Yazarlar
- Bayata, Halim Ferit(14)
- Bayrak, Osman Ünsal(10)
- Baş, Fatih İrfan(7)
- Çolak, Muhammed Ali(7)
- Katipoğlu, Okan Mert(6)
- Akar, Özlem(4)
- Atar, Muhammed(4)
- Cosgun, Suleyman Istemihan(4)
- Sarıçiçek, İnci(4)
- Özkan, Kemal(4)
- İnce, Elif Gökçe(4)
- Akar, Alper(3)
- Akar, Fahriye(3)
- Bas, Fatih Irfan(3)
- Demirci, Oğuz Kürşat(3)
- Demirel, Soner(3)
- Ince, Ozan(3)
- Kumbasaroglu, Atila(3)
- Mazlum, Yusuf(3)
- Nasery, Mohammad Manzoor(3)
- Pande, Chaitanya Baliram(3)
- Sarıgöl, Metin(3)
- Yazici, Ahmet(3)
- Yazıcı, Ahmet(3)
- Çelik, Alper(3)
- Aslan Yıldız, Özge(2)
- Bahadir, Murat(2)
- Büyüksaraç, Aydın(2)
- Cakil, Burak(2)
- Celik, Alper(2)
- Deniz, Fatih Ahmet(2)
- Eren, Erkin(2)
- Gökhan, Askan(2)
- Güncü, Arzu(2)
- Günes, Erkan(2)
- Hanakova, L.(2)
- Ince, Elif Gokce(2)
- Karakoç, Duygu Yıldız(2)
- Karsli, Fevzi(2)
- Keleş, Ömer Faruk(2)
Yayınlar
- ArticleAçık Erişim2025A Reinforcement Learning-Based Solution for the Capacitated Electric Vehicle Routing Problem from the Last-Mile Delivery Perspective
Aslan Yıldız, Özge; Sarıçiçek, İnci; Yazıcı, Ahmet
The growth of the urban population and the increase in e-commerce activities have resulted in challenges for last-mile delivery. On the other hand, electric vehicles (EVs) have been introduced to last-mile delivery as an alternative to fossil fuel vehicles. Electric vehicles (EVs) not only play a pivotal role in reducing greenhouse gas emissions and air pollution but also contribute significantly to the development of more energy-efficient and environmentally sustainable urban transportation systems. Within these dynamics, the Electric Vehicle Routing Problem (EVRP) has begun to replace the Vehicle Routing Problem (VRP) in last-mile delivery. While classic vehicle routing ignores fueling, both the location of charging stations and charging time should be included in the Electric Vehicle Routing Problem due to the long recharging time. This study addresses the Capacitated EVRP (CEVRP) with a novel Q-learning algorithm. Q-learning is a model-free reinforcement learning algorithm designed to maximize an agent’s cumulative reward over time by selecting optimal actions. Additionally, a new dataset is also published for the EVRP considering field constraints. For the design of the dataset, real geographical positions have been used, located in the province of Eskisehir, Türkiye. It also includes environmental information, such as streets, intersections, and traffic density, unlike classical EVRP datasets. Optimal solutions are obtained for each instance of the EVRP by using the mathematical model. The results of the proposed Q-learning algorithm are compared with the optimal solutions of the presented dataset. Test results show that the proposed algorithm provides remarkable advantages in obtaining routes in a shorter time for EVs.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2025Transportation Mode Selection Using Reinforcement Learning in Simulation of Urban Mobility
Taş, Mehmet Bilge Han; Özkan, Kemal; Sarıçiçek, İnci; Yazici, Ahmet
Transportation mode selection is pivotal for navigating through cities plagued by heavy traffic congestion. This plays a crucial role in ensuring the efficient utilization of time and resources to achieve the desired objectives. Given the complex dynamics of urban mobility, strategically selecting a transportation mode can significantly mitigate delays and enhance overall productivity in densely populated areas. The objective of this study is to find the most efficient result among various transportation modes to make deliveries from different points on a university campus. To solve this problem, reinforcement learning was used and tested on the simulation environment SUMO. Traffic density was increased by using an equal number of different transportation modes, such as driving, cycling, motorbiking, and walking. Various traffic densities were generated, and different reward models were applied to select the best means of transportation. Various probability distributions were used as reward models to avoid the unfair distribution caused by how near or how far the road is when moving from random points to the destination region. As a result of the models created using the applied reward–penalty functions, it was determined that the best means of transportation in areas with a low traffic density is cycling, and in areas with high traffic density, the optimal mode of transportation is motorbiking.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2025Traffic Impact Analysis of a Regional Shopping Center Using Microsimulation with the Analytical Hierarchy Process (AHP) Approach
