Yazarlar
- Katipoğlu, Okan Mert(75)
- Achite, Mohammed(21)
- Kartal, Veysi(15)
- Elshaboury, Nehal(12)
- Sarıgöl, Metin(12)
- Ertugay, Neşe(9)
- Katipoglu, Okan Mert(7)
- Pande, Chaitanya Baliram(6)
- Saroughi, Mohsen(6)
- Yavuz, Emre(6)
- Aktürk, Gaye(5)
- Caglar, Bulent(5)
- Elbeltagi, Ahmed(5)
- Gürses, Ahmet(5)
- Keblouti, Mehdi(5)
- Kilinc, Huseyin Cagan(5)
- Pandey, Kusum(5)
- Samui, Pijush(5)
- Acar, Reşat(4)
- Bazrafshan, Ommolbanin(4)
- Dalkilic, Huseyin Yildirim(4)
- Dalkılıç, Hüseyin Yıldırım(4)
- Emami, Somayeh(4)
- Ertugay, Nese(4)
- Jehanzaib, Muhammad(4)
- Kocakaplan, Nihal(4)
- Malkoç, Emine(4)
- Mirzania, Ehsan(4)
- Mohammadi, Babak(4)
- Simsek, Oguz(4)
- Abdallah, Mohammed(3)
- Bailek, Nadjem(3)
- Berhail, Sabri(3)
- El Nemr, Ahmed(3)
- Güner, Eda Keleş(3)
- Karakoyun, Erkan(3)
- Ozkan, Furkan(3)
- Santos, Celso Augusto Guimarães(3)
- Sökmen, Teoman Özgür(3)
- Uzun, Sinem(3)
Yayınlar
- 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 künye2023Comparison of different optimized machine learning algorithms for daily river flow forecasting
Samui, Pijush; Yesilyurt, Sefa Nur; Dalkilic, Huseyin Yildirim; Yaseen, Zaher Mundher; Roy, Sanjiban Sekhar; Kumar, Sanjay
River flow modeling is essential for critical aspects such as effective water management and structure planning, together with flood and drought analysis. There has been a growing interest in modeling hydrological systems via machine learning (ML) models. Various optimization techniques are utilized to develop the applications of ML-based hydrological models. The ultimate aim of this research is to establish high performance forecasting model. Therefore, this study conducts river flow modeling by using the daily data attained from a gauge station situated in the Euphrates Basin. For this purpose, Artificial Neural Network (ANN) model was hybridized with five different optimization algorithms i.e., Artificial Bee Colony (ABC), Teaching-Learning Based Optimization (TLBO), Ant Colony Optimization (ACO), Ant-Lion Optimization (ALO), and Imperialist Competitive Algorithm (ICA). In determining the inputs used to create the models, the distribution graph and correlation of the data with the previous period data were examined. The results were evaluated with eleven different statistical parameters and an error matrix. Examining the obtained results, the study revealed all models present high performance. When the results were reviewed in general, it was seen that all determination coefficient (R2) and Nash-Sutcliffe coefficient (NSE) values were higher than 0.962, and other parameters were very close to the optimum. By comparing all the developed hybrid models, ANN-ALO model reported the highest performance for river flow forecasting.
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 künye2023Application of deep learning approaches to predict monthly stream flows
Dalkilic, H. Yildirim; Kumar, Deepak; Samui, Pijush; Dixon, Barnali; Yesilyurt, S. Nur; Katipoğlu, O. Mert
Accurate and reliable flow estimations are of great importance for hydroelectric power generation, flood and drought risk management, and the effective use of water resources. This research carries out a comprehensive study on the application of gated recurrent unit (GRU) neural network, recurrent neural network (RNN), and long short-term memory (LSTM) to predict with river flows at three different streamflow observation stations in Erzincan, Bayburt, and Gümüshane. Monthly streamflow time series covering the years 1978 to 2015 were used to set up artificial intelligence models. During the modeling phase, 70% of the data was divided into training (October 1978–April 2004), 15% validation (May 2004–September 2009), and 15% test set (October 2010–September 2015). Model performances were made according to the correlation coefficient, root mean square error, the ratio of RMSE to the standard deviation, Nash–Sutcliffe efficiency coefficient, index of agreement, and volumetric efficiency values. The calculation results show that GRU leads efficient estimation results for estimating streamflow and can also be used in allied water resources.
