Sürdürülebilir Kalkınma Amaçları
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- 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 - 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 - ArticleYalnız künye2023Implementation of hybrid wind speed prediction model based on different data mining and signal processing approaches
Katipoğlu, Okan Mert
Accurate estimation of wind speed (WS) data, which greatly influences meteorological parameters, plays a vital role in the safe operation and optimization of the power system and water resource management. The study’s main aim is to combine artificial intelligence and signal decomposition techniques to improve WS prediction accuracy. Feed-forward back propagation neural network (FFBNN), support vector machine (SVM) and Gaussian processes regression (GPR) models, discrete wavelet transform (DWT), and empirical mode decomposition (EMD) were used to forecast the WS values 1 month ahead in Burdur meteorology station. Statistical criteria such as Willmott’s index of agreement, mean bias error, mean squared error, determination coefficient, Taylor diagram and regression analysis, and graphical indicators were used to evaluate the prediction success of the models. As a result of the study, it was determined that both wavelet transform and EMD signal processing increased the WS prediction performance of the stand-alone ML model. The best performance was obtained with the hybrid EMD-Matern 5/2 kernel GPR with test (R2:0.802) and validation (R2:0.606). The most successful model structure was obtained using input variables with a delay of up to 3 months. The study’s results contribute to wind energy-related institutions in terms of practical use, planning, and management of wind energy.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2023Combining discrete wavelet decomposition with soft computing techniques to predict monthly evapotranspiration in semi-arid Hakkâri province, Türkiye
Katipoğlu, Okan Mert
Accurate prediction of evapotranspiration values is important in planning agricultural irrigation, crop growth research, and hydrological modeling. This study is aimed at estimating monthly evapotranspiration (ET) values in Hakkâri province by combining support vector regression, bagged tree, and boosted tree methods with wavelet transform. For this purpose, precipitation, runoff, surface net solar radiation, air temperatures, and previous ET values were divided into sub-signals with various mother wavelets such as Daubechies 4, Meyer, and Symlet 2 and presented as input to machine learning (ML) algorithms. The study’s main contribution to the literature is to reveal which wavelet-based machine learning model, mother wavelet type, and combination of meteorological data show the most realistic results in ET estimation. While establishing the models, the data were divided into 80% training and 20% testing. The models’ performances were based on the widely used root mean square error, mean absolute error, determination coefficient, and Taylor diagrams. As a result of the study, it was revealed that the hybrid wavelet ML, which is established with input combinations separated into subcomponents by wavelet transform, generally produces more successful predictions than the stand-alone ML model. In addition, it was revealed that the optimum ET forecasting model was obtained with the wavelet bagged tree algorithm with Symlet 2 mother wavelet. Even though the best model established is based on the precipitation and temperature inputs, it was revealed that past ET, solar radiation, and runoff values are also effective inputs in ET prediction. The results can also be used in other regions of the world with semi-arid climates, such as Hakkâri. The study’s outputs provide essential resources to decision-makers and planners to manage water resources and plan agricultural irrigation.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2023Evaluation of Tree-Based Machine Learning and Deep Learning Techniques in Temperature-Based Potential Evapotranspiration Prediction
Akar, Funda; Katipoğlu, Okan Mert; Yeşilyurt, Sefa Nur; Han Taş, Mehmet Bilge
In this study, Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Random Forest (RF), Bagged Trees (BT), and Custom Deep Learning methods were used to estimate the potential evapotranspiration (PET) values at Diyarbakir airport station in the Tigris basin. In establishing the models, the average temperature, maximum temperature, minimum temperature, maximum wind speed, relative humidity, average wind speed, and total precipitation values in the monthly time period were chosen as inputs, and PET values were used as output. The data set is divided into 70% training and 30% testing. 