Sürdürülebilir Kalkınma Amaçları
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- ArticleAçık Erişim2024Comparative analysis of data-driven models and signal processing techniques in the monthly maximum daily precipitation prediction of El Kerma station Northeast of Algeria
Katipoğlu, Okan Mert; Keblouti, Mehdi
The availability of a climate database is an essential requirement for modeling and mapping hydrological and environmental processes. Regardless of the nature and structure of these models, most of them require a complete and reliable dataset on a spatiotemporal basis. Unfortunately, the measurement of hydrological variables (precipitation, flow, etc.) can be affected by systematic errors, gaps and random data. The watershed of Seybouse located in the North-East of Algeria, has a network that has gaps in the monthly data with an average error percentage of 20.46% over the period of 1970–2008 on all the stations in operation. The current study combines support vector regression, artificial neural networks, boosted tree, bagged tree, gaussian processes regression and linear regression techniques with empirical mode decomposition and discrete wavelet transform techniques to fill the missing precipitation gaps. This study aims to determine which kernel function, regression type, tree and network structure and data decomposition technique will produce the best outputs in predicting missing rainfall. For this purpose, it aims to comprehensively evaluate various soft computing and signal processing models and use strengthened hybrid approaches by combining them. While modeling, 70% of the data was used for training and the rest for testing. Mean absolute error (MAE), root mean square error (RMSE), determination coefficient (R2), Taylor diagram and Violin plots were used to find the best model. As a result of the analysis, it was concluded that the SVM model produced superior outputs compared to other machine learning models. The single LSVM model had an R2 value of 0.78, RMSE of 8.18, and MAE of 5.90. The W-LSVM model had an R2 value of 0.78, RMSE of 8.10, and MAE of 5.90. The EMD-CGSVM model had an R2 value of 0.69, RMSE of 10.43, and MAE of 7.10. These results indicate that the W-LSVM models provided the most accurate rainfall forecast outputs. The results showed that the wavelet-based linear support vector regression model in precipitation prediction is the most successful artificial intelligence model. Finally, using the W-LSVM model to predict rainfall in the study area can significantly improve water resources management and flood strategies and help optimize water use and structures.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2024Suspended sediment load prediction in river systems via shuffled frog-leaping algorithm and neural network
Katipoğlu, Okan Mert; Aktürk, Gaye; Kılınç, Hüseyin Çağan; Terzioğlu, Zeynep Özge; Keblouti, Mehdi
Suspended sediment load estimation is vital for the development of river initiatives, water resources management, the ecological health of rivers, determination of the economic life of dams and the quality of water resources. In this study, the potential of Feed Forward Neural Network (FFNN), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Shuffled Frog Leaping Algorithm (SFLA) models was evaluated for suspended sediment load (SSL) estimation in Yeşilırmak River. The heat map of Pearson correlation values of meteorological and hydrological parameters in 1973–2021, which significantly impacted SSL estimation, was examined to estimate SSL values. As a result of the analysis it was developed a prediction model with three different combinations of precipitation, stream flow and past SSL values (M1: streamflow, M2: streamflow and precipitation, M3: streamflow, precipitation, and SSL). The prediction accuracy of the models was visually compared with the Coefficient of Determination (R2), Bias Factor (BF), Mean Absolute Error (MAE), Mean Bias Error (MBE), Root Mean Square Error (RMSE), Akaike Information Criterion (AIC), Kling-Gupta Efficiency (KGE) statistical criteria and Bland-Altan plot, boxplot, scatter plot and line plot. Based on the analyses, the PSO-ANN model in the M1 model combination showed good estimation performance with an RMSE of 1739.92, MAE of 448.56, AIC of 1061.55, R2 of 0.96, MBE of 448.56, and BF of 0.29. Similarly, the SFLA-ANN model in the M2 model combination had an RMSE of 1819.58, MAE of 520.64, AIC of 1069.9, R2 of 0.96, MBE of 520.64, and BF of 0.19. In the M3 model combination, the SFLA-ANN model achieved an RMSE of 1423.09, MAE of 759.88, AIC of 1071.9, R2 of 0.81, MBE of 411.31, and BF of -0.77. Overall, these models can be considered good estimators as their predicted values are generally close to the measured values. The study outputs can help ensure water structures’ effective lifespan and operation and take precautions against sediment-related disaster risks.
RDL Kalıcı Kaydıİncele - CorrectionAçık Erişim2024Corrigendum to “Short lead time standard precipitation index forecasting: Extreme learning machine and variational mode decomposition” [J. Hydrol. Reg. Stud. 54 (2024) 101861] (Journal of Hydrology: Regional Studies (2024) 54, (S221458182400209X), (10.1016/j.ejrh.2024.101861))
Ladouali, Sabrina; Katipoğlu, Okan Mert; Bahrami, Mehdi; Kartal, Veysi; Sakaa, Bachir; Elshaboury, Nehal; Keblouti, Mehdi; Chaffai, Hicham; Salem, Ali; Pande, Chaitanya B.
