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Öğe A novel metaheuristic optimization and soft computing techniques for improved hydrological drought forecasting(Pergamon-Elsevier Science Ltd, 2024) Katipoglu, Okan Mert; Ertugay, Nese; Elshaboury, Nehal; Akturk, Gaye; Kartal, Veysi; Pande, Chaitanya BaliramDrought is one of the costliest natural disasters worldwide and weakens countries economically by causing negative impacts on hydropower and agricultural production. Therefore, it is necessary to create drought risk management plans by monitoring and predicting droughts. Various drought indicators have been developed to monitor droughts. This study aims to forecast Streamflow Drought Index (SDI) values with various novel metaheuristic optimization-based Artificial Neural Network (ANN) and deep learning models to predict 1-month lead-time hydrological droughts on 1, 3, and 12-month time scales in the Konya closed basin, one of the driest basins in Turkey. To achieve this goal, the ANN model was integrated with the Firefly Algorithm (FFA), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) techniques and compared against long shortterm memory (LSTM) networks. While establishing the SDI prediction model, lag values exceeding the 95% confidence intervals in the partial autocorrelation function graphs were used. Model performance was evaluated according to scatter matrix, radar, time series, bee swarm graphs, and statistical performance metrics. As a result of the analysis, the PSO-ANN hybrid model with (R2:0.468-0.931) at station 1611 and the FFA-ANN hybrid model with (R2:0.443-0.916) at station 1612 generally have the highest accuracy.Öğe Drought index time series forecasting via three-in-one machine learning concept for the Euphrates basin(Springer Heidelberg, 2024) Latifoglu, Levent; Bayram, Savas; Akturk, Gaye; Citakoglu, HaticeDroughts are among the most hazardous and costly natural disasters and are hard to quantify and characterize. Accurate drought forecasting reduces droughts' devastating economic effects on ecosystems and people. Eastern Anatolia is the largest and coldest geographical region of T & uuml;rkiye. Previous studies lack drought forecasting in the Eastern Anatolia (Upper Mesopotamia) Region, where agriculture is limited due to being under snow most of the year. This study focuses on the Euphrates basin, specifically the Tercan and the Tunceli meteorological stations of the Karasu River sub-basin, a vital Eastern Anatolia Region water resource. In this context, time series of 1-, 3-, 6-, 9-, and 12-month Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) values were created. The Tuned Q-factor Wavelet Transform (TQWT) method and Univariate Feature Ranking Using F-Tests (FSRFtest) were used for pre-processing and feature selection. Several models were created, such as stand-alone, hybrid, and tribrid. Machine Learning (ML) methods such as Artificial Neural Networks (ANN), Gaussian Process Regression (GPR), and Support Vector Machine (SVM) were conducted for the time series analyses. The GPR approach was concluded to perform better than the ANN and SVM at the Tercan station. In other words, GPR performs better in 80% of cases than SVM and ANN models. At the Tunceli station for the SPI output, SVM, which had a superior performance in 60% of the cases, demonstrated a performance comparable to GPR. At the same time, ANN once again exhibited an inferior performance. Similarly, for the SPEI output at the Tunceli station, no clear superiority was observed between the GPR and ANN methods. Because both methods were successful in 40% of cases. This study contributes by introducing a third concept to the stand-alone and hybrid model comparison of drought forecasting, adding tribrid models. It has been detected that the Hybrid and Tribrid ML methods lead to a 91% and 64% decrease relative root mean square error percentage compared stand-alone ML methods for SPEI and SPI in two stations. While the hybrid model at Tercan station was more successful in 80% of the cases, the hybrid model at Tercan station was more successful in 90% of the cases. While hybrid models were observed to be superior, tribrid models not only demonstrated performance close to the hybrid models but also provided advantages such as reducing computational load and shortening calculation time.