10-fold cross-validation to avoid overfitting problems. Training and test data were randomly selected. The prediction performances of the models were evaluated according to the statistical criteria of determination coefficient (R2), root mean square error (RMSE), mean absolute error (MAE), and rank analysis. The best PET estimates were obtained using the inputs of mean, min, maximum temperature, relative humidity, total precipitation, average, and maximum wind speed. It was also concluded that XGBoost was the highest performance. When the R2 values were examined, it was seen that the Deep Learning model had higher performance. But for RMSE and MAE, XGBoost did better. As a result of the rank analysis, it was seen that XGBoost got a higher score.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2023Comparison of the SPI and SPEI as drought assessment tools in a semi-arid region: case of the Wadi Mekerra basin (northwest of Algeria)
Berhail, Sabri; Katipoğlu, Okan Mert
The current research intends to compare the agreement level between the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI), the former’s variant, at different timescales such as 1, 3, 6, 9, 12, and 24 months. This data was compiled from the Mekerra basin with the help of time series of rainfall and temperature data for 42 years from 1970 to 2011. The authors used Cohen’s kappa statistics and the Bland–Altman method to determine the test of agreement between SPI and SPEI. Furthermore, the innovative trend analysis (ITA) method was also employed to analyze the severe and extreme dry trends. The outcomes achieved from the Cohen’s kappa statistic revealed the presence of a significant level of agreement between the SPI and SPEI values across the timescales. Furthermore, the researchers observed a positive linear correlation (r > 0.75, p < 0.0001) between the ratings under study. The existence of a small mean difference between the Bland and Altman analyses confirmed the level of agreement between the analytical tools under consideration. In addition to these, the outcomes also infer that in case of the unavailability of the temperature data to conduct the SPEI. Therefore, it is acceptable to use the SPI as the current study found the presence of an acceptable degree of agreement between these two scales in the study location. The present study suggests utilizing various types of drought indices to evaluate the drought performance in a specific location or geographical area to make knowledge-driven decisions.
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 - ArticleAçık Erişim2023Performance Evaluation of Five Machine Learning Algorithms for Estimating Reference Evapotranspiration in an Arid Climate
Raza, Ali; Fahmeed, Romana; Syed, Neyha Rubab; Katipoğlu, Okan Mert; Zubair, Muhammad; Alshehri, Fahad; Elbeltagi, Ahmed
The Food and Agriculture Organization recommends that the Penman–Monteith Method contains Equation 56 (PMF) as a widely accepted standard for reference evapotranspiration (ETo) calculation. Despite this, the PMF cannot be employed when meteorological variables are constrained; therefore, alternative models for ETo estimation requiring fewer variables must be chosen, which means that they perform at least as well as, if not better than, the PMF in terms of accuracy and efficiency. This study evaluated five machine learning (ML) algorithms to estimate ETo and compared their results with the standardized PMF. For this purpose, ML models were trained using monthly time series climatic data. The created ML models underwent testing to determine ETo under varying meteorological input combinations. The results of ML models were compared to assess their accuracy and validate their performance using several statistical indicators, errors (root-mean-square (RMSE), mean absolute error (MAE)), model efficiency (NSE), and determination coefficient (R2). The process of evaluating ML models involved the utilization of radar charts, Smith graphs, heatmaps, and bullet charts. Based on our findings, satisfactory results have been obtained using RBFFNN based on M12 input combinations (mean temperature (Tmean), mean relative humidity (RHmean), sunshine hours (Sh)) for ETo estimation. The RBFFNN model exhibited the most precise estimation as RMSE obtained values of 0.30 and 0.22 during the training and testing phases, respectively. In addition, during training and testing, the MAE values for this model were recorded as 0.15 and 0.17, respectively. The highest R2 and NSE values were noted as 0.98 and 0.99 for the RBFNN during performance analysis, respectively. The scatter plots and spatial variations of the RBFNN and PMF in the studied region indicated that the RBFNN had the highest efficacy (R2, NSE) and lowest errors (RMSE, MAE) as compared with the other four ML models. Overall, our study highlights the potential of ML models for ETo estimation in the arid region (Jacobabad), providing vital insights for improving water resource management, helping climate change research, and optimizing irrigation scheduling for optimal agricultural water usage in the region.