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 - ArticleAçık Erişim2023Application of Soft Computing Techniques in River Flow Modeling
Yesilyurt, Sefa Nur; Dalkilic, Huseyin Yildirim; Samui, Pijush
Modeling of data is critical in the analysis and evaluation of hydrological behavior. River flow data is one of the most important data in explaining hydrology. Management of water resources; It takes place in the literature as an area that needs to be investigated in order to provide early warning for undesirable situations such as floods and drought. For this reason, it is of important to develop different techniques for the estimation and modeling of river flow or to make comparisons between techniques. In this study, the flow data of fourteen stations located in the Euphrates-Tigris basin between 1981 and 2010 were used. Adaptive Network Based Fuzzy Inference Systems (ANFIS), Support Vector Machine (SVM) techniques that are frequently used in the literature, and newly introduced Gaussian Process Regression (GPR), Extreme Learning Machine (ELM) and Emotional Neural Network (ENN) artificial intelligence techniques are compared. In addition, considering all performance indices, it was determined which technique gave better results with rank analysis. Although all models worked well, it was seen that the methods were ranked as ELM, GPR, ENN, SVM and ANFIS starting from the best. This has shown that ELM, GPR and ENN methods, which have been used recently in flow modeling, give better results than traditional methods with complex structures. In addition, flow values were used in the whole study and these values were examined in 3 different combinations. It was seen that the model structure that gave the best performance was the model structure that used the flow data from one, two and three days ago as an estimator. The results were analyzed with a Taylor diagram and time series graphs.
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 - ArticleAçık Erişim2023Prediction of Streamflow Drought Index for Short-Term Hydrological Drought in the Semi-Arid Yesilirmak Basin Using Wavelet Transform and Artificial Intelligence Techniques
Katipoğlu, Okan Mert
The prediction of hydrological droughts is vital for surface and ground waters, reservoir levels, hydroelectric power generation, agricultural production, forest fires, climate change, and the survival of living things. This study aimed to forecast 1-month lead-time hydrological droughts in the Yesilirmak basin. For this purpose, support vector regression, Gaussian process regression, regression tree, and ensemble tree models were used alone and in combination with a discrete wavelet transform. Streamflow drought index values were used to determine hydrological droughts. The data were divided into 70% training (1969–1998) and 30% (1999–2011) testing. The performance of the models was evaluated according to various statistical criteria such as mean square error, root means square error, mean absolute error, and determination coefficient. As a result, it was determined that the prediction performance of the models obtained by decomposing into subcomponents with the discrete wavelet transform was optimal. In addition, the most effective drought-predicting model was obtained using the db10 wavelet and MGPR algorithm with mean squared error 0.007, root mean squared error 0.08, mean absolute error 0.04, and coefficient of determination (R2) 0.99 at station 1413. The weakest model was the stand-alone FGSV (RMSE 0.88, RMSE 0.94, MAE 0.76, R2 0.14). Moreover, it was revealed that the db10 main wavelet was more accurate in predicting short-term drought than other wavelets. These results provide essential information to decision-makers and planners to manage hydrological droughts in the Yesilirmak basin.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2023Coupling machine learning with signal process techniques and particle swarm optimization for forecasting flood routing calculations in the Eastern Black Sea Basin, Türkiye
Katipoğlu, Okan Mert; Sarıgöl, Metin
With the effect of global warming, the frequency of floods, one of the most important natural disasters, increases, and this increases the damage it causes to people and the environment. Flood routing models play an important role in predicting floods so that all necessary precautions are taken before floods reach the region, loss of life and property in the region is prevented, and agricultural lands are protected. This research aims to compare the performance of hybrid machine learning models such as least-squares support vector machine technique hybridized with particle swarm optimization, empirical mode decomposition, variational mode decomposition, and discrete wavelet transform processes for flood routing estimation models in Ordu, Eastern Black Sea Basin, Türkiye. In addition, it is aimed to examine the effect of data division in flood forecasting. Accordingly, 70%, 80%, and 90% of the data were used for training, respectively. For this purpose, the flood data of 2009 and 2013 in Ordu were used. The performance of the established models was evaluated with the help of statistical indicators such as mean bias error, mean absolute percentage error, determination coefficient, Nash–Sutcliffe efficiency, Taylor Diagrams, and boxplot. As a result of the study, the particle swarm optimization least-squares support vector machine technique was chosen as the most successful model in predicting flood routing results. In addition, the optimum data partition ratio was found to be Train:70:Test:30 in the flood routing calculation. The findings are essential regarding flood management and taking necessary precautions before the flood occurs.