Sürdürülebilir Kalkınma Amaçları
Yayınlar
- ArticleYalnız künye2026Investigating the rubberized concrete-filled steel tube composite columns and developing artificial intelligence-based analytical models for ultimate axial strength prediction
Simsek, Oguz; Katipoğlu, Okan Mert; İpek, Süleyman; Güneyisi, Erhan; Güneyisi, Esra Mete
The application of rubberized concrete (RuC) in structural systems has led to the development of a novel composite system: the rubberized concrete-filled steel tube (RuCFST). The characteristics of infill materials, particularly RuC, significantly influence the structural behavior and performance of these composite systems. This study aims to evaluate the ultimate axial strength of RuCFST columns, considering rubber aggregate content up to 75% and infill concrete compressive strength up to 63 MPa, and propose artificial intelligence (AI)-based design models using deep learning (CNN, DNN, Autoencoder), machine learning (CatBoost), hybrid AI/neuro-fuzzy (ANFIS), and unsupervised neural network (SOM) to predict the ultimate axial strength. A robust dataset was compiled from the literature, comprising the results of 131 RuCFST column specimens. The experimental results underwent a comprehensive statistical assessment using various analytical methods, including Pearson correlation coefficient analysis, histogram analysis, distribution fitting curve analysis, and box-and-whisker plot analysis. Additional statistical metrics such as Kolmogorov–Smirnov, skewness, and kurtosis values were also used. Furthermore, by considering various variables covering the geometrical and material properties of the RuCFST columns, the design models were developed using AI techniques. The statistical evaluation results indicated that the compiled dataset is statistically significant and encompasses various input and output parameters. The design models developed in this study displayed varying prediction performance. However, those created using the Autoencoder and ANFIS methods showed statistically satisfactory performance in determining the load-carrying capacity of the columns.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2025Developing sediment concentration prediction in the Euphrates River catchment, Türkiye, with a honey badger and coati optimization-based hybrid algorithm
Saroughi, Mohsen; Katipoğlu, Okan Mert; Kartal, Veysi; Simsek, Oguz; Kilinc, Huseyin Cagan; Pande, Chaitanya Baliram
Estimation of sediment concentration (SC) is of vital importance in terms of siltation and economic life of dams, lakes and aqueducts, reservoir operations, design of water resource structures, monitoring and control of water pollution, and flood management. Direct measurement of SC is a challenging and expensive task. For these reasons, it was used to estimate the SC values at a station in the Euphrates River. New hybrid models were established by combining the CatBoost regressor (CBR) and artificial neural network (ANN) models with the honey badger optimization algorithm (HBA) and coati optimization algorithm (COA). The performance of the new model was compared with stand-alone model of ANN and CBR, and their accuracy was evaluated. In the setup of the models, 4 different input combinations of lagged sediment and discharge values for up to 3 months were evaluated. It is noteworthy that as the number of input variables, i.e., lagged data input, increases, the prediction accuracy of the models generally increases. HBA and COA algorithms often improve the accuracy of sediment prediction by optimizing the parameters of the single machine learning model. In addition, according to the AIC performance metric, the HBA algorithm is generally slightly better capable of optimization than the COA. The best model outputs were obtained according to the HBA-CBR hybrid approach of scenario 4 (RMSE = 59.78, AIC = 785.03, R2 = 0.32, PBIAS = 0.016, SI = 0.48, and MBE = − 2.05 in test phase), which consists of discharge and sediment with a delay of up to 3 months. The results of the study are valuable for decision-makers and planners in terms of practical reservoir and flood management and protection of coasts and river beds.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2025Daily prediction of Urmia Lake water level using remote sensing data and honey badger optimization-based data-driven models
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.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2024Analyzing the dynamical relationships between meteorological and hydrological drought of Wadi Mina basin, Algeria using a novel multiscale framework
Achite, Mohammed; Simsek, Oguz; Sankaran, Adarsh; Katipoğlu, Okan Mert; Caloiero, Tommaso
Understanding the relationship between hydrological and meteorological drought in drought-prone regions is critical for proper reservoir management. This study presents a novel multiscale framework for investigating the associations between hydrological and meteorological drought based on the Time-Dependent Intrinsic Correlation (TDIC) method. Firstly, the characteristics of short, medium and long term standardized precipitation index (SPI) and the standardized runoff index (SRI) of the Wadi Mina basin (Algeria) have been analyzed based on data from 6 rainfall and hydrometric stations. Then an Improved Complete Ensemble Empirical Mode Decomposition with adaptive noise (ICEEMDAN) method is used to decompose the most correlated SPI and SRI series to different scales. A stronger association between the two types of droughts is evident in the low-frequency trend component regardless of the station, but their evolution pattern does not remain the same. Subsequently, a TDIC based running correlation analysis is performed between the modes to examine the SPI–SRI associations over the time domain and across the time scales. TDIC analysis has proven the dynamic behavior in the SPI–SRI associations bearing frequent alterations in nature and strength across the process scales and along the time domain. In general, at the intra-annual scales the SPI–SRI correlations are mostly weak positive with localized alterations to negative along the time domain, whereas the relationship is dominantly strong positive and long range at inter-annual scales up to 4 years. This dynamic behavior in the SPI–SRI association and the evolution pattern of trend decipher that the rainfall processes are not directly transferred to streamflow drought, but it also gets controlled by many other local meteorological processes.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2024Prediction of groundwater drought based on hydro-meteorological insights via machine learning approaches
Kartal, Veysi; Katipoğlu, Okan Mert; Karakoyun, Erkan; Simsek, Oguz; Yavuz, Veysel Süleyman; Ariman, Sema
This study aims to predict groundwater drought-based meteorological drought index using machine learning instead of traditional approaches. Groundwater drought (GWD) was predicted using machine learning methodologies such as Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), Support Vector Machine (SVM), Random Forest (RF), Least Squares Boosting Tree (LSBT), Generalized Linear Regression (GLR) and k-Nearest Neighbours (KNN). In addition, monthly, seasonal, and annual drought indices such as the Standardised Precipitation-Evapotranspiration Index (SPEI), China Z Index (CZI), Standardised Precipitation Index (SPI), Z-Score Index (ZSI), Decile Index (DI), Percent of Normal Index (PNI) and Rainfall Anomaly Index (RAI) were used to analyse the drought of groundwater. These traditional drought indices were modified for the assessment of groundwater drought. Moreover, groundwater drought was predicted based on the hydro-meteorological parameters (temperature, relative humidity, wind speed, rainfall, groundwater level). The applied models’ performances were evaluated via Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Akaike Information Criterion (AIC), R-squared (R2), Mean Bias Error (MBE), Bias Factor, and Variance Account Factor (VAF). Linear SVM is generally the best model for predicting GWD, while GLR is the second-best performing model. The KNN algorithm obtained the weakest performances. Although all types of drought and wet categories were observed, normal drought occurred more than in the other drought and wet categories. This study can contribute to the assessment of groundwater drought in regions where there is no groundwater station.
RDL Kalıcı Kaydıİncele