Bayata, Halim Ferit; Baş, Fatih İrfan; Mengi, Gamze Şeyda
In today’s modern city life, shopping centers have become a part of the urban identity. Erzincanpark Shopping Center, which was put into service at the city’s western entrance in 2022 in Erzincan, created an additional traffic volume causing inadequacies and accidents in urban transportation. This study aimed to identify these problems and inadequacies and develop possible solutions and suggestions. To examine the importance of coordination between junctions, traffic flow, and phase arrangements, Erzincanpark was chosen as the study area, and the junctions on Halitpasa Street were examined. The study used the traffic impact analysis method to examine traffic counts at junctions, signal optimization, travel time, queue length, delay, CO2, NOx, VOC emissions, and fuel consumption values. The study results showed that Yildiz and Nedim Muratoglu junctions should be modern roundabouts to ensure the optimum situation. Furthermore, it was determined that there was no change in the parameters studied in the design of Ergan Junction in 3 different ways: signaled, 4-way (without an island), and modern roundabout. Analytical Hierarchy Process (AHP), one of the multi-criteria decision- approaches, was used to find the feasible result, considering safety and construction costs.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2025Transit passenger-oriented optimisation of arrival aircraft sequencing
Demirel, S.
This study presents a model that aims to optimise the sequencing arrival aircraft around the terminal manoeuvring area (TMA). The model considers the transit passenger counts of these aircraft and employs the point merge at Sabiha Gokcen Airport. In this study, aircraft were categorised into two groups, namely ‘High or Low Transit Passenger (HTP/LTP)’. Subsequently, multi-objective models were employed to solve the test problems. Weighted sum scalarisation (WSS), conic scalarization (CS), and epsilon constraint (EC) models were utilised to increase robustness and their results were compared with a single-objective optimisation model. This approach aims to provide decision-makers with a variety of outcomes, thus expanding their options. Simultaneously, efforts are made within the model to allow aircraft with HTP counts to have minimal delays. Additionally, emission calculations were conducted to offer a critical perspective on the environmental implications, and the delay results of the multiobjective optimisation (MOO) models underwent statistical analysis.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2025Explaining building energy efficiency prediction through architectural and engineering solutions considering environmental impacts using a hybrid model
Aslay, Semi Emrah
The aim of this study is to investigate building energy efficiency by integrating hybrid modelling approaches and interpretable models into architectural design processes and engineering solutions while considering environmental impacts. At the same time, it is aimed to achieve the most efficient building design possible. Separate analyses were carried out using a total of 6,913 data from Rutland (185) and Salford (6718) cities. The data were grouped into carbon emission information, architectural information, lighting information, personal heating information, and main heating system information to form a dataset. Light Gradient Boosting Machine (LightGBM) was preferred as the base model and Particle Swarm Optimisation (PSO) method was applied for hyperparameter optimisation. The hybrid model created in this way is called PSO-LightGBM. The optimization process was carried out using software in both R Studio and Python environments, utilizing seven different hyperparameters. Apart from the hybrid model used as a method, 2 different SHAP analyses, neural network based, and tree based, were performed to clearly explain the parameter relationships. The PSO-LightGBM hybrid model provided more successful predictions compared to the basic LightGBM model. While R2 values improved between 0.82 and 0.90 in the Rutland dataset, this value increased from 0.8687 to 0.8901 for test data and from 0.8538 to 0.9091 for training data in the Salford dataset. R2 values show an improvement of 7% in the Rutland dataset and maximum 6% in the Salford dataset. When the reduction in error rates is evaluated, it is found that the greatest improvement is in the Mean Squared Error (MSE) metric. MSE decreased by 17% in the Rutland dataset and by 4% in the Salford dataset. According to the SHAP Analysis results, CO2 emissions have the largest impact on