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 - ArticleYalnız künye2023Evaporation Prediction with Wavelet-Based Hyperparameter Optimized K-Nearest Neighbors and Extreme Gradient Boosting Algorithms in a Semi-Arid Environment
Katipoğlu, Okan Mert
The study aims to reveal which mother wavelet type performs best in evaporation prediction. This study used a hybrid algorithm that combined K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost) methods, and discrete wavelet transform to estimate the evaporation values in the summer months in Bursa. While establishing wavelet-machine learning models, Daubechies2, coiflet 2, symlet 2 and biortogonal 2.2 wavelets were obtained by separating various input variables into sub-signals. For the model’s setup, total precipitation, average temperature, average relative humidity, average wind speed, total sunshine duration values in the monthly time period were used as inputs. Total evaporation data were presented as output of the algorithms. The performances of the established models were evaluated with the coefficients of determination, root mean square error, mean absolute error and scatter diagrams. It has been determined that the optimum model is obtained with the hybrid algorithm established by the combination of biorthogonal 2.2 and KNN methods. The study results provide agricultural, meteorological, hydrological and environmental engineers with essential resources for planning and managing water-related issues.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2023Evaluation of the performance of data-driven approaches for filling monthly precipitation gaps in a semi-arid climate conditions
Katipoğlu, Okan Mert
Missing data cause problems in meteorological, hydrological, and climate analysis. The observation data should be complete and cover long periods to make the research more accurate and reliable. Artificial intelligence techniques have attracted interest for completing incomplete meteorological data in recent years. In this study the abilities of machine learning models, artificial neural networks, the nonlinear autoregressive with exogenous input (NARX) model, support vector regression, Gaussian processes regression, boosted tree, bagged tree (BAT), and linear regression to fill in missing precipitation data were investigated. In developing the machine learning model, 70% of the dataset was used for training, 15% for testing, and 15% for validation. The Bayburt, Tercan, and Zara precipitation stations, which are closest to the Erzincan station and have the highest correlation coefficients, were used to fill the data gaps. The accuracy of the constructed models was tested using various statistical criteria, such as root-mean-square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe model efficiency coefficient (NSE), and determination coefficient (R2) and graphical approaches such as scattering, box plots, violin plots, and Taylor diagrams. Based on the comparison of model results, it was concluded that the BAT model with R2: 0.79 and NSE: 0.79 and error (RMSE: 11.42, and MAE: 7.93) was the most successful in the completion of missing monthly precipitation data. The contribution of this research is assist in the choice of the best and most accurate method for estimating precipitation data in semi-arid regions like Erzincan.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2023Predicting hydrological droughts using ERA 5 reanalysis data and wavelet-based soft computing techniques
Katipoğlu, Okan Mert
The effective forecasting of droughts is the basic step of drought risk management, which consists of drought preparedness, mitigation, and early warning phases. This study used historical values of meteorological and hydrological drought indices to predict ERA 5 reanalysis data based on future hydrological droughts with a 1-month lead-time in Diyarbakır, in the Tigris basin. Regression tree (RT), boosted tree (BT), bagged tree (BAT), support vector machines (SVM), and adaptive neuro-fuzzy inference system (ANFIS) models and hybrid wavelet-machine learning models created by combining these models with discrete wavelet transform were analyzed to predict Standardized Runoff Index (SRI) based hydrological droughts. To construct the models and determine the temporal dependencies and relationships between variables, autocorrelation functions (ACF), partial autocorrelation functions (PACF), and correlation matrices were used. In addition, statistical criteria such as root mean square error (RMSE), mean absolute error (MAE) and, coefficients of determination (R 2), and the Taylor diagram were used to evaluate the success of the established models. The study has provided evidence that the application of wavelet transform can significantly