Sürdürülebilir Kalkınma Amaçları
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- ArticleAçık Erişim2026Adaptive Weighting Ensemble Approach for High-Fidelity PM10 Forecasting via Evolutionary TCN and Bidirectional GRU
Kilinc, Huseyin Cagan; Dalkilic, Huseyin Yildirim; Yurtsever, Adem; Ates, Sinan; Yeşilyurt, Sefa Nur
Rapid socioeconomic development has intensified air pollution, making accurate air quality forecasting essential for effective pollution management and public health protection. However, short-term PM10 prediction remains challenging because of complex temporal dependencies, nonlinear pollutant–meteorological interactions, and rapidly changing pollution episodes. To address these challenges, this study proposes the AWE-Bi-GRU-EA-TCN-LR hybrid framework, where Bi-GRU captures bidirectional temporal dependencies, EA-TCN extracts multi-scale temporal patterns through enhanced attention, and Linear Regression (LR) statistically calibrates residual relationships. The framework is evaluated using hourly air quality and meteorological observations from an industrialized urban area. As a consequence of the estimate, the RMSE value of 17.740 obtained with the comparison model, the persistence model, was decreased to 12.228 in the proposed model, while the R2 values were obtained as 0.786 and 0.899 in the same models, respectively. A number of different models were used in this research for the purpose of predicting PM10, and the Enhanced Temporal Convolutional Network (EA-TCN) model was found to have a root mean square error (RMSE) value of 12.804. Consequently, this proposed synergistic model has the capability to generate accurate results while maintaining robustness in air quality forecasting using complex datasets in regions with different environmental characteristics and meteorological variables.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2025Model performance evaluation with Siamese neural network in hydrologic modelling
Dalkilic, Huseyin Yildirim; Baran, Ahmet; Yilmaz, Baris; Yildiz, Eyyup
Global warming impacts water resources, altering the hydrological cycle, availability, and quality, posing challenges for sustainable use. This study applied hydrological modelling to evaluate future scenarios in the Fırat Basin using flow data. Gated recurrent unit (GRU) and long short-term memory (LSTM) models were assessed using conventional performance indicators. A Siamese neural network (SNN) with graphical similarity rate (GSR) calculation was introduced to simplify evaluation and reduce parameter complexity. Results showed high model performance, with SNN providing statistically equivalent outcomes to traditional metrics, offering a novel approach to hydrological modelling challenges.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2023Comparison of different optimized machine learning algorithms for daily river flow forecasting
Samui, Pijush; Yesilyurt, Sefa Nur; Dalkilic, Huseyin Yildirim; Yaseen, Zaher Mundher; Roy, Sanjiban Sekhar; Kumar, Sanjay
River flow modeling is essential for critical aspects such as effective water management and structure planning, together with flood and drought analysis. There has been a growing interest in modeling hydrological systems via machine learning (ML) models. Various optimization techniques are utilized to develop the applications of ML-based hydrological models. The ultimate aim of this research is to establish high performance forecasting model. Therefore, this study conducts river flow modeling by using the daily data attained from a gauge station situated in the Euphrates Basin. For this purpose, Artificial Neural Network (ANN) model was hybridized with five different optimization algorithms i.e., Artificial Bee Colony (ABC), Teaching-Learning Based Optimization (TLBO), Ant Colony Optimization (ACO), Ant-Lion Optimization (ALO), and Imperialist Competitive Algorithm (ICA). In determining the inputs used to create the models, the distribution graph and correlation of the data with the previous period data were examined. The results were evaluated with eleven different statistical parameters and an error matrix. Examining the obtained results, the study revealed all models present high performance. When the results were reviewed in general, it was seen that all determination coefficient (R2) and Nash-Sutcliffe coefficient (NSE) values were higher than 0.962, and other parameters were very close to the optimum. By comparing all the developed hybrid models, ANN-ALO model reported the highest performance for river flow forecasting.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2023Application of Soft Computing Techniques in River Flow Modeling
Yesilyurt, Sefa Nur; Dalkilic, Huseyin Yildirim; Samui, Pijush
Modeling of data is critical in the analysis and evaluation of hydrological behavior. River flow data is one of the most important data in explaining hydrology. Management of water resources; It takes place in the literature as an area that needs to be investigated in order to provide early warning for undesirable situations such as floods and drought. For this reason, it is of important to develop different techniques for the estimation and modeling of river flow or to make comparisons between techniques. In this study, the flow data of fourteen stations located in the Euphrates-Tigris basin between 1981 and 2010 were used. Adaptive Network Based Fuzzy Inference Systems (ANFIS), Support Vector Machine (SVM) techniques that are frequently used in the literature, and newly introduced Gaussian Process Regression (GPR), Extreme Learning Machine (ELM) and Emotional Neural Network (ENN) artificial intelligence techniques are compared. In addition, considering all performance indices, it was determined which technique gave better results with rank analysis. Although all models worked well, it was seen that the methods were ranked as ELM, GPR, ENN, SVM and ANFIS starting from the best. This has shown that ELM, GPR and ENN methods, which have been used recently in flow modeling, give better results than traditional methods with complex structures. In addition, flow values were used in the whole study and these values were examined in 3 different combinations. It was seen that the model structure that gave the best performance was the model structure that used the flow data from one, two and three days ago as an estimator. The results were analyzed with a Taylor diagram and time series graphs.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2021Evaluation of combined use of drought indices in the case of Konya Closed Basin
Dalkilic, Huseyin Yildirim; Bayçinar, Metin; Samui, Pijush
The climate covers a series of events that deeply affect human life. Today, one of the most effective of these is climate change. As drought is cited as the most important one of the consequences of climate change, it is important to examine it with various. In this study, the drought status of the study area are located statistically calculated and evaluated 4 drought indices. At the same time, factor analysis and validity-reliability analysis were conducted to test the computability of the indices used in the study as a single index and to determine the reliability of the operations. While using exploratory factor analysis, Kaiser-Meyer-Olkin (KMO) test and Bartlett test for factor analysis; Cronbach's alpha coefficient was used for reliability analysis. In our study, K-means cluster analysis method was performed to determine the cut-off values of indices. According to the cluster analysis, new clusters were created and ANOVA test was conducted to determine the difference between clusters.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2020Evaporation modelling using soft computing techniques
Dalkilic, Huseyin Yildirim
Evaporation, which is one of the most important components of the hydrological cycle, is of great importance for developing, planning, operating, and managing water resources. In the present study, the average weekly evaporation and other hydrometeorological data measured by Manasgoan [1] between 1990 and 2004 were modelled using extreme learning machine (ELM), minimax probability machine regression (MPMR), and Gaussian process regression (GPR) methods. Wind speed, air temperature, relative humidity, and the number of sunshine hours were used as model input, and evaporation was the output. The correlation coefficient, mean absolute error (MAE), root mean square error (RMSE), and performance index were used as performance criteria in the evaluation of the model results. The model results indicated that the Gaussian process regression (GPR) model is more accurate and provides more successful results compared to other methods.
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