energy consumption, while primary fuel types, number heated rooms and individual heating systems are other important parameters. While the tree-based SHAP model is more sensitive to physical parameters, the neural network model is more sensitive to indirect relationships. In both analyses, the communal heating system type has the lowest impact. In order to improve building energy efficiency, high efficiency individual boiler systems should be preferred, architectural approaches that optimise the number of heated rooms and smart heating solutions should be used, and central heating systems should be modernised. The results highlight the effectiveness of hybrid modelling approaches with SHAP analyses based on different baselines to ensure the integration of environmental impacts, architectural design processes and engineering solutions in terms of building energy efficiency. Furthermore, the findings contribute to the importance of interdisciplinary work in buildings to improve energy efficiency. Future studies can focus on the development of building energy performance prediction models that can be explained by integrating various learning algorithms and different optimisation techniques, using regions with high cooling demand, large datasets and different building types.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2025Predictive Modeling of Urban Travel Demand Using Neural Networks and Regression Analysis
Çolak, Muhammed Ali; Bayrak, Osman Ünsal
Urban transportation systems are increasingly strained by population growth, changing mobility patterns, and the need for sustainable infrastructure planning. The accurate modeling of urban trip generation is critical for effective and sustainable transportation planning, especially in the context of rapidly growing urban populations and evolving travel behaviors. This study investigated the application of advanced statistical methods and artificial intelligence-based techniques for forecasting urban travel demand. Erzincan, with a population of approximately 200,000, serves as a representative mid-sized city, offering valuable insights for transportation planning and traffic management. Data collected from various user groups, including households and university students, provide a comprehensive understanding of local travel behavior. Four predictive modeling techniques, linear regression, Poisson regression, negative binomial regression, and artificial neural networks (ANNs), were applied to the dataset, followed by a comparative performance evaluation. Additionally, a macro-level simulation was conducted using VISUM (Release 18.2.22) software to evaluate the current transportation network and assess the potential impacts of proposed improvement scenarios. The results show that the ANN model provided the highest predictive accuracy for household-based data (R2 = 0.62), while the linear regression model yielded the best results for dormitory-based data (R2 = 0.95). Furthermore, Poisson regression proved most effective in estimating the minimum trip generation time, which was estimated to be 22.77 min under simulated conditions. The study offers practical insights for transport planners and policymakers by demonstrating how predictive analytics and simulation tools can be integrated to address urban mobility challenges.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2025G-ABORE: regularizing complex geometry building boundaries from pre-processed building LiDAR points using Bézier curve
Karsli, Fevzi; Ozdemir, Emirhan; Bahadir, Murat
The increasing use of Light Detection and Ranging (LiDAR) point clouds in vector map production highlights the need for a generalized approach to represent complex (linear and curvilinear) building boundary geometries, considering both technological advancements and architectural diversity. In this paper, the aim is to automatically regularize the complex geometric building boundaries extracted from LiDAR point clouds. In order to achieve this aim, building corner points were first detected with the Automatic Building Outline Regularization (ABORE) method. Then, a distance-based statistical method was implemented to determine the edge types representing the building boundary. If the building edges are linear, ABORE corner points are used. Conversely, if they are curvilinear, the edges are regularized using Bézier curves based on Bernstein polynomials. Thus, by generalizing the proposed ABORE method for regularizing only linear building boundaries, a novel method called Generalized Automatic Building Outline Regularization (G-ABORE) is developed to regularize building boundaries