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2023Investigation of Giardia spp., Cryptosporidium spp. and Cyclospora cayetanensis in Samples Collected from Different Spring Waters Iğdır, Türkiye; [Farklı Kaynak Sularından Alınan Örneklerde Giardia spp., Cryptosporidium spp. ve Cyclospora cayetanensis’in Araştırılması Iğdır, Türkiye]
Akkaş, Önder; Gürbüz, Esra; Aydemir, Selahattin; Şahin, Maksut; Ekici, Abdurrahman
Objective: In this study, it was aimed to investigate the physical and chemical properties of different spring waters and parasitic factors with different methods. Methods: This study was carried out on 69 water samples collected from different spring waters in and around Iğdır Province in April and June 2021. The samples were analyzed by native-Lugol, modified acid-fast staining, and nested polymerase chain reaction (nPCR). In addition, altitude (meter) and pressure (mmHg) measurements were made at the point where the water samples were taken. Results: One or more parasites were detected in 27.5% of the 69 water samples examined. Only C. cayetanensis was found in 13% of the samples, only Cryptosporidium spp. in 10.1%, only Giardia spp. in 1.4%, only C. cayetanensis and Giardia spp. in 1.4%, only C. cayetanensis, Cryptosporidium spp., and Giardia spp. in 1.4%. Only Giardia spp. cyst (4.3%) was detected by the direct examination method. While C. cayetanensis and Cryptosporidium spp. oocysts were detected in 8.7% and 7.2% of the samples by the modified acid-fast staining method, C. cayetanensis was detected in 15.9% and Cryptosporidium spp. was detected in 11.6% of the samples by nPCR. When the C. cayetanensis and Cryptosporidium spp. positivity rates were compared according to the characteristics of the water, there was no statistical difference between the altitude, salinity, pH, mmHg, and temperature (kelvin) values, but a significant correlation was found between the amount of dissolved oxygen and Cryptosporidium spp. positivity (p=0.047). Conclusion: Cryptosporidium spp., C. cayetanensis, and G. intestinalis are important waterborne pathogens that can cause epidemics. It is our belief that in order to reduce the risk of contamination of these parasitic factors with spring waters, public awareness should be raised, infrastructures should be improved, and new water treatment techniques, such as ultraviolet, ozonation and monitoring systems, should be used.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2023Monthly streamflow prediction in Amasya, Türkiye, using an integrated approach of a feedforward backpropagation neural network and discrete wavelet transform
Katipoğlu, Okan Mert
Due to climate change and increasing demand for water, effective planning of water resources is a current issue. Reliable and accurate streamflow prediction is of great importance in the planning of water resources. This study aimed to predict monthly streamflows in Amasya by combining a discrete wavelet transform and a feedforward backpropagation neural network (FFBPNN) model. Various meteorological variables were separated into sub signals with mother wavelets commonly used in hydrometeorological studies, such as Haar, Daubechies 2, Daubechies 4, Discrete Meyer, Coiflet 3, Coiflet 5, Symlet 3, and Symlet 5, and entered into the FFBBNN model to create a hybrid wavelet-based FFBBNN model. Inputs with a significant relationship with the output were entered into the model. Precipitation, temperature, and previous streamflow values covering 1960–2011 were used to create the model. During the modeling phase, 70% of the data were divided into training, 15% into validation, and 15% into testing. The performance of the model was compared using mean square error, correlation coefficient, and rank analysis. Coiflet 5 mother wavelet showed the best results. Moreover, it was proven that monthly streamflow can be successfully predicted using previous precipitation, temperature, and streamflow values and the Coiflet 5 mother wavelet with the FFBBNN hybrid model (MSE: 7.143, R: 0.921). In addition, all built wavelet FFBPNN models except the Symlet 3 mother wavelet performed better than the single FFBPNN model. The results of the study will assist planners, and decision makers in terms of providing sustainable and effective water resources and drought management.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2023Hydrological Drought Prediction Based on Hybrid Extreme Learning Machine: Wadi Mina Basin Case Study, Algeria
Achite, Mohammed; Katipoğlu, Okan Mert; Jehanzaib, Muhammad; Elshaboury, Nehal; Kartal, Veysi; Ali, Shoaib
