One of the solutions to reduce environmental emissions is related to the deployment of electric vehicles (EVs) with sustainable energy. In order to be able to increase the number of electric vehicles in circulation, it is important to implement optimal planning and design of the infrastructure, with particular reference to areas equipped with charging stations. The suitable analysis of the location of current electric vehicle charging stations (EVCSs) is the central theme of this document. The research focused on the actual location of the charging stations of five major EVCS companies in the province by selecting Istanbul as the study area. The study was conducted through a three-step approach and specifically (i) the application of the analytical hierarchy process (AHP) method for creating the weights of the 6 main and 18 secondary criteria that influence the location of EVCSs; (ii) a geospatial analysis using GIS considering each criterion and developing the suitability map for the locations of EVCSs, and (iii) application of the technique for order preference by similarity to ideal solution (TOPSIS) to evaluate the location performance of current EVCSs. The results show that the ratio between the most suitable and unsuitable areas for the location of EVCSs in Istanbul and the study area is about 5% and 4%, respectively. The results achieved means of improving sustainable urban planning and laying the basis for an assessment of other areas where EVCSs could be placed.

The liquid limit (LL) is considered the most fundamental parameter in soil mechanics for the design and analysis of geotechnical systems. According to the literature, the LL is governed by different particle sizes such as sand content (S), clay content (C), and silt content (M). However, conventional methods do not incorporate the effect of all the influencing factors because traditional methods utilize material passing through a # 40 sieve for LL determination (LL₄₀), which may contain a substantial number of coarse particles. Therefore, recent advancements suggest that the LL must be determined using material passing from a # 200 sieve. However, determining the liquid limit using # 200 sieve material, referred to as LL₂₀₀ in the laboratory, is a time-consuming and difficult task. In this regard, artificial-intelligence-based techniques are considered the most reliable and robust solutions to such issues. Previous studies have adopted experimental routes to determine LL₂₀₀ and no such attempt has been made to propose empirical correlation for LL₂₀₀ determination based on influencing factors such as S, C, M, and LL₄₀. Therefore, this study presents a novel prediction model for the liquid limit based on soil particle sizes smaller than 0.075 mm (# 200 sieve) using gene expression programming (GEP). Laboratory experimental data were utilized to develop a prediction model. The results indicate that the proposed model satisfies all the acceptance requirements of artificial-intelligence-based prediction models in terms of statistical checks such as the correlation coefficient (R²), root-mean-square error (RMSE), mean absolute error (MAE), and relatively squared error (RSE) with minimal error. Sensitivity and parametric studies were also conducted to assess the importance of the individual parameters involved in developing the model. It was observed that LL₄₀ is the most significant parameter, followed by C, M, and S, with sensitivity values of 0.99, 0.93, 0.88, and 0.78, respectively. The model can be utilized in the field with more robustness and has practical applications due to its simple and deterministic nature.

Energy demand forecasting is a fundamental aspect of modern energy management. It impacts resource planning, economic stability, environmental sustainability, and energy security. This importance is making it critical for countries worldwide, particularly in cases like Türkiye, where the energy dependency ratio is notably high. The goal of this study is to propose ensemble machine learning methods such as boosting, bagging, blending, and stacking with hyperparameter tuning and k-fold cross-validation, and investigate the application of these methods for predicting Türkiye’s energy demand. This study utilizes population, GDP per capita, imports, and exports as input parameters based on historical data from 1979 to 2021 in Türkiye. Eleven combinations of all predictor variables were analyzed, and the best one was selected. It was observed that a very high correlation exists among population, GDP, imports, exports, and energy demand. In the first phase, the preliminary performance was investigated of 19 different machine learning algorithms using 5-fold cross-validation, and their performance was measured using five different metrics: MSE, RMSE, MAE, R-squared, and MAPE. Secondly, ensemble models were constructed by utilizing individual machine learning algorithms, and the performance of these ensemble models was compared, both with each other and the best-performing individual machine learning algorithm. The analysis of the results revealed that placing Ridge as the meta-learner and using ET, RF, and Ridge as the base learners in the stacking ensemble model yielded the highest R-squared value, which was 0.9882, indicating its superior performance. It is anticipated that the findings of this research can be applied globally and prove valuable for energy policy planning in any country. The results obtained not only highlight the accuracy and effectiveness of the predictive model but also underscore the broader implications of this study within the framework of the United Nations’ Sustainable Development Goals (SDGs).

Personnel evaluation and selection is an essential part of modern business. Appropriate candidate selection can significantly contribute to companies in terms of increased profit, good culture, reputation, reduced costs, etc. This paper addresses the personnel evaluation and selection problem at the University of Pardubice, Faculty of Transport Engineering (UPCE). Since this is a typical ranking alternative problem where multiple criteria affect the decision, the Z-numbers-based Alternative Ranking Order Method Accounting for the two-step Normalization (AROMAN) is applied. Four Ph.D. candidates are assessed, and the most appropriate is selected to be employed by the UPCE. The Z-numbers fuzzy AROMAN method ranks Ph.D. candidate number four as the most appropriate alternative. To investigate the stability and sensitivity of the Z-numbers fuzzy AROMAN method, the values of parameters β and λ used in the mathematical calculations of the method were changed. The results of sensitivity analysis revealed that the obtained solution is stable. To confirm the robustness of the proposed approach, a comparative analysis is performed. Simple Additive Weighting (SAW), Weighted Product Model (WPM), and Z-number fuzzy TOPSIS were applied. Besides, we applied the fuzzy inferior ratio method as well. The results confirm the high robustness of the proposed Z-numbers fuzzy AROMAN method.

Dünya genelinde hızla tükenen enerji kaynaklarını korumaya yönelik birtakım tedbirler alınmaktadır. Özellikle ulaşım araçlarında enerji verimliliğinin artırılması amacıyla sürdürülebilir enerji kaynaklarına yönelim giderek artmaktadır. Yapılan bu çalışmada ülkemizdeki demiryolu taşıtlarının son 43 yıldaki hat uzunluğu, yolcu sayısı ve yük miktarı değerleri esas alınarak toplam enerji tüketim miktarları incelenmiştir. Yapay sinir ağları yöntemi ile oluşturulan 5 farklı model için tükenen enerji miktarı ile tahmin edilen enerji miktarları korelasyon katsayıları, R2, mutlak hata ve mutlak bağıl hata ölçütleri Levenberg-Marquardt ve Conjugate Gradient Descent algoritmaları kullanılarak karşılaştırılmıştır. Girdi olarak yolcu sayısı ve yük miktarı alınan 3 numaralı modelin doğruluk değerleri ve hata ölçütleri daha iyi çıkmıştır. Çalışmada elde edilen sonuçlara göre enerji tüketim miktarının en çok yük miktarı daha sonra ise yolcu sayısı ile ilişkisi olduğu hat uzunluğu ve yıllardaki değişimin daha az etkili olduğu ortaya konmuştur. Bu çalışmada elde edilen veriler ile gelecek dönemlerdeki demiryollarında taşınması planlanan yolcu sayısı ve yük miktarı kullanılarak harcanabilecek enerji miktarı tespit edilebilecektir. Tespit edilen enerji miktarı sayesinde sürdürülebilir enerji kaynaklarına yönelim yapılarak önemli miktarda tasarruf sağlanabilecektir.