in complex geometry. The proposed method was implemented on the open-source Toronto, São Paulo, Minnesota, and locally produced Ordu LiDAR datasets. Regarding accuracy assessments, the overall performance of the novel method for building boundary regularization is 98.76%, 98.87%, 98.11%, and 98.99% for Completeness (Cp), Correctness (Cr), Quality (Q), and F-score metrics, respectively. In addition, RMSE and PoLiS positional accuracy values were calculated as 0.27 m and 0.24 m, respectively. The results demonstrate that the G-ABORE method effectively regularizes complex building boundaries with high accuracy. As a result, it is proven that the developed method can regularize building layers with an accuracy suitable for representation in vector maps, regardless of the structure of building boundary geometries extracted from point cloud data in fields such as architectural design, urban planning, and more.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2025Appropriate Intersection Design Type Based on TOPSIS Multiple Criteria Decision Method with AIMSUN Mesoscopic Simulation
Bayata, Halim Ferit; Baş, Fatih İrfan; Demircioğlu, Muhammed Sami; Çolak, Muhammed Ali
The increasing world population has also increased the demand for vehicles and brought along various problems such as traffic delays, accidents, and air pollution. In addition, the increase in the number of vehicles causes an increase in the time spent at intersections, negatively affecting people’s quality of life, distracting drivers, and unsafe driving practices. Moreover, greenhouse gas emissions, including CO2 and NOx from vehicles, cause climate change and deterioration of air quality. To solve these problems, in this study, seven signalized intersections on a corridor in the city center of Erzincan province were examined. Using the Aimsun traffic simulation program, factors such as CO2, VOC, NOx emissions, queue length, delay time, travel time, and speed were evaluated. Using one of the multiple criteria decision methods, the TOPSIS method, the ideal scenario was determined based on the simulation results, considering the construction costs. The study revealed significant improvements in several parameters. CO2 emissions were reduced by 10%, delay time by 35%, VOC emissions by 14%, NOx emissions by 13%, queue length by 35%, delay by 38%, and travel by 19%. Furthermore, the speed was increased by 15%.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2025Examining the serial mediating role of emotional solidarity and perceived concern in the effect of residents' community commitment on support for tourism development
Apak, Ömer Ceyhun; Taş, Muhammed; Güneş, Erkan
This paper examines the serial mediating role of emotional solidarity and perceived concern in the impact of community commitment on support for tourism development. The study is designed within the framework of the relational survey model, which is one of the quantitative research designs. The data were collected from the residents of Bayburt (Türkiye), one of the destinations in the early stages of tourism development. The residents participating in the study were informed that it was voluntary. Thus, the study was conducted on a total of 423 samples. SPSS and PROCESS macros Model 6 were used to analyze the data in the study. As a result of the study, community commitment and emotional solidarity were positive, and perceived concern was negatively related to support for tourism development. In addition, it was determined that emotional solidarity and perceived concern significantly mediate the effect of community commitment on support for tourism development.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2025Investigation of the Impact of Intersection Designs, Signalization, and Directions of Intersection Arms on Microscopic Networks: The Case of Erzurum Province
Zirek, Zühal; Çolak, Muhammed Ali
Transportation has an important connection in many aspects such as economic growth, trade, tourism and social interaction. Today, the demand for transportation services is increasing with the rapid socio-economic development. This increase brings with it various traffic problems. Effective intersection design in preventing congestion, delays and accidents is of great importance in terms of ensuring traffic safety and sustainability. In this study, Gürcükapı Intersection and Taşhan Intersection were remodeled with Aimsun software in order to reduce the density at the intersections in Erzurum province and to meet traffic needs. Signalization was added to the modeled intersections, the directions of the intersection arms were changed and four alternative scenarios were created with these changes using current scenario. A total of five scenarios, including the current scenario and the developed alternatives, were evaluated and compared based on travel time, delay time, waiting time, speed, queuing, instant CO2, NOx, PM (Particulate Matter) and VOC (Volatile Organic Compounds) values. As a result of the comparison, the scenario in which the current intersection arm direction and Gürcükapı and Taşhan intersections are modernized and there is no signaling system was determined as the most effective scenario.