çalışmada toplam 123 fitoplankton türü tespit edilmiştir. Bu türlerden Bacillariophyta grubuna ait 106 tür, Chlorophyta grubuna ait 10 tür, Cyanobacteri grubuna ait 6 tür ve Euglenophyta grubuna ait 1 tür teşhis edilmiştir. Çalışma boyunca Shannon-Wiener, Simpson Margalef ve Menhinick çeşitlilik indeksleri sırasıyla 1.83 H ́, 11.53, 5.12 ve 0.83 olarak hesaplanmıştır. Bu çalışma sonucunda fitoplankton çeşitlilik indeks değerleri ile Karasu Nehri’nin su kalitesine bağlı ekolojik durum değerlendirilmesi bir biri ile uyumlu olmadığı tespit edilmiştir. Ancak farklı şehirler için referans koşulların geliştrilmesi endekslerin kullanılabilirliğini artırabilir. Bu nedenle bu çalışmaların devam etmesini ve yaygınlaşmasını öneriyoruz.
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ArticleAçık Erişim2023/r/ebyu/20271
Trends of changing land use dynamics in the Terkos Lake basin between 1980 and 2023 and their impact on natural ecosystems
Şenol, Celal; Taş, Mehmet Akif
Anthropogenic processes are one of the main causes of environmental change in this century. As an indicator of man’s struggle for dominance over nature, the environment has been directly affected. These changes, which we encounter in every region of the place where we live, on a local and global scale, and which are due to man, cause the natural cycle of ecosystems to be disrupted. As in Türkiye in general, land use changes in the study area under the influence of direct and indirect factors in some regions are very rapid. No matter how much effort are made to control them, factors that have a direct impact on the environment, such as population growth, agriculture, industrial facilities, and the design of transport infrastructure, are the most important causes of change. In this direction, this study aims to identify the trends of land use changes around Lake Terkos and to reveal the deficiencies and fragilities of the relationship networks. It is also an indication of possible changes in the ecological status of the lake and its surroundings in relation to this situation. Terkos Lake’s prolonged use as a crucial drinking water source for Istanbul makes it a valuable study site to investigate its ecological state and management practices. Nevertheless, it has recently been under pressure due to transportation (airport, Northern Marmara Motorway, Istanbul Canal and other transport networks), urban sprawl, and industry. In this context, first of all, the dynamics of land use and the rapidly changing areas (hot zones) between 1980 and 2023 have been identified and the extent of the changes over time has been shown. Possible future land use changes were analysed in the light of this data. As a result of the study, the speed of transformation of green areas was revealed. In this direction, especially in the field of the aquatic ecosystem, vulnerable areas were identified, the degree of being affected by future change was revealed and the trend in the dimensions of use and change was analysed.
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ArticleAçık Erişim2023/r/ebyu/19905
One City One Added Value: Almus Dam
Çiftçi, Yasemin
Cities defined as living units; They are important formations that can transfer to the society many features of this geographical area, which they surround as much as they own their geographical limitations, The added values that cities have created on the social area cannot be denied. One of the most important added values created by cities is the added value seen at the socioeconomic level. In this context, Tokat City was chosen as the city within the scope of the paper. The reason why Tokat was chosen is to present the socioeconomic added value created by the Almus Dam which was one of the important achievements of the republican period and which was established in the Almus District, at the city level. In order to reveal this added value, the original documents obtained from the Republic Archives of the Prime Ministry and the newspaper news on the online platform were studiously examined with the content analysis method, and the valuable outputs obtained by compiling among themselves were shared with the reader. Paper is at a level that can fill an important gap in the literature, both due to the fact that the studies on the city history of the Republican period are less in quantity and because of the limited number of studies directly related to the Almus Dam. According to the results obtained, after the Almus Dam enter into service; ıt has created an undeniable socio-economic added value in agriculture, fishery industry, employment and energy production. In order to increase the contribution of the paper to the literature, it is the most usual suggestion to reach the socio-economic numerical outputs related to the Almus Dam and to evaluate these outputs on the level of district and city
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ArticleAçık Erişim2023/r/ebyu/19194
Microwave-Assisted Fabrication of Pd, Co and Ni Nanoparticles Modified-SiO2; as Catalysts in the Reduction Reaction of Organic Pollutants
Dayan, Serkan; Yavuz, Sevtap Çaglar; Yavuz, Emre
Nanomaterials have been used in catalytic degradation of organic pollutants also act as catalysts in for many years. Due to excellent catalytic performances of metal-based nanoparticles, these materials have been used extensively in various hybrid catalyst synthesis. The main subject of this study, heterogeneous catalysis is a low cost and multi-purpose process for many pollutants. Catalytic degradation of organic pollutants such as; 2-nitrophenol, quinolin yellow and rhodamine B was investigated by using Ni, Co, Pd nanoparticles modified SiO2 based nanomaterials. The co-doping effect on the prepared nanomaterials has been investigated with different characterization methods in terms of structural and morphological features: scanning electron microscopy, UV/vis absorption spectroscopy, energy-dispersive X-ray spectroscopy and foruier-transform infrared spectroscopy. The highest catalytic reduction efficiencies (97.6% and 97.5%) for 2-nitrophenol and rhodamine B was obtained by Pd-PEG-AP@SiO2 respectively. The synthesized Co-PEG-AP@SiO2 illustrated higher catalytic reduction efficiency for quinolin yellow (70.1%) at the end of 60s. The prepared M-PEG-AP@SiO2 nanomaterial (M: Pd,Co,Ni) can be able to utilized degradation of organic contaminants effectively.