The authors regret the need to issue this corrigendum to acknowledge the change in author name and address for correspondence. 1- The author's name “Salem Ali” should be “Ali Salem” 2- Corresponding author at: Structural Diagnostics and Analysis Research Group, Faculty of Engineering and Information Technology, University of Pécs, Hungary We thank the journal for accommodating this update. The authors would like to apologise for any inconvenience caused.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2024Applying 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.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2024An 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.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2024Advanced 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.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2024Understanding 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.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2024Sediment 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.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2024Bee-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.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2024Modeling 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.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2024Enhancing 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.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2024A 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.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2023Combining 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.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2023Evaluation of hydro-meteorological drought indices for characterizing historical droughts in the Mediterranean climate of Algeria
Achite, Mohammed; Bazrafshan, Ommolbanin; Katipoğlu, Okan Mert; Azhdari, Zahra
Determining drought indices and characteristics in Algeria is crucial because droughts significantly impact water resources and agricultural production. Additionally, identifying the most suitable drought indicator for the region facilitates effective monitoring of droughts. The study’s main objective is to compare hydro-meteorological droughts, determine their distribution, and assess drought risk. Various drought indices, which are continuous functions of rainfall and other hydro-meteorological variables, are typically used for this purpose. This study calculates seven indices' effectiveness for drought monitoring and assessment in Algeria's Wadi Ouhrane basin. For this purpose, the temporal variation of drought indicators, distribution graphs, weighted Cohen's kappa (Kw), and correlation coefficient values was compared. Among the indices used in the study, four indices, namely SPI, CZI, ZSI, and MCZI, showed high similarity in their behavior. As the time scale increases from 1 to 24 months, the correlation coefficient exceeds 0.97, and Kw becomes greater than 0.57. Furthermore, there is a weak correlation (R < 0.4) between the meteorological and hydrological-based SRI indicators, and the highest correlation was found between the RDI and SRI indices. Therefore, these indices indicate that the precipitation and ET (temperature) ratio is more suitable for hydrological drought studies.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2023Application of novel artificial bee colony optimized ANN and data preprocessing techniques for monthly streamflow estimation
Katipoğlu, Okan Mert; Keblouti, Mehdi; Mohammadi, Babak
Streamflow estimation is important in hydrology, especially in drought and flood-prone areas. Accurate estimation of streamflow values is crucial for the sustainable management of water resources, the development of early warning systems for disasters, and for various applications such as irrigation, hydropower production, dam sizing, and siltation management. This study developed the ANN algorithm by optimizing with an artificial bee colony (ABC). Then, the ABC-ANN hybrid model, which was established, was combined with different signal decomposition techniques to evaluate its performance in streamflow estimation in the East Black Sea Region, Türkiye. For this purpose, the lagged streamflow values were divided into subcomponents using the local mean decomposition (LMD) with the empirical envelope and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) signal decomposition techniques presented to the ABC-ANN algorithm. Thus, the success of the novel hybrid LMD-ABC-ANN and CEEMDAN-ABC-ANN approaches in streamflow prediction was evaluated. The outputs are reliable strategies and resources for water resource planners and policymakers.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2023Application of empirical mode decomposition, particle swarm optimization, and support vector machine methods to predict stream flows
Katipoğlu, Okan Mert; Yeşilyurt, Sefa Nur; Dalkılıç, Hüseyin Yıldırım; Akar, Funda
Modeling stream flows is vital for water resource planning and flood and drought management. In this study, the performance of hybrid models constructed by combining least square support vector machines (LSSVM), empirical model decomposition (EMD), and particle swarm optimization (PSO) methods in modeling monthly streamflow was evaluated. For establishing the models, 42 years of monthly average streamflow data was used in two hydrometer stations located in the Konya Closed Basin, covering 1964 to 2005. Lagged streamflow values were selected as inputs according to partial autocorrelation values in establishing the models. The dataset was divided into 70% training and 30% testing. Model performances were evaluated according to mean square error, root mean square error, correlation coefficients, scatter plot, and Taylor and Violin diagrams. As a result of the analysis, it was determined that the PSO-LSSVM and EMD-LSSVM models were slightly more successful than the single LSSVM model, and the best model was obtained with the EMD-PSO-LSSVM. In addition, in estimating monthly stream flows, 1-, 9-, 10-, 11-, and 12-month lagged streamflow values were the input combination that gave the best results in semi-arid climatic regions. This result demonstrated that EMD improved the performance of both LSSVM and PSO-LSSVM models by 1% to 5% based on correlation coefficient (R) values.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2023Application of boosted tree algorithm with new data preprocessing techniques in the forecasting one day ahead streamflow values in the Tigris basin, Türkiye
Katipoğlu, Okan Mert; Sarıgöl, Metin
