Yazar "Yilmaz, Mustafa Utku" seçeneğine göre listele

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    A multi-step strategy for enhancing the rainfall-runoff modeling: combination of lumped and artificial intelligence-based hydrological models
    (Springer, 2025) Mohammadi, Babak; Safari, Mir Jafar Sadegh; Vaheddoost, Babak; Yilmaz, Mustafa Utku
    Accurate rainfall-runoff (RR) modeling holds significant importance in environmental management, playing a central role in understanding the dynamics of water cycle. In this respect, the precision in the determination of RR is crucial for mitigating the adverse effects of both water scarcity and excessive runoff, ensuring the sustainable management of ecosystems and water resources. As a primary hydrological variable, runoff engages in direct interactions with other hydrological variables. Due to the complexity of the RR process, two primary approaches are commonly used in modeling, namely conceptual (lumped) models and artificial intelligence (AI) models. Conceptual approaches are based on hydrological processes and use a larger number of hydrological variables, yet they often exhibit lower performance compared to AI models. In contrast, AI models rely on fewer parameters and lack physical interpretability, but demonstrate high performance. This study merges the advantages of both lumped and AI techniques to develop an advanced RR model. Hence, the applicability of several lumped and AI-based models in estimating the streamflow rates with the help of basic meteorological variables is investigated. The lumped hydrological models, namely the Modello Idrologico SemiDistribuito in continuo (MISD), Identification of Unit Hydrographs and Component Flows from Rainfall, Evaporation, and Streamflow (IHACRES), and G & eacute;nie Rural & agrave; 4 param & egrave;tres Journalier (GR4J), are employed in conjunction with AI algorithms as Radial Basis Function (RBF) neural networks, Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multilayer Perceptron (MLP). An ensemble of conceptual models (MISD, IHACRES, and GR4J) and three AI models (MLP, RBF, and ANFIS) with various lag times are considered as effective variables, where Support Vector Machine (SVM) was utilized as a feature selection method with five different kernels in determining the best inputs. Afterward, the SVM-ANFIS model, as the best model, is hybridized with Ant Colony Optimization (ACO) to develop the SVM-ANFIS-ACO model. It is found that the coupling of lumped and AI methodologies considerably enhanced the accuracy of the RR models; and SVM-ANFIS-ACO outperformed other models in streamflow computation.
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    Estimation of flow duration and mass flow curves in ungauged tributary streams
    (Elsevier Sci Ltd, 2023) Vaheddoost, Babak; Yilmaz, Mustafa Utku; Safari, Mir Jafar Sadegh
    The mastery in forecasting the streamflow rates is of great importance in the design, planning and resilience against droughts. Likewise, the application of flow duration and mass flow curves in the design of the reservoir capacity, energy generation, water allocation, etc. especially at the tributary reaches is a great challenge mostly due to the lack of information and data records. In this study, we have developed a methodology to obtain the flow duration curve (FDC) and mass flow curve (MFC) in tributary stream stations with the help of estimated streamflow rates. The procedure suggests using two alternative approaches in the selection of the reference station on the mainstream. The streamflow in the reference station is decomposed into direct runoff (DR) and base flow (BF) using one-parameter digital filter method. Together with the precipitation records in the tributary station, the DR and BF on the reference station are then used to estimate the FDC and MFC. The multivariate adaptive regression spline (MARS) and random forest (RF) methods are used to alternate each other, and the residual of the models are simulated using the autoregressive conditionally heteroscedastic (ARCH) approach to develop the hybrid MARS-ARCH and RF-ARCH models. A data set related to Coruh River Basin, in Turkey is used to confirm the methodology, while results with R2 >= 0.92, reasonable bias, and relative error in the estimation of the expected FDC and MFC rates indicated the robustness of the suggested methodology.