Öğe Effects of Climate Change on Streamflow in the Ayazma River Basin in the Marmara Region of Turkey(Mdpi, 2023) Seddiqe, Khaja Haroon; Sediqi, Rahmatullah; Yildiz, Osman; Akturk, Gaye; Kostecki, Jakub; Gortych, MartaThis study investigates the effects of climate change on streamflow in the Ayazma river basin located in the Marmara region of Turkey using a hydrological model. Regional Climate Model (RCM) outputs from CNRM-CM5/RCA4, EC-EARTH/RACMO22E and NorESM1-M/HIRHAM5 with the RCP4.5 and RCP8.5 emission scenarios were utilized to drive the HBV-Light (Hydrologiska Byrans Vattenbalansavdelning) hydrological model. A trend analysis was performed with the Mann-Kendall trend test for precipitation and temperature projections. A meteorological drought assessment was presented using the Standardized Precipitation-Evapotranspiration Index (SPEI) method for the worst-case scenario (i.e., RCP8.5). The calibrated and validated hydrological model was used for streamflow simulations in the basin for the period 2022-2100. The selected climate models were found to produce high precipitation projections with positive anomalies ranging from 22 to 227 mm. The increase in annual mean temperatures reached up to 1.8 degrees C and 2.6 degrees C for the RCP4.5 and RCP8.5 scenarios, respectively. The trend results showed statistically insignificant upward and downward trends in precipitation and statistically significant upward trends in temperatures at 5% significance level for both RCP scenarios. It was shown that there is a significant increase in drought intensities and durations for SPEI greater than 6 months after mid- century. Streamflow simulations showed decreasing trends for both RCP scenarios due to upward trend in temperature and, hence, evapotranspiration. Streamflow peaks obtained with the RCP8.5 scenario were generally lower than those obtained with the RCP4.5 scenario. The mean values of the streamflow simulations from the CNRM-CM5/RCA4 and NorESM1-M/HIRHAM5 outputs were approximately 2 to 10% lower than the observation mean. On the other hand, the average value obtained from the EC-EARTH/RACMO 22E outputs was significantly higher than the observation average, up to 32%. The results of this study can be useful for evaluating the impact of climate change on streamflow and developing sustainable climate adaptation options in the Ayazma river basin.Öğe Investigating the climate change effects on annual average streamflows in the Euphrates-Tigris basin using the climate elasticity method(Gazi Univ, Fac Engineering Architecture, 2021) Alivi, Abdulrezzak; Yildiz, Osman; Akturk, GayeThis study was conducted to investigate the response of streamflows (Q) to changes in precipitation (P), potential evapotranspiration (E-p) and drought index within the Euphrates-Tigris basin. For this purpose, 37 sub-basins that are not affected by dams were identified within the basin. Here, the sensitivity of annual average streamflows to precipitation, E-p and drought index was evaluated by the climate elasticity method proposed by Schaake [1]. With this method, the average values of the precipitation and E-p sensitivity coefficients of the streamflow (epsilon(P) and epsilon(Ep), respectively) throughout the basin were calculated as 1.50 and -0.50, respectively. Therefore, it is observed that a 10% increase (decrease) in precipitation would lead to an average increase (decrease) of 15% in streamflow, on the other hand, a 10% increase (decrease) in Ep would result in an average decrease (increase) of 5% in streamflow across the basin. Moreover, the average value of sensitivity coefficient of streamflow to drought index (epsilon((sic))) was determined as -0.47, which means that a 10% increase in the drought index will result in an average decrease of 4.7% in streamflow within the basin. Additionally, it is observed that there is a nonlinear inverse correlation between the climate change sensitivity coefficients (i.e., epsilon(P), |epsilon(Ep)| and |epsilon((sic))|) and the flow coefficient (Q/P) values of the sub-basins indicating that the decrease in streamflow would increase the sensitivity of streamflow to climatic changes. Finally, it was determined that there exist relative increases in epsilon(P), |epsilon(Ep)| and |epsilon((sic))| values from high to low elevations across the basin.Öğe Meteorological Drought Analysis and Regional Frequency Analysis in the Kızılırmak Basin: Creating a Framework for Sustainable Water Resources Management(Mdpi, 2024) Akturk, Gaye; Citakoglu, Hatice; Demir, Vahdettin; Beden, NeslihanDrought research is needed to understand the complex nature of drought phenomena and to develop effective management and mitigation strategies accordingly. This study presents a comprehensive regional frequency analysis (RFA) of 12-month meteorological droughts in the K & imath;z & imath;l & imath;rmak Basin of Turkey using the L-moments approach. For this purpose, monthly precipitation data from 1960 to 2020 obtained from 22 meteorological stations in the basin are used. In the drought analysis, the Standard Precipitation Index (SPI), Z-Score Index (ZSI), China-Z Index (CZI) and Modified China-Z Index (MCZI), which are widely used precipitation-based indices in the literature, are employed. Here, the main objectives of this study are (i) to determine homogeneous regions based on drought, (ii) to identify the best-fit regional frequency distributions, (iii) to estimate the maximum drought intensities for return periods ranging from 5 to 1000 years, and (iv) to obtain drought maps for the selected return periods. The homogeneity test results show that the basin consists of a single homogeneous region according to the drought indices considered here. The best-fit regional frequency distributions for the selected drought indices are identified using L-moment ratio diagrams and ZDIST goodness-of-fit tests. According to the results, the best-fit regional distributions are the Pearson-Type 3 (PE3) for the SPI and ZSI, generalized extreme value (GEV) for the CZI, and generalized logistic distribution (GLO) for the MCZI. The drought maps obtained here can be utilized as a useful tool for estimating the probability of drought at any location across the basin, even without enough data for hydrological research.