Tanas Savaş, Gülçin; Ertugay, Neşe; Katipoğlu, Okan Mert

Global climate change is reshaping basin hydrology by altering precipitation timing, evaporative demand, runoff generation, and groundwater recharge, which makes long-term streamflow trend detection essential for sustainable water resources management and climate adaptation. In this study, monthly, seasonal, and annual streamflow trends from three gauging stations in the upper Euphrates basin (D21A001, E21A019, and E21A051) were evaluated using an integrated non-parametric framework that combines Mann–Kendall (MK), Spearman’s rank correlation (SRC), Sequential Mann–Kendall (SMK), innovative trend analysis (ITA), and ITA slope (ITAS). This multi-method design allowed consistent identification of trend direction, magnitude, and temporal transition behavior, while also improving interpretive robustness across stations with different streamflow characteristics. The results show a clear common signal: streamflow weakening is most pronounced in the spring to early summer period, although the strength and timing vary by station. E21A019 shows a relatively coherent declining trend across methods, particularly during April–June and at seasonal and annual scales, indicating a stronger tendency toward streamflow reduction compared to the other stations. E21A051 shows a seasonally contrasting regime, with increasing tendencies in winter and late summer/early autumn (notably January–February and August–September), but decreasing trends during spring and early summer, implying intra-annual redistribution rather than uniform change. D21A001 presents weaker trend magnitudes overall, yet decreasing tendencies are still evident in late spring, early summer, and autumn. Across methods, MK and SRC consistently support the trend directions, while ITA/ITAS better highlight magnitude and seasonal contrast. Overall, the findings provide robust evidence of long-term streamflow change and hydrological pressure in the upper Euphrates basin. They can support adaptive and transboundary water management planning, while attribution to specific climatic or anthropogenic drivers requires direct analysis of the relevant forcing variables.

Saroughi, Mohsen; Katipoğlu, Okan Mert; Aktürk, Gaye; Gul, Enes; Simsek, Oguz; Citakoglu, Hatice

Artificial neural networks (ANNs), support vector regression (SVR) and CatBoost regression (CBR) machine learning methods have been combined with the honey badger optimization algorithm (HBA) and metaheuristic optimization algorithm to accurately and reliably predict lake water level (LWL), which is of great importance for the management and planning of water resources. In this study, meteorological and hydrological parameters, including temperature (T), precipitation (P), date (D), surface soil moisture (SSW), root zone moisture (RZW) and water level (WL), were employed as input data for predicting the LWL of Urmia Lake. The input data were employed to develop six different prediction scenarios. This study not only examined the impact of meteorological and hydrological parameters on LWL prediction but also compared the performance of individual models and hybrid models. The Akaike information criterion (AIC) index was used to ascertain the optimal machine learning model and to evaluate the six prediction scenarios. The results of the study indicate that, according to the AIC index, the data regarding the water level (WL) were significant in the prediction models. However, it should be noted that satisfactory results could also be obtained without using the WL data in certain scenarios. In scenario 4 (input data: D, T, P, SSW, RZW), where the WL variable was not included, the HBA-CBR hybrid model was the best model with the lowest AIC value (Train: -63,735, Test:-4693). In prediction scenario 6 (input data: D, T, P, SSW, RZW, WL), which included the WL data, the HBA-SVR hybrid model demonstrated high performance with the lowest AIC value (Train: -102,358, Test:-27,233). Accordingly, it was recommended to use lagged WL values as input in WL prediction because the prediction accuracy of the models significantly improved. Furthermore, hybrid models were found to perform better than individual models due to their more consistent results.

Bektaş, Özcan; Büyüksaraç, Aydın; Sarıtepe, Halil Erdim; Önal, Kemal Mert; Canbaz, Oktay; Eyisüren, Onur; Pamuk, Eren; Akın, Özgenç; Akar, Fahriye; Koşaroğlu, Sinan