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    Mapping the Evolution of Sea Outfall Research: Insights for Marine Environmental Sustainability
    (2025) Yılmaz, Damla; Yilmaz, Hulya; Erdem, Elif Aybike; Yilmaz, Mustafa Utku; Vaheddoost, Babak; Aras, Egemen
    Effective management of sea outfall is a critical topic in marine environmental science, sustainability regulations, and adaptation for the road maps. Therefore, the development of sea outfall research is mapped in this study by addressing 248 articles published during 1970 – 2023. A detailed assessment of the field’s evolution is provided through a combination of bibliometric and content analysis. Network analysis techniques, including co-occurrence, co-authorship, citation, and bibliographic coupling, are used to identify dominant research topics, citation patterns, and productive authors and countries. Subsequently, content analysis is applied to investigate the attributes, research methods, and gaps in the existing research. A new and detailed classification of sea outfall research is obtained from the content analysis namely: field studies, laboratory experiments, and data analysis, with consideration to the methodologies applied in the studies (modelling, numerical analysis, experimental techniques, simulation, and field studies). The findings not only highlight the progression of sea outfall research but also offer new perspectives that could inform future research and strategic investments aimed at enhancing marine environmental sustainability and managmenet.
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    Mapping the evolution of sea outfall research: insights for marine environmental sustainability
    (Gazi Univ, 2025) Yilmaz, Damla; Yilmaz, Hulya; Erdem, Elif Aybike; Yilmaz, Mustafa Utku; Vaheddoost, Babak; Aras, Egemen
    Effective management of sea outfall is a critical topic in marine environmental science, sustainability regulations, and adaptation for the road maps. Therefore, the development of sea outfall research is mapped in this study by addressing 248 articles published during 1970 - 2023. A detailed assessment of the field's evolution is provided through a combination of bibliometric and content analysis. Network analysis techniques, including co-occurrence, co-authorship, citation, and bibliographic coupling, are used to identify dominant research topics, citation patterns, and productive authors and countries. Subsequently, content analysis is applied to investigate the attributes, research methods, and gaps in the existing research. A new and detailed classification of sea outfall research is obtained from the content analysis namely: field studies, laboratory experiments, and data analysis, with consideration to the methodologies applied in the studies (modelling, numerical analysis, experimental techniques, simulation, and field studies). The findings not only highlight the progression of sea outfall research but also offer new perspectives that could inform future research and strategic investments aimed at enhancing marine environmental sustainability and managmenet.
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    Rainfall-Runoff Simulation in Ungauged Tributary Streams Using Drainage Area Ratio-Based Multivariate Adaptive Regression Spline and Random Forest Hybrid Models
    (Springer Basel Ag, 2023) Vaheddoost, Babak; Safari, Mir Jafar Sadegh; Yilmaz, Mustafa Utku
    For various reasons, it is not always possible to obtain adequate and reliable long-term streamflow records in a river basin. It is known that streamflow records are even shorter when the stations located on tributary channels are of the interest. Hence, it is necessary to develop dependable streamflow estimation models for the tributary streams that play a key role in the micro-hydrology of the basin. In this study, rainfall-runoff models are developed to estimate the daily streamflow in ungauged tributary streams. Precipitation and streamflow in the most similar gauging station on the main channel and lagged values up to three days before on the same tributary station are used as the input variables of the allocated models. To select the most similar gauging station, a similarity index criterion is developed and used in the analysis. Then, two scenarios based on the streamflow or the corresponding set of direct runoff and base-flow in the same station are used. By applying multivariate adaptive regression spline (MARS) and random forest (RF) methods, several rainfall-runoff models are developed and evaluated based on determination coefficient, mean absolute percentage error, root mean square error, relative peak flow, scatter plot and time series plot. Alternatively, the MARS and RF models are combined with a drainage area ratio (DAR) model to produce the DAR-MARS and DAR-RF models. It is concluded that the direct runoff in the mainstream is more effective on the streamflow of the tributary station, while the integration of models with DAR enhanced the capabilities of the models in estimation of extreme values in the streamflow time series.