Accurate streamflow forecasting is very useful in water resources management, design of hydraulic structures, and almost all issues related to the use of water and water resources, especially in arid regions that have increased in recent years. Since water is the source of all life and the most important basic element for humanity to continue its life, streamflow prediction studies increase its importance daily. This research combined the boosted tree (BT) model with robust empirical mode decomposition, empirical mode decomposition, complete ensemble empirical mode decomposition with adaptive noise, empirical wavelet transforms and variational mode decomposition for predicting daily average streamflow data. While historical streamflow data was input in the model's setup, one-day lead-time streamflow data was used as the target. 70% of the data is reserved for training and the rest for testing. 5-fold cross-validation technique was used to solve the over-fitting problem. The coefficient of determination, mean squared error, Nash-Sutcliffe efficiency and percent bias statistical criteria and Taylor diagrams, polar plot, scattering diagram, and violin plot were used to determine the algorithm's success. At the end of the study, it was found that the most successful streamflow predictions were made with the variational mode decomposition-based BT hybrid approach.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2023Revealing the trend and change point in Hargreaves equation based on potential evapotranspiration values with various statistical approaches
Katipoğlu, Okan Mert
Hotter and drier weather conditions due to climate change negatively affect water resources and agricultural production. For this reason, it is vital to analyze the change in potential evapotranspiration (PET) values, which is one of the most important parameters related to plant growth and agricultural irrigation planning. This study analyses the trends and changes in monthly and annual PET values between 1965 and 2018 at Erzincan, Bayburt and Gümüşhane meteorological stations in Turkey. For this purpose, monotonic trends in PET values were determined by Spearman’s rho (SR), Mann–Kendall (MK), Şen slope (SS), and innovative trend analysis (ITA) tests and change points were analyzed with the sequential MK (SQMK) test. The Hargreaves equation was used to calculate the PET values. As a result of the study, according to MK and SR tests, while increasing trends at 95% and 99% significance levels were dominant at Erzincan and Bayburt stations, no statistically significant trends were found at Gümüşhane station except in February. ITA generally detected more than 5% increasing trends in PET data’s low, medium, and high values. According to ITA slope analysis, there are significant increase trends in PET values at all periods, trend 1% significance level. According to the SQMK test, it was revealed that the trend started in PET values, especially in 1995, 2005, and 2010. The findings emphasized the importance of taking measures against decreased agricultural production and managing water resources effectively.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2023Modeling the effect of meteorological variables on streamflow estimation: application of data mining techniques in mixed rainfall–snowmelt regime Munzur River, Türkiye
Katipoğlu, Okan Mert
Revealing the dynamic link between rainfall and runoff, which are the main components of the hydrological cycle, is significant for the planning and managing water resources, disaster risk management, and construction of water structures. This study used feed-forward neural network (FFNN), adaptive neuro-fuzzy inference system (ANFIS), and long short-term memory (LSTM) network to model the rainfall-runoff relationship. Various variations of lagged precipitation, temperature, relative humidity, and flows were presented as inputs, and the flow values of Munzur River were estimated as outputs. During the selection of input parameters, variables with high correlation to flow values were utilized. The model’s success was tested using several statistical indicators, including the coefficient of correlation (R), coefficient of determination (R2), and root mean square error (RMSE). When measuring values and model results are compared, FFNN and ANFIS models show accurate predictive results with high accuracy, while LSTM prediction results are not satisfactory. However, it was concluded that the FFNN model with the hyperbolic tangent sigmoid transfer function and Levenberg-Marquardt training algorithm made a slightly more accurate estimation. In addition, it was revealed that the best ANFIS-Sugeno model was obtained with a hybrid learning algorithm, Gaussmf membership function, and eight subsets. As a result of the analysis, it has been found that FFNN is superior to ANFIS in flow prediction. These results provide policymakers and planners with helpful information for developing flood and drought management strategies.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2023Prediction of flood routing results in the Central Anatolian region of Türkiye with various machine learning models
Katipoğlu, Okan Mert; Sarıgöl, Metin
Flood routing models are vital in predicting floods and taking all necessary precautions in the region where floods occur, preventing loss of life and property in the region and protecting agricultural areas. This study aims to compare the performance of various machine learning models such as Bagged Tree, Gradient-Boosted Machine, Random Forest, K-Nearest Neighbor, Support Vector Machine and Extreme Gradient Boosting for flood routing prediction models in Ankara, Eskişehir and Sivas. In addition, the predictive success of tree-based algorithms established according to the optimized and default parameters was compared. For this purpose, the flood data of 2013, 2014 and 2015 discharge observation stations located in Ankara D12A242-D12A126, D12A170-D12A172 in Eskişehir and D15A290-E15A035 in Sivas were used. While establishing the machine learning (ML) models, the data was selected as 80% training and 20% testing. Model performances were tested according to various statistical indicators such as root mean square error, mean absolute error and determination coefficient. As a result of the study, the Gradient-Boosted Machine was chosen as the most successful model in estimating flood routing. In addition, the K-nearest neighbor model with 3-nearest neighbor achieved high-level prediction success with the lowest error rates in Ankara. The findings are important in terms of flood management and taking necessary precautions before the flood occurs.
RDL Kalıcı Kaydıİncele