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- ArticleYalnız künye2026A New Paradigm in Flood Management: Hydrological, Hydraulic, and Deep Learning Methods for Flood Routing
Sarıgöl, Metin; Katipoğlu, Okan Mert; Aktürk, Gaye
Flood routing is essential for water resources management and flood mitigation, yet traditional methods often struggle to represent complex hydraulic processes and rapidly changing flow conditions. In this study, hydrological models (Muskingum and SCS), hydraulic methods (Kinematic Wave, Muskingum–Cunge, and Dynamic Wave), and deep learning (DL) algorithms (Autoencoder, Deep Neural Network (DNN), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Recurrent Neural Network (RNN)) were applied to predict flood routing. Model performance was evaluated using several statistical indicators, including RMSE, AIC, MAE, KGE, NSE, R², Pbias, and MBE. The results showed that the Autoencoder model provided the best predictive performance (RMSE: 0.30, MAE: 0.17, NSE: 0.97, R²: 0.99), while the RNN model produced the weakest results. Other DL models, particularly DNN, CNN, and LSTM, also demonstrated strong predictive capability. Among the hydraulic approaches, the Kinematic Wave method yielded the most accurate results, whereas the SCS model showed the best performance among hydrological routing methods. Overall, the findings indicate that DL models generally outperform classical routing approaches in flood prediction, with the Autoencoder architecture emerging as the most effective model under the conditions of the present study.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2025Advanced Soft Computing Techniques for Monthly Streamflow Prediction in Seasonal Rivers
Achite, Mohammed; Katipoğlu, Okan Mert; Kartal, Veysi; Sarıgöl, Metin; Jehanzaib, Muhammad; Gül, Enes
The rising incidence of droughts in specific global regions in recent years, primarily attributed to global warming, has markedly increased the demand for reliable and accurate streamflow estimation. Streamflow estimation is essential for the effective management and utilization of water resources, as well as for the design of hydraulic infrastructure. Furthermore, research on streamflow estimation has gained heightened importance because water is essential not only for the survival of all living organisms but also for determining the quality of life on Earth. In this study, advanced soft computing techniques, including long short-term memory (LSTM), convolutional neural network–recurrent neural network (CNN-RNN), and group method of data handling (GMDH) algorithms, were employed to forecast monthly streamflow time series at two different stations in the Wadi Mina basin. The performance of each technique was evaluated using statistical criteria such as mean square error (MSE), mean bias error (MBE), mean absolute error (MAE), and the correlation coefficient (R). The results of this study demonstrated that the GMDH algorithm produced the most accurate forecasts at the Sidi AEK Djillali station, with metrics of MSE: 0.132, MAE: 0.185, MBE: −0.008, and R: 0.636. Similarly, the CNN-RNN algorithm achieved the best performance at the Kef Mehboula station, with metrics of MSE: 0.298, MAE: 0.335, MBE: −0.018, and R: 0.597.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2024Estimation of monthly evaporation values using gradient boosting machines and mode decomposition techniques in the Southeast Anatolia Project (GAP) area in Turkey
Sarıgöl, Metin; Katipoğlu, Okan Mert
Today, the biggest issue appears to be the increase in drought in some regions brought on by global warming, which has greatly increased the significance of water management. In light of evaporation's effect on drought, this research intends to evaluate the effectiveness of hybrid machine learning (ML) models, such as the Gradient Boosting Machines (GBM) technique paired with Empirical Mode Decomposition (EMD), Robust Empirical Mode Decomposition (REMD), Ensemble Empirical Mode Decomposition (EEMD), and Variational Mode Decomposition (VMD) signal decomposition techniques, for monthly evaporation prediction models in the Southeast Anatolia Project Area. In the design of the models, 80% of the data was used for training and 20% for testing. Furthermore, tenfold cross-validation was applied to solve the overfitting problem, which negatively affected the forecast performance. In the model setup, various combinations of precipitation, average air temperature, minimum air temperature, maximum air temperature, wind speed, actual air pressure, relative humidity, and solar time variables are presented to artificial intelligence models as input. The study revealed that the GBM methodology in combination with the signal decomposition methods REMD, EMD, EEMD, and VMD generally allowed for more accurate evaporation estimations than the GBM model alone. The study’s results are essential in relation to agricultural production, irrigation planning, water resources management studies, and hydrological modeling studies in the region.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2024Evaluating the Accuracy of Machine Learning, Deep Learning and Hybrid Algorithms for Flood Routing Calculations