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Review ArticleAçık Erişim2023/r/ebyu/19125
Katı atık yönetimi: balıkesir büyükşehir belediyesi örneği
Ekinci, Elif; Bilgin, Hande
Atık yönetimleri, tüm ülkelerin çevre politikaları içerisinde önemli bir yer edinmiştir. Katı atık yönetimleri ve çevre koruma faaliyetleri ülkelerin ekonomik, siyasi ve sosyal durumlarına göre farklılık arz etmektedir. Dünya hızla kentleşmekte ve artan nüfus; daha fazla üretim yapılması daha fazla doğal kaynağın kullanılması anlamına gelmektedir. Artan tüketim sonucu katı atık oranı da çoğalmaktadır. Belediyeler, yerel halkını, katı atıklardan ve çevre sorunlarından korumakla yükümlüdür.Çalışmanın ilk bölümünde kavramsal çerçeve başlığı altında katı atık yönetimi ve çeşitlerinden, entegre katı atık yönetimi ve sürdürülebilir katı atık yönetiminden bahsedilecektir. İkinci bölümde Türkiye’ de katı atık yönetiminin tarihsel çerçevesinden bahsedilerek, katı atıklarla ilgili kanunlara ve kalkınma planlarına değinilecektir. Üçüncü bölümde Balıkesir Büyükşehir Belediyesi katı atık çalışmaları incelenecektir. Balıkesir Büyükşehir Belediyesi’nin çalışma içerisinde incelenmesinin nedeni özellikle büyükşehir belediyesine geçiş noktasında katı atık hizmetleri ile ilgili yapmış olduğu başarılı projelerini inceleyebilmektir. Büyükşehir olmadan önce ve Büyükşehir Belediyesine geçmesinden itibaren katı atık yönetimi ile ilgili nelerin değiştiği, hangi alanlarda ilerleme katettiği ve nerelerde halen sorun yaşandığını tespit edebilmek açısından karşılaştırma yapılarak, literatüre katkıda bulunulacaktır
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ArticleAçık Erişim2023/r/ebyu/17860
Evaluation of the success of the hybrid wavelet-based ANFIS approach in the estimation of monthly stream flows of the Bitlis River, Turkey
Katipoglu, Okan Mert
This study aimed to predict monthly flows using an adaptive neuro-fuzz y inference system (ANFIS) and wavelet-ANFIS (W-ANFIS) and to deter-mine the effect of wavelet transformation on the success of the machine learning model. For this purpose, the model inputs are divided into three subcomponents with Daubechies 10 mother wavelets. Subcomponents with the highest correlation were chosen as inputs. The most suitable models were selected by dividing the inputs into 3-7 sub-sets, using 11 different lagged input combinations, and testing various membership functions. In establishing the ANFIS model, 75% of the data were used for training and 25% for testing. The performance of ANFIS models was evaluated with root mean square error, Pearson correlation coefficient, determination coefficients, and Taylor diagram. A model with two sub-sets, a hybrid learning algorithm, a Gbellmf membership function, and 400 iterations was selected as the most suit-able. It was concluded that the W-ANFIS model used with the wavelet transform method increased the success of the established ANFIS model. Moreover, it was suggested that the W-ANFIS hybrid machine learning model established in the study can be used effectively in simi-lar climatic regions fed by snowmelt and dominated by a semi-arid climate.
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ArticleAçık Erişim2023/r/ebyu/13385
Combining Machine Learning Algorithms with Empirical Mode Decomposition and Discrete Wavelet Transform for Monthly Peak Discharge Prediction
Katipoğlu, Okan Mert; Sarıgöl, Metin
Accurate and reliable peak discharge prediction is of great importance in water resource management and flood control studies. The aim of this study was to predict monthly peak discharge by combining various signal decomposition processes and machine learning models. For this purpose, monthly peak discharge data were decomposed into subsignals utilizing Daubechies 3, Coiflet 5, discrete Meyer main wavelets, and empirical mode decomposition methods, which are the ones most commonly used in hydrological studies. The separated signals were subjected to correlation analysis, and the sum of the highly correlated signals was presented as input to the machine learning models. Support vector machines, regression trees, ensemble trees, and adaptive neuro-fuzzy inference system models were used for peak discharge forecasting. The performance of the established models was evaluated with the help of statistical indicators such as mean absolute error, root mean squared error, and determination coefficient, and graphically through Taylor diagrams. At the end of the study, the most effective results were obtained with the hybrid model established by the Daubechies 3 wavelet-4 decomposition levels and combined with the coarse Gaussian support vector machine.
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