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Öğe A Multi-Criteria Forest Fire Danger Assessment System on GIS Using Literature-Based Model and Analytical Hierarchy Process Model for Mediterranean Coast of Manavgat, Türkiye(MDPI, 2025) Ersoy, Izzet; Unsal, Emre; Gursoy, OnderForest fires pose significant environmental and economic risks, particularly in fire-prone regions like the Mediterranean coast of T & uuml;rkiye. This study presents a comprehensive Forest Fire Danger Assessment System (FoFiDAS), by integrating Geographic Information Systems (GIS), a literature-based model, the Analytical Hierarchy Process (AHP), and machine learning (ML) to improve forest fire danger classification. Both models integrate 13 key parameters identified through the literature. A comparison of these models revealed 53% overlap in fire danger classifications. While the AHP model, based on expert-weighted assessment, provided a more structured and localized classification, the literature-based model relied on broader scientific data but lacked adaptability. Pearson correlation analysis demonstrated a strong correlation between fire danger classifications and historical fire occurrences, with correlation scores of 0.927 (AHP) and 0.939 (literature-based). Further ROC analysis confirmed the predictive performance of both models, yielding AUC values of 0.91 and 0.9121 for the literature-based and AHP models, respectively. Five ML algorithms were used to validate classification performances, with Artificial Neural Network (ANN) achieving the highest accuracy (86.5%). The accuracy of the ANN algorithm exceeded 0.93 for each danger class, and the F1-Score was above 0.85. FoFiDAS offers a reliable tool for fire danger assessment, supporting early intervention and decision making.Öğe A Novel Voltage-Current Characteristic Model for Understanding of Electric Arc Furnace Behavior Using Experimental Data and Grey Wolf Optimization Algorithm(MDPI, 2025) Seker, Mustafa; Unsal, Emre; Aksoz, Ahmet; Dursun, MahirThe control of nonlinear systems cannot be effectively achieved using linear mathematical methods. This paper introduces a novel mathematical model to characterize the voltage-current (V-I) characteristics of the electric arc furnace (EAF) melting process, incorporating experimental field data for validation. The proposed model integrates polynomial curve fitting, the modified Heidler function, and double S-curves, with the grey wolf optimization (GWO) algorithm applied for parameter optimization, enhancing accuracy in predicting arc dynamics. The performance of the model is compared against the exponential, hyperbolic, exponential-hyperbolic, and nonlinear resistance models, as well as real-time measurement data, to assess its effectiveness. The results show that the proposed model significantly reduces voltage and current harmonic distortion compared to existing models. Specifically, the total harmonic distortion (THD) for voltage is reduced to 2.34%, closely matching the real-time measured value of 2.30%. Similarly, in the current spectrum, the proposed model achieves a significant reduction in third harmonic distortion and a THD of 11.40%, compared to 13.76% in real-time measurements. Consequently, a more precise characterization of the EAF behavior enables more effective mitigation of harmonics and vibrations, enhancing the stability and power quality of the electrical networks to which they are connected.Öğe Data Imputation on Lost IoT Data(Institute of Electrical and Electronics Engineers Inc., 2023) Kurtulmuslu, Resul; Unsal, EmreThe amount of data resulting from IoT-based industrial applications is growing rapidly nowadays. However, due to failures and communication breakdowns in IoT devices, noise, uncertainty and incompleteness may occur in the collected data. This issue has become a critical issue for data generation, quality, processing and analysis. Incomplete or inaccurate heat meter data in central heat cost sharing system, which has an important place in energy efficiency, is one of the biggest problems in making a fair share. In this study, based on the heat meter data retrieved from Pro Tek Energy Services company serving in Sivas, 8 different machine learning algorithms including Linear Regression, Polynomial Regression, K-Nearest Neighborhood, Support Vector Machines, Random Forest, Decision Tree, Adaboost and Multilayer Perceptron were used to predict the average daily outdoor temperature and independent unit of energy consumption. As a result of the experimental studies of these algorithms were evaluated based on R2, RMSE and MAPE metrics. It is observed that the performance of different algorithms stands out in different data sets. © 2023 IEEE.Öğe Development of an Internet of Things-Based Ultra-Pure Water Quality Monitoring System(MDPI, 2025) Ozturk, Mehmet Akif; Unsal, Emre; Yelkuvan, Ahmet FiratMonitoring ultra-pure water quality is crucial in dialysis centers and medical laboratories as even minor impurities can significantly impact health and diagnostic accuracy. In addition, the semiconductor industry needs and uses a significant amount of ultra-pure water. This study introduces an Internet of Things-based system for real-time monitoring and analysis of ultra-pure water conductivity, temperature, and other key parameters. The proposed system integrates a high-precision conductivity sensor, an ESP32 microcontroller, and a web-based interface to enable remote data access and visualization. Data transmission is through wireless communication, and values are stored on a web-based server for long-term analysis. Rigorous tests conducted at Sivas Numune Hospital validated the system's reliability, accuracy, and ability to maintain stringent ultra-pure water quality standards. This robust and cost-effective monitoring solution addresses the limitations of conventional systems and provides real-time insights, ensuring consistent water quality for sensitive medical applications.
