Afet Yönetimi Enstitüsü

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http://hdl.handle.net/11527/25334

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  • Öge
    Data-driven modeling for the prediction of stack gas concentration in a coal-fired power plant in Türkiye
    (Springer, 2024) Mohammadi, Mandana ; Saloğlu, Didem ; Dertli, Halil ; Ghaffari-Moghaddam, Mansour ; Mohammadi, Mitra ; 0000-0002-1119-1047 ; 0000-0003-0503-056X ; 0000-0001-6498-7594 ; 0000-0002-2925-7286 ; 0000-0003-3231-0946 ; Afet ve Acil Durum Yönetimi Anabilim Dalı
    In this research, deep learning and machine learning methods were employed to forecast the levels of stack gas concentrations in a coal-fired power plant situated in Türkiye. Real-time data collected from continuous emission monitoring systems (CEMS) serves as the basis for the predictions. The dataset includes measurements of carbon monoxide (CO), sulfur dioxide (SO2), nitrogen oxides (NOx), oxygen (O2), and dust levels, along with temperatures recorded. For this analysis, deep learning methods such as multi-layer perceptron network (MLP) and long short-term memory (LSTM) models were used, while machine learning techniques included light gradient boosted machine (LightGBM) and stochastic gradient descent (SGD) models were applied. The accuracy of the models was determined by analysing their performance using mean absolute error (MAE), root means square error (RMSE), and R-squared values. Based on the results, LightGBM achieved the highest R-squared (0.85) for O2 predictions, highlighting its variance-capturing ability. LSTM excelled in NOx (R-squared 0.87) and SO2 (R-squared 0.85) prediction, while showing the top R-squared (0.67) for CO. Both LSTM and LGBM achieved R-squared values of 0.78 for dust levels, indicating strong variance explanation. Conclusively, our findings highlight LSTM as the most effective approach for stack gas concentration forecasting, closely followed by the good performance of LightGBM. The importance of these results lies in their potential to effectively manage emissions in coal-fired power plants, thereby improving both environmental and operational aspects.
  • Öge
    Discovering the perception differences of stakeholders on the sustainable and innovative stormwater management practices
    (Springer, 2024) Ekmekçioğlu, Ömer ; 0000-0002-7144-2338 ; Afet ve Acil Durum Yönetimi Anabilim Dalı
    The overarching aim of the present work is to explore the perception differences of stakeholders, i.e., municipalities (MN), water administrations (WS), non-governmental organizations (NGO), and universities (UN), playing vital roles in the decision mechanisms regarding one of the sustainable flood mitigation techniques, i.e., low impact development (LID) practices. As being rewarding alternative to conventional drainage techniques, four different LID strategies, i.e., green roof (GR), bioretention cells (BC), permeable pavement (PP), and infiltration trench (IT), and three of their combinations were adopted to the densely urbanized Ayamama River basin, Istanbul, Turkey. The performances of the LIDs were comprehensively evaluated based on three pillars of sustainability (i.e., social, economic, and environmental) using a hybrid multi-criteria decision-making (MCDM) framework containing the implementation of fuzzy analytical hierarchy process (fuzzy AHP) and the VIKOR (VIse KriterijumsaOptimiz acija I Kompromisno Resenje) for finding the weights of constraining criteria and prioritizing the LID scenarios, respectively. The major outcomes of this research showed that experts from MN, WS, and UN put forward the environmental dimension of sustainability, whereas respondents from NGO concentrated on the social aspect. Furthermore, MN and WS highlighted initial investment cost as the most determining criterion in optimal LID selection. On the other hand, criteria weights regarding the judgments of the experts attended from NGO revealed the significance of community resistance in specifying the optimal LID practices, while aesthetic appearance was the major concern of the academia. Hence, the present study, as an initial attempt, enabled critical standpoints for discovering perceptions of stakeholders.
  • Öge
    No more black-boxes : estimate deformation capacity of non-ductile RC shear walls based on generalized additive models
    (Springer, 2024) Değer, Zeynep Tuna ; Taşkın, Gülşen ; Wallace, John W. ; 0000-0003-3585-6477 ; 0000-0002-2294-4462 ; Deprem Mühendisliği Anabilim Dalı
    Machine learning techniques have gained attention in earthquake engineering for their accurate predictions, but their opaque black-box models create ambiguity in the decision-making process due to inherent complexity. To address this issue, numerous methods have been developed in the literature that attempt to elucidate and interpret black-box machine learning methods. However, many of these methods evaluate the decision-making processes of the relevant machine learning techniques based on their own criteria, leading to varying results across different approaches. Therefore, the critical significance of developing transparent and interpretable models, rather than describing black-box models, becomes particularly evident in fields such as earthquake engineering, where the interpretation of the physical implications of the problem holds paramount importance. Motivated by these considerations, this study aims to advance the field by developing a novel methodological approach that prioritizes transparency and interpretability in estimating the deformation capacity of non-ductile reinforced concrete shear walls based on an additive meta-model representation. Specifically, this model will leverage engineering knowledge to accurately predict the deformation capacity, utilizing a comprehensive dataset collected from various locations globally. Furthermore, the integration of uncertainty analysis within the proposed methodology facilitates a comprehensive investigation into the influence of individual shear wall variables and their interactions on deformation capacity, thereby enabling a detailed understanding of the relationship dynamics. The proposed model stands out by aligning with scientific knowledge, practicality, and interpretability without compromising its high level of accuracy.