The change in duration, amplitude, and frequency content of the earthquake ground motion as it passes through the rock and ground environment is referred to as the local ground effect. Impedance differences between bedrock and soil, as well as the dynamic behavior of soils, can amplify this effect. The geometry of both dense and loose soil layers must be known to accurately define soil–structure interaction and properly assess how soil behavior affects a structure during an earthquake. Local ground effects are known to play a significant role in structural damage during earthquakes. In basin-like environments, however, studies based on foundation and sub-base depth often lack sufficient information, making it difficult to identify problems associated with basin effects. It is not appropriate to provide construction-related information, especially in environments with a basin structure like Sivas, without determining the bedrock or solid ground conditions. This study aimed at determining the bedrock/seismic foundation depth for the central settlement of Sivas and defining the basin structure, involved large-scale microgravity measurements. The study area was modeled in three dimensions using the gravity data obtained. Long-term microtremor measurements were also conducted, and one-dimensional depth–shear-wave (Vs) velocity models were generated using the Rayleigh ellipticity method. The bedrock/seismic foundation structure of the study area was defined using two different methods, and these definitions were combined into two-dimensional sections. A depth map of the study area was created, revealing that the thickness of the loose basin unit is approximately 90 m. Ambient noise was recorded at 35 points with a velocity seismometer, and S velocity (Vs) profiles were obtained from joint inversion of Rayleigh ellipticity data and dispersion curves from MASW and ReMi data. Furthermore, the Vs-depth structure of the basin was defined along the profiles cutting the basin in NW–SE and S–N directions, based on the Vs velocities in the 2D gravity model. The frequency range along these profiles was found to be 0.6 Hz in the deep parts of the basin and 2.5 Hz in the shallow parts.

Akar, Fahriye; Hoskan, Nihan

The Erzincan Basin is one of the most actively deforming geologic features in Eastern Anatolia owing to the high seismic activity taking place in the surrounding area. In the last century, the 1939 Mw = 7.9 and the 1992 Mw = 6.8 earthquakes are the two largest events that devastated the city of Erzincan caused the loss of more than 33,000 and 600 lives, respectively. This study analyzes the seismicity in the vicinity of the Basin. The main trace of the North Anatolian Fault Zone (NAFZ) crossing the Erzincan Basin reveals relatively lower seismicity pattern in terms of small earthquakes. On the contrary, remarkable off-fault seismic activity occurs along the secondary segments. The segments located to the south of NAFZ are striking at high angles to the main trace. The potential of the secondary faults to generate Mw = 6.0 earthquakes was revealed by the 2003 Pülümür earthquakes, pointing out their significant contribution in shaping the Erzincan Basin. The fault segment ruptured by the Pülümür event is located to the east of the subparallel Ovacık fault showing left-lateral strike-slip sense of motion. Similar seismic lineaments exist to the west of the Ovacık fault. All these secondary segments are laying to the south of the NAFZ main trace. In this study, the stress tensor analysis of the Erzincan Basin was conducted using the azimuths and plunges of the P- and T-axes obtained from the focal mechanism solutions of the earthquakes that occurred in the region. The focal mechanisms of the events around those segments have P- and T-axes oriented close to N–S and E–W, respectively. On the contrary, to the north of NAFZ prominent seismic activity takes place only along a segment striking almost E–W accommodating events mostly showing right-lateral strike-slip faulting. These facts indicate a complex stress field acting around the Erzincan Basin.

Kilinc, Huseyin Cagan; Apak, Sina; Ergin, Mahmut Esad; Ozkan, Furkan; Katipoğlu, Okan Mert; Yurtsever, Adem

Hydrological time series forecasting often relies on addressing the inherent uncertainties and complex temporal dependencies embedded in the data. This study presents an innovative hybrid framework, the Bayesian-ConvLSTM-PSO model, specifically designed to tackle these challenges. The framework synergistically combines 1D convolutional neural networks (CNNs), a convolutional Bayesian network, multi-head attention, and long short-term memory (LSTM) networks, with parameters optimized through particle swarm optimization (PSO). The fusion of the convolutional Bayesian network and 1D convolutional neural networks enhances feature robustness by capturing both probabilistic uncertainties and spatial patterns effectively. The multi-head attention model further amplifies this by focusing on the most relevant features, improving the learning process and ensuring better representation of complex temporal dependencies. The proposed model is rigorously tested on daily streamflow data from three flow measurement stations (FMS): Ahullu (D14A014), Kızıllı (D14A080), and Erenkaya (D14A127). Experimental results reveal that the Bayesian-ConvLSTM-PSO model achieves significant performance gains across various evaluation metrics, including root mean square error (RMSE), mean absolute error (MAE), determination coefficient (R2), Kling–Gupta efficiency (KGE), and bias factor (BF). Notably, the model demonstrates exceptional accuracy with an R2 of 0.9950, a KGE of 0.9950, and a bias factor of 0.0003, surpassing the results of PSO-1D CNN-LSTM and benchmark models, such as DNN, DNN-LSTM, and 1D ConvLSTM. These compelling findings underscore the potential of the Bayesian-ConvLSTM-PSO framework as a robust and effective tool for applications in river engineering and hydrological time series forecasting.