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2025Investigation of the Required Earthquake Gap Distance between Reinforced Concrete Buildings with Nonlinear Time History Analysis; [Betonarme Binalar Arasında Bırakılması Gereken Derz Mesafesinin Doğrusal Olmayan Zaman Tanım Alanında Analizler ile İncelenmesi]
Cakil, Burak; Ince, Ozan; Atar, Muhammed; Ince, Elif Gokce
Earthquakes are caused by movements of the Earth's crust, resulting in destructive impacts on structures. One of the factors that increase the destructiveness of earthquakes is the pounding effect between buildings. Reinforced concrete buildings need to displace freely during seismic events. However, when the gap between adjacent buildings is insufficient, collisions occur, causing significant structural damage. This phenomenon, known as the pounding effect, can lead to severe damage during earthquakes. In this study, nonlinear time history analyses were conducted for two types of reinforced concrete buildings, 5-storey and 10-storey structures, to obtain their maximum displacement values. The minimum separation distances required to prevent pounding when these buildings are constructed adjacently were calculated and compared with the values specified in the 2018 Turkish Building Earthquake Code (TBEC-2018). The analysis results revealed that the distances calculated according to TBEC-2018 are insufficient.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2025Prediction of Pedestrian Traffic Accident Severity and Evaluation of Driver and Pedestrian Faults
Baş, Fatih İrfan
This study investigated pedestrians involved traffic accidents with the aim of classifying the severity of accidents based on the number of injured pedestrians using machine learning algorithms, including AdaBoost, Gradient Boosting, XGBoost, K-Nearest Neighbors (KNN), Logistic Regression, Support Vector Classifier, Decision Tree, and Random Forest. The Random Forest model was identified as the best model for classifying pedestrian-involved traffic accidents, achieving high predictive accuracy of 95%, an F1 score of 0.95, and demonstrating low error metrics. The research analyzed both driver and pedestrian faults, alongside factors such as the presence of pedestrian crossings, intersection type, driver age, time of day, month and seasonal variations. The results revealed that accidents at locations without intersections were primarily caused by driver faults, such as speeding, while pedestrian faults, such as crossing at unintended locations, also significantly contribute to the overall accident rate. The findings offered valuable insights into the characteristics of pedestrian accidents to improve traffic safety and reduce pedestrian injuries and fatalities.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2025Temporal and Spatial Analysis of Light Pollution Changes: A Case Study of Italy
Yılmaz, Abdulvahap
This study presents a comprehensive temporal and spatial analysis of light pollution in Italy using the VIIRS-DNB dataset, the Mann–Kendall Trend Model (MKTM), the Sen's Slope Estimator (SSE) method, Bortle classes, and the QGIS-Molusce plugin. In the entire data set analyzed, an increasing Artificial Light (AL) trend is observed in 68 Italian provinces and a decreasing AL trend in 40 provinces. Examining the distribution of light pollution across the country for 2022, Ogliastra and Rome show the lowest and highest values, respectively. Light pollution strongly correlates with the number of inhabited households (Formula presented.) and. Gross National Product (GNP) (Formula presented.). Many observatories are strongly affected by AL. Furthermore, between 2012 and 2022, the darkest sky quality categories, namely “Excellent sky (> 21.9 (Formula presented.)), Typical dark sky (21.9–21.5 (Formula presented.)), and Rural sky (21.5–21.3 (Formula presented.))” have completely disappeared. As a result, the Annual Rate of Change Percentage (ARC%) has been calculated as −10. The analysis of the transition matrix shows that the combined classes (22.02–20.8 (Formula presented.)) are mostly converted to suburban areas (20.8–20.1 (Formula presented.)), and this conversion rate is quite high (90.14%). The results show statistically reliable findings that will help national and local governments to tackle the problems more effectively.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2025Shear-wave velocity model of the Sivas City (inner eastern, Türkiye) using Rayleigh wave ellipticity inversion controlled by 2D microgravity modeling