improve the efficacy of hydrological drought prediction. The ANFIS model with Train RMSE: 0.65, MAE: 0.47, R 2: 0.49; Test RMSE: 0.84, MAE: 0.64, R 2: 0.39, which combines SRI (T − 1) and SRI (T) inputs, is considered the best stand-alone machine learning model for hydrological drought prediction. Furthermore, the most successful prediction performance is achieved by combining the SRI (T − 3), SRI (T − 2), SRI (T − 1), and SRI (T) inputs using the wavelet transform and BAT algorithm with Train RMSE: 0.59, MAE: 0.45, R 2: 0.59; Test RMSE: 0.74, MAE: 0.60, R 2: 0.48. Accordingly, it was concluded that hydrological droughts could be predicted using only previous hydrological droughts. Therefore, the study's outputs can be used to effectively plan water resources for decision-makers and various institutions in managing drought risk.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2023Daily global solar radiation time series prediction using variational mode decomposition combined with multi-functional recurrent fuzzy neural network and quantile regression forests algorithm
Abdallah, Mohammed; Mohammadi, Babak; Nasiri, Hamid; Katipoğlu, Okan Mert; Abdalla, Modawy Adam Ali; Ebadzadeh, Mohammad Mehdi
Global solar radiation (GSR) prediction capability with a reliable model and high accuracy is crucial for comprehending hydrological and meteorological systems. It is vital for the production of renewable and clean energy. This research aims to evaluate the performance of combined variational mode decomposition (VMD) with a multi-functional recurrent fuzzy neural network (MFRFNN) and quantile regression forests (QRF) models for GSR prediction in daily scales. The hybrid VMD-MFRFNN and QRF models were compared with standalone MFRFNN, random forest (RF), extreme gradient boosting (XGB), and M5 tree (M5T) models across the Lund and Växjö meteorological stations in Sweden. The meteorological data from 2008 to 2017 were used to train the models, while the prediction accuracy was verified by using the data from 2018 to 2021 under five different input combinations. The various meteorological-based scenarios (including the input are air temperatures (Tmin, Tmax, T), wind speed (WS), relative humidity (RH), sunshine duration (SSH), and maximum possible sunshine duration (N)) were considered as input of predictor models. The current study resulted that the M5T model exhibited higher accuracy than RF and XGB models, while the QRF model showed equivalent performance with the M5T model at both study sites. The MFRFNN model outperformed QRF and M5T models across all input combinations at both study sites. The hybrid VMD-MFRFNN model showed the best performance when fewer input variables (Tmin, Tmax, T, WS at Lund station and Tmin, Tmax, T, WS, SSH, RH at Växjö station) were used for GSR prediction. We conclude that the MFRFNN model best predicts average daily GSR when combining all meteorological variables (Tmin, Tmax, T, WS, SSH, RH, N).
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2023Simulation of the Potential Impacts of Projected Climate and Land Use Change on Runoff under CMIP6 Scenarios
Haider, Saif; Masood, Muhammad Umer; Rashid, Muhammad; Alshehri, Fahad; Pande, Chaitanya B.; Katipoğlu, Okan Mert; Costache, Romulus
Assessing the impacts of climate change and land use/land cover changes on water resources within a catchment is essential because it helps us understand how these dynamic factors affect the quantity, quality, and availability of freshwater. This knowledge is crucial for making informed decisions about water management, conservation, and adaptation strategies, especially in regions facing increasing environmental uncertainties and challenges to water resource sustainability. In Pakistan’s Kunhar River Basin (KRB), this investigation explores the potential effects of shifting land use/land cover (LULC), and climate on stream flows. The SWAT (Soil and Water Assessment Tool), a semi-distributed hydrological model, and the most recent Coupled Model Intercomparison Project phase 6 (CMIP6) dataset from multiple global climate models (GCMs) were used to evaluate these effects. The temperature and precipitation data were downscaled using the CMhyd software; for both shared socioeconomic pathways (SSP2 and SSP5), the top-performing GCM out of four was required to produce downscaled precipitation and temperature predictions while taking future land use characteristics into account. The output from the chosen GCM indicated that by the conclusion of the 21st century, relative to the reference period (1985–2014), the study area’s average monthly precipitation, highest temperature, and lowest temperature will be increasing. Precipitation is anticipated to increase between 2015 and 2100 by 20.5% and 29.1% according to the SSP2 and SSP5 scenarios, respectively. This study’s findings, which emphasize the need for project planners and managers taking into account the effects of climate and land cover changes in their management techniques, show that climate change can have a significant impact on the changing seasons of flows in the Kunhar River basin.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2022Dicle Havzasındaki Bazı İstasyonlara ait Potansiyel Evapotranspirasyon Verilerinin Trend Analizi