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.

Akoğuz, Harun

Seepage control is critically essential for dams, slopes, and the stability of many engineering structures, developing over many years using different methods. This study presents a novel approach for seepage control using the photopolymerization technique to form an impermeable crust at the soil surface. Effects of exposure duration (5, 10, 15, and 30 min) and light intensities (140, 550, 710, and 1000 W/m2) on the seepage control effectiveness of four different sand soils were investigated by using the photopolymerization technique. The three-point bending, permeability and acid rain simulation tests were performed to illustrate the effect of photopolymerization on the seepage control of sand soils. Furthermore, the influence of photopolymerization on the microstructure of soils was examined with the aid of SEM analysis. The results indicated that the photopolymerization technique could improve the crust strength, acid rain durability, and permeability of sand soils. The crusts at the surface layer of soils were formed after the photopolymer was applied to the sand surface and then exposed to UV light. These crusts of different soils have bending strengths and thicknesses in the range of 34.57 and 68.07 MPa, 2.13 and 6.18 mm, respectively. The increase in exposure duration and light intensity, resulted in the gradual increment of soil crust thickness. On the other hand, the increase in light intensities have more effective on the crust thickness. The formed soil crust has been provided an impermeable surface that prevents the weight loose from the surface of the soil under acid rain conditions. The SEM analysis indicated that the photopolymerized gels were homogeneously distributed between the grains in all samples, although the grain distribution of soils were different. Since the photopolymerization method has a short treatment duration and is easier to apply on the sand surface, the occurrence of the crust at the soil surface by this technique may open a new and improvable path for seepage control. The obtained results showed that the photopolymerization method is promising for repairing, restoring, and strengthening the construction materials in different engineering areas, as well as seepage control.

Kilinc, Huseyin Cagan; Haznedar, Bulent; Katipoğlu, Okan Mert; Ozkan, Furkan

The management of water resources and the modeling of river flow have a prominent position within environmental research. They form a critical bridge between human societies and the delicate ecosystems they inhabit. Scholars have focused on benefiting more efficient methods based on the use of artificial intelligence for river flow forecasting, notably because modeling hydrological systems is quite challenging. This study primarily centered on exploring the predictive capacities of hybrid models in establishing a link between daily flow data and prospective data. In the study, the mentioned algorithms, firefly algorithm (FFA), artificial bee colony (ABC), genetic algorithm (GA), were hybridized with the artificial neural network (ANN) model and data analyzes were examined with the stations in the Konya Closed Basin. A comparative analysis of FFA–ANN, GA–ANN, ABC–ANN, and long short-term memory (LSTM) models was conducted for daily flow forecasting for daily flow forecasting according to a range of graphical and statistical metrics. The outcomes indicate that the FFA–ANN hybrid model generally performed better than other models and the deep learning algorithm.

Kilinc, Huseyin Cagan; Haznedar, Bulent; Ozkan, Furkan; Katipoğlu, Okan Mert

Streamflow estimation is necessary to develop sustainable water management strategies that balance the needs of various water users while protecting the ecological health of the watershed. This study presents a comprehensive analysis of river flow data in Turkey's Meriç basin using long-term daily data spanning the years 2001–2011. Within the scope of forecasting of daily river flows, 80% of dataset was used for training stage and the 20% of dataset was used for testing. The performance of four different models, including Extreme Gradient Boosting (XGBoost) benchmark model, Linear Regression, Long Short-Term Memory Network (LSTM), and proposed Particle Swarm Optimization (PSO)—XGBoost hybrid model, was evaluated using several statistical evaluation criteria such as RMSE, MAPE, MAE, SD, NSE, KGE, and model success was assessed based on determination coefficient (R2) values. The forecasting results of the models were evaluated with statistical metrics. The proposed PSO-XGBoost model outperformed all models with R2 values of 0.7460 at the Babaeski FMS station, 0.9582 at the Meriç FMS station, and 0.8116 at the Hayrabolu FMS station, respectively. It has been found that when the parameters of the XGBoost model are optimized with the meta-heuristic PSO algorithm, the daily flow prediction accuracy is significantly increased in the Meriç River basin, which has a pluviozonal River regime. The results demonstrate that the proposed hybrid model produces precise and reliable estimations for daily river flow analysis in the Meriç basin. The findings derived from the proposed model demonstrate its promising potential for water resource management and decision-making in the region.

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.