Bektaş, Özcan; Büyüksaraç, Aydın; Sarıtepe, Halil Erdim; Önal, Kemal Mert; Canbaz, Oktay; Eyisüren, Onur; Pamuk, Eren; Akın, Özgenç; Akar, Fahriye; Koşaroğlu, Sinan
The change in duration, amplitude, and frequency content of the earthquake ground motion as it passes through the rock and ground environment is referred to as the local ground effect. Impedance differences between bedrock and soil, as well as the dynamic behavior of soils, can amplify this effect. The geometry of both dense and loose soil layers must be known to accurately define soil–structure interaction and properly assess how soil behavior affects a structure during an earthquake. Local ground effects are known to play a significant role in structural damage during earthquakes. In basin-like environments, however, studies based on foundation and sub-base depth often lack sufficient information, making it difficult to identify problems associated with basin effects. It is not appropriate to provide construction-related information, especially in environments with a basin structure like Sivas, without determining the bedrock or solid ground conditions. This study aimed at determining the bedrock/seismic foundation depth for the central settlement of Sivas and defining the basin structure, involved large-scale microgravity measurements. The study area was modeled in three dimensions using the gravity data obtained. Long-term microtremor measurements were also conducted, and one-dimensional depth–shear-wave (Vs) velocity models were generated using the Rayleigh ellipticity method. The bedrock/seismic foundation structure of the study area was defined using two different methods, and these definitions were combined into two-dimensional sections. A depth map of the study area was created, revealing that the thickness of the loose basin unit is approximately 90 m. Ambient noise was recorded at 35 points with a velocity seismometer, and S velocity (Vs) profiles were obtained from joint inversion of Rayleigh ellipticity data and dispersion curves from MASW and ReMi data. Furthermore, the Vs-depth structure of the basin was defined along the profiles cutting the basin in NW–SE and S–N directions, based on the Vs velocities in the 2D gravity model. The frequency range along these profiles was found to be 0.6 Hz in the deep parts of the basin and 2.5 Hz in the shallow parts.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2025A transformer-based deep reinforcement learning for the Dial-a-Ride problem
Aslan Yıldız, Özge; Sarıçiçek, İnci; Yazıcı, Ahmet
Dial-a-Ride problem (DARP) is a specialized variant of the vehicle routing problem that focuses on designing optimal routes for users who specify pick-up and delivery requests between origins and destinations. This problem involves determining the most effective and efficient routes using limited vehicles and resources to offer responsive door-to-door transportation services for users with specific needs. Logistics service providers require systems that can deliver optimal solutions to these combinatorial problems within a reasonable time. Recently, there has been a significant increase in the use of artificial intelligence optimization algorithms such as meta-heuristics or learning-based approaches to solve such problems. Among the learning-based approaches, reinforcement learning has gained prominence for routing and scheduling tasks, owing to its ability to adaptively learn from complex state spaces and dynamically changing environments. In this study, a novel transformer-based deep reinforcement learning method is proposed to solve the Dial and Ride problem for a single service vehicle. In the proposed model, we adopt a modified transformer architecture instead of employing traditional linear layers, we integrate convolutional layers. To validate our approach, we conduct comprehensive experiments comparing our method against four well-known metaheuristic algorithms and a Deep Q-Network algorithm. The results indicate that proposed approach outperforms these techniques in terms of shorter total travel distances. Additionally, the proposed method is tested on a real-world scenario generated in the Buyukdere neighborhood of Eskisehir. The results demonstrate that the proposed method makes it possible to solve the problem within a reasonable time. This study confirms that the proposed deep reinforcement learning method can effectively address the Dial-a-Ride Problems.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2025Predicting air quality index in başakşehir, istanbul with hybrid ai models: unveiling key drivers through catboost-based shap and feature importance analysis