Katipoğlu, Okan Mert
Potansiyel evapotranspirasyon (PET) değerlerindeki eğilimlerin belirlenmesi, iklim değişikliği ve kuraklık yönetimi, tarımsal su kaynaklarının daha verimli yönetimi ve sulama planlamasının optimal tasarımı konuları açısından büyük öneme sahiptir. Bu çalışmada Dicle havzasında bulunan Batman, Cizre, Diyarbakır ve Hakkâri meteoroloji istasyonları için, 1964-2017 yılları arasında hesaplanan mevsimsel ve yıllık PET değerlerinin trend analizleri gerçekleştirilmiştir. Trendlerin belirlenmesinde Şen in Yenilikçi Trend Analizi (ITA) ve Mann-Kendall Mertebe Korelasyon (MKMK) metotları kullanılmıştır. PET değerlerinin hesaplanmasında sıklıkla kullanılan, basit ve az veri gerektiren Thornthwaite metodolojisi tercih edilmiştir. Araştırma sonunda ITA yöntemine göre, havzada seçilen istasyonların PET verilerinde genelde istatistik açıdan anlamlı artış trendleri hakimken, Diyarbakır istasyonu için ilkbahar ve kış mevsimlerinde istatistik açıdan anlamlı azalan trendler tespit edilmiştir. MKMK testine göre, kış mevsimi dışında istasyon konumlarında PET değerlerinde anlamlı artışlar vardır. Ayrıca ITA ve MKMK testleri büyük ölçüde örtüşmekle birlikte bazı istasyonlarda ITA ile tespit edilen artış trendleri MKMK testi ile saptanamamıştır. Elde edilen sonuçlar tarımsal su kaynaklarının planlanması ve verimli bir şekilde kullanılmasında, küresel ısınmanın etkileriyle mücadele etmek için adaptasyon ve azaltma stratejileri geliştirilmesinde karar vericilere önemli bilgi temin etmektedir.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2022Monthly stream flows estimation in the karasu river of euphrates basin with artificial neural networks approach
Katipoğlu, Okan Mert
This study aims to estimate streamflow values with artificial neural networks (ANN) using various meteorological parameters. In developing the ANN model, various combinations of precipitation, air temperatures, and potential evapotranspiration values were used as inputs, and streamflow values were obtained. Meteorological data is divided into 70% train, 15% test, and 15% validation. In the model's design, various training algorithms, network architecture, input combinations, and the number of iterations were tried, and the most suitable model was tested. Correlation coefficient (R), coefficient of determination (R2), absolute error (AE), and absolute relative error (ARE) coefficients were compared, and the most suitable model was selected. According to the analysis results, the optimal model was obtained using 2000 iterations, the architecture of the 4-4-1 model, and the Quasi-Newton algorithm. It was determined that the ANNs successfully modeled the rainfall-runoff relationship and produced reliable estimates. In addition, it was revealed that the inclusion of potential evapotranspiration values obtained by the Thornthwaite method into the model increases the model's success.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2022Yeşilırmak havzasındaki hidrolojik kuraklıkların Mann-Kendall ve Yenilikçi Şen yöntemi ile trend analizi
Dalkılıç, Hüseyin Yıldırım; Katipoğlu, Okan Mert; Yeşilyurt, Sefa Nur
Bu çalışmada Yeşilırmak havzasında bulunan 7 adet akım gözlem istasyonuna ait aylık ve yıllık zaman periyodundaki akım kuraklık indekslerinin (AKİ) trend analizi yapılmıştır. Kuraklık trendlerinin ortaya çıkarılmasında Mann-Kendall (MK), Yenilikçi Şen Yöntemi (YŞY) ve Thiel Şen Yaklaşımı (TŞY) kullanılmıştır. İçsel bağımlılığı bulunan AKİ serilerine MK testi uygulanmadan önce trend-free pre-whitening (ön arındırma) işlemi ile serisel bağımlılığın etkisi kaldırılmıştır. Trend analizleri istatistiksel açıdan %1 ve %5 anlamlılık seviyelerine göre değerlendirilmiştir. Sonuç olarak hidrolojik kuraklık trendlerinin değişimini istenilen aralıkta grafiksel olarak göstermesi ve daha hassas olarak trendleri ifade edebilmesi açısından YŞY’nin MK testine göre üstün olduğu belirlenmiştir. Ayrıca çalışma alanında aylık ve yıllık zaman periyotlarında artan hidrolojik kuraklık trendlerinin hâkim olduğu tespit edilmiştir. Bu nedenle kuraklık risklerinin yönetilmesi, kuraklığa karşı alınabilecek önlemler ve erken uyarı sistemleri gibi aksiyonların bütünleşik bir şekilde uygulanması gerekmektedir.
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