Öğe Modeling of irrigation water quality parameter (sodium adsorption ratio) using hybrid swarm intelligence-based neural networks in a semi-arid environment at SMBA dam, Algeria(Springer Wien, 2024) Achite, Mohammed; Katipoglu, Okan Mert; Elshaboury, Nehal; Kartal, Veysi; Akturk, Gaye; Ertugay, NeseSodium adsorption rate (SAR), which significantly affects soil and plant health, is determined according to the concentration of sodium ions, calcium, and magnesium in irrigation water. Accurate estimation of SAR values is vital for agricultural production and irrigation. In this study, hybrid swarm intelligence-based neural networks are used to model sodium adsorption ratio in irrigation water quality parameters in a semi-arid environment at Sidi M'Hamed Ben Aouda (SMBA) dam, Algeria. For this, the nature-inspired optimization techniques of particle swarm optimization (PSO), genetic algorithm (GA), Jaya algorithm (JA), artificial bee colony (ABC), and firefly algorithm (FFA) and the signal processing technique of variational mode decomposition (VMD) have been combined with artificial neural networks (ANN). Correlation matrices were used to select the data entry structure in the established models. Water quality parameters with a statistically significant and medium to high relationship with SAR values were presented as input to the model. The overall performance was measured using various statistical metrics, scatter diagrams, Taylor diagrams, correlograms, boxplots, and line plots. In addition, the effect of input parameters on model estimation was evaluated according to Sobol sensitivity analysis. As a result, the GA-ANN algorithm demonstrated superior performance (MSE = 0.073, MAE = 0.193, MAPE = 0.048, MBE=-0.16, R2 = 0.934, WI = 0.968, and KGE = 0.866) based on the statistical indicators, indicating better results compared to other models. The second-best model, ABC-ANN (MSE = 0.084, MAE = 0.233, MAPE = 0.066, MBE=-0.135, R2 = 0.897, WI = 0.965, and KGE = 0.920) was also selected. The weakest prediction outputs were obtained from the VMD-ANN model. The accurate and reliable estimation of SAR in irrigation water has the potential to facilitate improvements in agricultural irrigation management and agricultural production efficiency for farmers, agricultural practitioners, and policymakers.Öğe Suspended sediment load prediction in river systems via shuffled frog-leaping algorithm and neural network(Springer Heidelberg, 2024) Katipoglu, Okan Mert; Akturk, Gaye; Kilinc, Huseyin cagan; Terzioglu, Zeynep ozge; Keblouti, MehdiSuspended sediment load estimation is vital for the development of river initiatives, water resources management, the ecological health of rivers, determination of the economic life of dams and the quality of water resources. In this study, the potential of Feed Forward Neural Network (FFNN), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Shuffled Frog Leaping Algorithm (SFLA) models was evaluated for suspended sediment load (SSL) estimation in Ye & scedil;il & imath;rmak River. The heat map of Pearson correlation values of meteorological and hydrological parameters in 1973-2021, which significantly impacted SSL estimation, was examined to estimate SSL values. As a result of the analysis it was developed a prediction model with three different combinations of precipitation, stream flow and past SSL values (M1: streamflow, M2: streamflow and precipitation, M3: streamflow, precipitation, and SSL). The prediction accuracy of the models was visually compared with the Coefficient of Determination (R2), Bias Factor (BF), Mean Absolute Error (MAE), Mean Bias Error (MBE), Root Mean Square Error (RMSE), Akaike Information Criterion (AIC), Kling-Gupta Efficiency (KGE) statistical criteria and Bland-Altan plot, boxplot, scatter plot and line plot. Based on the analyses, the PSO-ANN model in the M1 model combination showed good estimation performance with an RMSE of 1739.92, MAE of 448.56, AIC of 1061.55, R2 of 0.96, MBE of 448.56, and BF of 0.29. Similarly, the SFLA-ANN model in the M2 model combination had an RMSE of 1819.58, MAE of 520.64, AIC of 1069.9, R2 of 0.96, MBE of 520.64, and BF of 0.19. In the M3 model combination, the SFLA-ANN model achieved an RMSE of 1423.09, MAE of 759.88, AIC of 1071.9, R2 of 0.81, MBE of 411.31, and BF of -0.77. Overall, these models can be considered good estimators as their predicted values are generally close to the measured values. The study outputs can help ensure water structures' effective lifespan and operation and take precautions against sediment-related disaster risks.