Akiner, Muhammed Ernur; Katipoğlu, Okan Mert; Çintaş, Emre
Urban air quality influences public health, ecosystem sustainability and economic productivity. This study focuses on predicting the Air Quality Index (AQI) in Başakşehir, Istanbul. The study proposes a hybrid artificial intelligence (AI) model that amalgamates Categorical Boosting (CatBoost), Shapley Additive Explanations (SHAP) and the feature importance analysis. The dataset encompasses various meteorological parameters, including Tempmax, Tempmin, Temp, Dew, Humidity, Precip, Windspeed, Sea level pressure, Cloud cover, Solar radiation, Solar energy and UV index, in addition to air quality parameters such as PM10, SO2, CO, NO2, NOX, NO and O3. These variables serve as inputs for models like ANN, BAT-ANN,- BBO-ANN, GWO-ANN, HCA-ANN, CatBoost and CNN; the intent is to enhance the accuracy of the AQI prediction. When the combined set of variables were employed as inputs, the most precise results emerged from the CNN model, which yielded an RMSE of 1.43, an AIC of 949.21 and an NSE and R2 of 0.99. The CatBoost model exhibited exceptional performance among the various input combinations, providing the most accurate results for these configurations. Non-parametric statistical Friedman and Nemenyi post-hoc tests were used for multi-model comparison, and it was concluded that there were significant performance differences between the models used according to the p-value values, both in general and based on pairs. While prior studies have explored hybrid AI models for AQI prediction, this study uniquely integrates CatBoost and SHAP for enhanced explainability and model performance evaluation. SHAP analysis provided transparent insights into variable contributions; however, PM10 emerged as the dominant predictor, achieving the highest mutual information score of 6.88. These findings underscore the importance of integrating pollutant and meteorological data. The proposed methodology aligns with global sustainability goals, including SDG 11 (Sustainable Cities and Communities) and SDG 13 (Climate Action).
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2025Integrated Emission and Cost Analysis of Battery-Electric Vehicles up to 2035
Demirci, Oğuz Kürşat
This study quantifies when battery-electric vehicles (BEVs) reach total cost of ownership (TCO) parity with their internal-com-bustion counterparts and characterises the cradle-to-grave greenhouse-gas (GHG) intensity trajectory to 2035. Our contribution is a transparent, Python-based framework that integrates battery-cost learning, ownership economics and life-cycle impacts within a harmonised scenario set, and links them to policy timing, ISO 15118-20-ready bidirectional charging and power-system carbon intensity. Drawing on a systematic synthesis of 221 peer-reviewed sources (2013–2025), the model runs annually for a representa-tive C-segment BEV across three scenarios (Reference, Fast-Progress, Slow-Progress; 2024–2035). The results indicate that, under the median battery-pack price learning trajectory, BEV TCO falls below the ICE benchmark around 2029 (2028–2032 across sce-narios), while life-cycle GHG intensity declines from approximately 73 to 34 g CO2-eq km-1 by 2035, spanning 29–42 g km-1 depending on grid decarbonisation. Global sensitivity analysis identifies battery price as the principal driver of TCO outcomes and grid carbon intensity as the principal driver of emissions outcomes. Results are reported for three regional aggregates (OECD average, EU-27 and China), and the policy discussion highlights contrasts for the United States to contextualise cross-market dif-ferences. Policy alignment on three fronts—parity-linked purchase-incentive phase-outs, rapid deployment of ISO 15118-20-ready bidirectional charging, and stronger recycled-content targets—shortens time to cost competitiveness and amplifies the climate ben-efits of large-scale electrification.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2025Is Erzincan, Located on the North Anatolian Fault Zone, Which Produced the Biggest Earthquake in Turkey and the World, Ready for the Next Severe Earthquake?