Sarıgöl, Metin
The increase in average temperatures in the last century has caused global warming, which has increased the frequency of natural disasters. Floods are one of the most important natural disasters and harm the environment and especially human life. Flood routing techniques also play an important role in predicting floods. For this reason, the accuracy and precision of flood routing calculations are of vital importance in taking all necessary precautions before the floods reach the region and in preventing loss of life. This study aims to compare the performance of machine learning, deep learning and hybrid algorithms for flood routing prediction models in the Büyük Menderes River. In this research deep learning model Long-Short Term Memory (LSTM), machine learning model Artificial Neural Network (ANN), and hybrid machine learning models empirical mode decomposition (EMD)-ANN, and particle swarm optimization (PSO)-ANN algorithms were compared to forecast the flood routing results in two discharge observation stations in the Büyük Menderes river. The analysis results of the established ML algorithms were compared with statistical criteria such as mean error, mean absolute error, root mean square error and coefficient of determination. Additionally, Taylor diagrams, box plots, and beeswarm plot visual graphs were also used in this comparison analysis. At the end of the research, it was determined that the hybrid algorithm PSO-ANN was the most successful algorithm in forecasting flood routing results among other models according to the error values of MAE: 0.2514, MSE: 0.4613, RMSE: 0.6791, NSE: 0.941 and MBE: 0.047. Moreover, the LSTM algorithm was the approach with second estimation accuracy. The findings are vital in terms of taking necessary precautions and gaining time before floods reach any region.
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 - 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 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 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 - 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 - 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 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.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2023Boosting flood routing prediction performance through a hybrid approach using empirical mode decomposition and neural networks: a case study of the Mera River in Ankara
KatipoğLu, Okan Mert; Sarıgöl, Metin
Flood routing is vital in helping to reduce the impact of floods on people and communities by allowing timely and appropriate responses. In this study, the empirical mode decomposition (EMD) signal decomposition technique is combined with cascade forward backpropagation neural network (CFBNN) and feed-forward backpropagation neural network (FFBNN) machine learning (ML) techniques to model 2014 floods in Ankara, Mera River. The data are split in order to avoid the underfitting and overfitting problems of the algorithm. While establishing the algorithm, 70% of the data were divided into training, 15% testing and 15% validation. Graphical indicators and statistical parameters were used for the analysis of model performance. As a result, the EMD signal decomposition technique has been found to improve the performance of ML models. In addition, the EMD-FFBNN hybrid model showed the most accurate estimation results in the flood routing calculation. The study’s outputs can assist in designing flood control structures such as levees and dams to help reduce flood risk.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2021Günlük Ortalama Akımların Yapay Sinir Ağları Metodu ile Taşkın Öteleme Hesabında Kullanılması
Sarıgöl, Metin
Bu çalışmada, Büyük Menderes Havzası’ndaki Menderes Nehri üzerinde yer alan E07A043 Ertuğrul ve D07A115 Yenice Regülatör Girişi Akım Gözlem İstasyonlarına (AGİ) ait günlük ortalama hidrograf verileri kullanılmıştır. Bu veriler Yapay Sinir Ağları Metodu kullanılarak eğitim ve test aşamalarından geçirilmiş, kurulan bu modele 2015 yılındaki taşkın verileri uygulanarak Taşkın Öteleme hesapları yapılmıştır. Elde edilen sonuçlar, hem ölçülmüş değerlerle hem de Yapay Sinir Ağları Metodu sonuçları ile karşılaştırılmıştır. Karşılaştırma analizinde Belirlilik Katsayısı (R2 ), Hataların Ortalama Karekökü (HOK) değerleri hesaplanmıştır. Sonuçta, günlük akım verilerinin uygulandığı Yapay Sinir Ağları Metodu ile yapılan tahmin sonuçlarının oldukça iyi sonuçlar verdiği, bir sonraki yıla ait taşkın öteleme sonuçlarının ise iyi sayılabilecek bir düzeyde olduğu tespit edilmiştir.
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