Drought is one of the most severe climatic calamities, affecting many aspects of the environment and human existence. Effective planning and decision making in disaster-prone areas require accurate and reliable drought predictions globally. The selection of an effective forecasting model is still challenging due to the lack of information on model performance, even though data-driven models have been widely employed to anticipate droughts. Therefore, this study investigated the application of simple extreme learning machine (ELM) and wavelet-based ELM (W-ELM) algorithms in drought forecasting. Standardized runoff index was used to model hydrological drought at different timescales (1-, 3-, 6-, 9-, and 12-month) at five Wadi Mina Basin (Algeria) hydrological stations. A partial autocorrelation function was adopted to select lagged input combinations for drought prediction. The results suggested that both algorithms predict hydrological drought well. Still, the performance of W-ELM remained superior at most of the hydrological stations with an average coefficient of determination = 0.74, root mean square error = 0.36, and mean absolute error = 0.43. It was also observed that the performance of the models in predicting drought at the 12-month timescale was higher than at the 1-month timescale. The proposed hybrid approach combined ELM’s fast-learning ability and discrete wavelet transform’s ability to decompose into different frequency bands, producing promising outputs in hydrological droughts. The findings indicated that the W-ELM model can be used for reliable drought predictions in Algeria.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2023Prediction of scour hole characteristics caused by water jets using metaheuristic artificial bee colony-optimized neural network and pre-processing techniques
Kartal, Veysi; Emiroglu, Muhammet Emin; Katipoglu, Okan Mert; Karakoyun, Erkan
Preventing plunge pool scouring in hydraulic structures is crucial in hydraulic engineering. Although many studies have been conducted experimentally to determine relationship between the scour depth and water jets in several fields, available equations have deficiencies in calculating the exact scour due to complexity of scour process. This study investigated local scour depth in plunge pool using Metaheuristic Artificial Bee Colony-Optimized Feed Forward Neural Network (ABCFFNN), variational mode decomposition (VMD) and ensemble empirical mode decomposition (EEMD) techniques. To set modeling, the input parameters are impact angle, densimetric Froude number, impingement length, and nozzle diameter. The models' training and testing were conducted using data available in the literature. The models' performances were compared with experiments. The results demonstrate that scour depth, length, width, and ridge height can be calculated more accurately than available equations. A rank analysis was also applied to obtain the most critical parameter in predicting scour parameters in water jet scouring. ABC-FFNN, VMD-ABCFFNN and EEMD-VMD-FFNN hybrid models were performed to obtain scour parameters. As a result, ABC-FFNN algorithms produced the best solution to predict the scour due to circular water jets, with the values for training (R2: 0.331 to 0.778) and testing (R2: 0.495 to 0.863).
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2023Improving the accuracy of rainfall-runoff relationship estimation using signal processing techniques, bio-inspired swarm intelligence and artificial intelligence algorithms
Katipoğlu, Okan Mert; Sarıgöl, Metin
In water resources engineering, streamflow estimation models are of great importance. The use of black box models in determining streamflow estimation is preferred because it saves time compared to deterministic models. In addition, the data needed is less in quality and quantity and has a lower transaction volume. Therefore, accurate flow estimation plays a crucial role in water resources planning, management, and sizing of water structures. This study aimed to forecast the monthly streamflow using particle swarm optimization, least-squares support-vector machine, and signal processing models. For this purpose, 575 monthly data between 1963-2010 were used in Sivas, and 622 monthly data between 1960-2011 were used in Kayseri. The model structure used past streamflow, temperature, and precipitation values as input. During the installation of hybrid models, the inputs are divided into subcomponents with three levels of discrete Meyer mother wavelet and presented to the LS-SVM model. The performance of the models was evaluated with the help of statistical indicators such as determination coefficient, mean absolute error, Nash–Sutcliffe Efficiency, mean square error, Violin plots and visual Taylor diagrams. At the end of the study, EMD-LSSVM was the best streamflow estimation model at station 1501, and W-LSSVM was the most successful streamflow estimation model at station 1535.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2023Spatiotemporal detection of abrupt change in trends of rainfall and dry and wet periods at different time scales: The case of the Medjerda basin in northeast Algeria