Ulusu, İsmet
The main causes of damage include poor site selection, such as building on fault lines or on fill soil, as well as deficiencies in design, materials, and workmanship. Damage levels are also linked to the economic conditions of the region. In the 1939 earthquake, there were high casualties due to the magnitude of the earthquake, lack of engineering design in traditional structures and unsuitable soil conditions. Similarly, in the 1992 earthquake, unexpected damage occurred due to faulty designs created by inexperienced engineers who lacked sufficient knowledge of the seismic behavior of structures, errors in craftsmanship and workmanship, and unsuitable residential area selection for construction. These problems continue today and put most of the building stock at risk in case of a major earthquake. Seismic steel isolators are used in two new buildings in the city; if they are effective, they should be made mandatory in new construction. Otherwise, consideration should be given to relocating the city to the more stable southern rocky areas, which were unaffected in both 1939 and 1992.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2024Determination of Radiation Value by Month Using Artificial Neural Network Model; Ankara, Sivas, Erzurum example
Uzun, Sinem; Arslantaş, Hatice
This research examines the estimation of solar radiation using artificial neural network (ANN) models in Turkish cities with similar latitude values such as Ankara, Sivas and Erzurum. The aim of this study is to investigate whether cities at similar latitudes exhibit similar trends in solar radiation values, despite their geographical differences. In this study, solar radiation prediction was made for 3 cities with a single-layer neural network. Monthly solar radiation intensity was estimated for the 10-year period between 2012 and 2022 with a total of 4764 samples taken from the General Directorate of State Meteorology. An artificial neural network model was developed with 8 units in the first hidden layer and 4 units in the second hidden layer. The optimizer used in compiling the model was determined as Adam, the loss function as 'mean_squared_error' and the metric as 'mse'. ReLU activation function was used in the input layer and hidden layers. A 10-year solar radiation intensity value was used in the output layer. 70% of the data set is reserved for training and 30% for testing data set. As a result, similar solar radiation trends were obtained in the same latitude regions, the results were confirmed by meteorological data.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2024İmar Barışının Mera ve Yayla Alanlarındaki Yapılaşmaya Etkisinin İncelenmesi
Bayata, Halim Ferit; Akar, Alper; Akar, Özlem
Yayla ve meralar genellikle doğal yollarla oluşan bitki topluluklarıdır. Yapılan tahmini hesaplamalara göre, mera ekosistemleri dünya topraklarının yaklaşık %50' sini kapsamakta ve dünyadaki hayvanların yem ihtiyacının büyük bir kısmını karşılamaktadır. Yapılan çalışma ile, 2018 yılında yürürlüğe giren ve halk arasında imar barışı olarak adlandırılın 7143 sayılı yasanın, mera ve yayla alanlarında oluşabilecek kaçak yapılaşmaya olan etkisi araştırılmıştır. Çalışma alanı olarak Trabzon ili Akçaabat ilçesi sınırları içerisinde yer alan mera ve yaylalar seçilmiştir. Çalışmada Google Earth Pro programından elde edilen uydu görüntüleri ile Triplesat uydu görüntüleri kullanılarak zaman içerisinde meydana gelen kontrolsüz yapılaşma tespit edilmiştir. 1. çalışma bölgesinde 33 ve 2. çalışma bölgesinde 50 olmak üzere toplam 83 yeni yapının bu süre içerisinde yapıldığı belirlenmiştir. Elde edilen sonuçlar halen bu alanlarda kaçak yapılaşmanın devam ettiğini, denetlenme sürecinin hızlı ve etkili bir suretle yapılamadığını ve çıkarılan bu af niteliğindeki yasaların kaçak yapılaşmayı teşvik ettiğini göstermektedir.
RDL Kalıcı Kaydıİncele