Berhail, Sabri; Katipoğlu, Okan Mert
This study investigates possible rainfall and drought trends using data from 38 rainfall stations in the Medjerda basin (northeast of Algeria) over 54 years (1965–2018). Drought-related data were calculated with the Standardized precipitation index (SPI). The Mann–Kendall test was used to find positive or negative precipitation trends. The magnitude of these trends was calculated using Sen’s slope method. According to the analysis, a decrease during the spring precipitation season was observed. Furthermore, the authors found the maximum increasing (decreasing) precipitation magnitude to be 2.14 mm/season (− 4.41 mm/season) in winter (spring). In addition, the magnitude of the precipitation trend per year ranged from − 6.26 to 2.54 mm/year, with an average reduction of 39% for the entire basin. From the outcomes of drought trend analysis, it can be inferred that for a short-time scale, the innovative trend analysis method exhibited a negative trend for the minimum and maximum SPI values. Drought severity was found to have increased during severe and extreme wet episodes, directly affecting Algeria’s frequently drought-affected agricultural regions, such as the Merdja plain and the irrigated perimeters of Sedrata and Zouabi. Considering the long-time scales, an increase was detected in drought severity and a decline during severe and extreme wet episodes. These findings show that the southeastern and central parts of the Medjerda basin’s long-term water resources have been severely affected, which negatively impacts the newly-constructed Ouldjet Mellegue dam in Tébessa province.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2023Modeling of meteorological, agricultural, and hydrological droughts in semi-arid environments with various machine learning and discrete wavelet transform
Achite, Mohammed; Katipoglu, Okan Mert; Şenocak, Serkan; Elshaboury, Nehal; Bazrafshan, Ommolbanin; Dalkılıç, Hüseyin Yıldırım
Recent meteorological, hydrological, and agricultural droughts in the Mediterranean regions have raised concerns about the impact of climate change. In this study, the meteorological, agricultural, and hydrological droughts were modeled in the Wadi Ouahrane Basin using various machine learning (ML) models and standardized indices of rainfall, evapotranspiration, and runoff (SPI, SPEI, and SRI) at different times scales (1, 3, 6, 9, 12, and 24 months). The applied ML models were the linear support vector machine (SVM), quadratic SVM, cubic SVM, fine gaussian SVM, medium gaussian SVM, coarse Gaussian SVM, rational quadratic Gaussian process regression (GPR), squared exponential GPR, Matern 5/2 GPR, exponential GPR, bagged tree, and boosted tree. Moreover, the hybrid models acquired by combining these ML models with wavelet transform were evaluated. The performance of the models was analyzed using statistical criteria such as root mean square error, determination coefficient, and mean absolute error. As a result, wavelet-GPR models showed the most promising results in estimating SPI, SPEI, and SRI values. The values for SPI (R 2 of train: 0.393; and test: 0.351), SPEI (R 2 of train: 0.809; test: 0.746), and SRI (R 2 of train: 0.999; test: 0.808) indicate monthly time scale. Additionally, for the time periods of 3, 6, 9, 12, and 24 months, the predictions for SPI, SPEI, and SRI generally obtain R 2 of train 0.99 and test 0.95 values. Moreover, it was determined that wavelet-based ML models, established with inputs divided into three subcomponents with Daubechies mother wavelet, showed superior results than standalone ML models. The study results could guide decision-makers and planners in developing drought risk management and mitigation strategies.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2023Estimation of hourly flood hydrograph from daily flows using machine learning techniques in the Büyük Menderes River
Sarıgöl, Metin; Katipoğlu, Okan Mert
Flood routing calculations are of vital importance in estimating the floods occurring in the downstream region and taking all necessary measures to minimize the damages that the flood may cause. The prediction success of the created algorithms in the flood routing analysis was tried to be measured by training the daily flood data with machine learning algorithms. This research trained the daily average flood hydrograph data with regression tree, Gaussian process regression, support vector machine, ANFIS and regression tree ensembles. Then the flood routing calculations were made using these models for the hourly flood hydrograph of 2015. The success of the created machine learning algorithms was compared with statistical criteria such as mean absolute error, the root of mean squares of errors and coefficient of determination (R 2), and visually with Taylor diagrams and boxplots. As a result of the analysis established, the ANFIS model divided the inputs into 5 subsets, and the trim membership function showed good results in flood routing. It has been proved that with limited flow data, floods can be postponed, and loss of life and property can be prevented.
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