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Streamflow estimation is necessary to develop sustainable water management strategies that balance the needs of various water users while protecting the ecological health of the watershed. This study presents a comprehensive analysis of river flow data in Turkey's Meriç basin using long-term daily data spanning the years 2001–2011. Within the scope of forecasting of daily river flows, 80% of dataset was used for training stage and the 20% of dataset was used for testing. The performance of four different models, including Extreme Gradient Boosting (XGBoost) benchmark model, Linear Regression, Long Short-Term Memory Network (LSTM), and proposed Particle Swarm Optimization (PSO)—XGBoost hybrid model, was evaluated using several statistical evaluation criteria such as RMSE, MAPE, MAE, SD, NSE, KGE, and model success was assessed based on determination coefficient (R2) values. The forecasting results of the models were evaluated with statistical metrics. The proposed PSO-XGBoost model outperformed all models with R2 values of 0.7460 at the Babaeski FMS station, 0.9582 at the Meriç FMS station, and 0.8116 at the Hayrabolu FMS station, respectively. It has been found that when the parameters of the XGBoost model are optimized with the meta-heuristic PSO algorithm, the daily flow prediction accuracy is significantly increased in the Meriç River basin, which has a pluviozonal River regime. The results demonstrate that the proposed hybrid model produces precise and reliable estimations for daily river flow analysis in the Meriç basin. The findings derived from the proposed model demonstrate its promising potential for water resource management and decision-making in the region. [ABSTRACT FROM AUTHOR]

Many companies prefer to use third party logistics firms to deliver their goods and as such planning the return of the vehicles to the depot is not required. This is called open vehicle routing problem (OVRP). In literature, the OVRP is handled with minimum distance as objective function like vehicle routing problem. But in the real world, the objective function achieves minimum many costs like standard routing cost, stopping by cost and the deviation cost. The standard routes are previously defined under free market conditions by third party logistic firms. The deviation from the standard route is required to arrive cities which are not on the standard route. The stop by cost occurs on the delivery points. In this paper mentioned three costs are considered in the objective function while many papers consider only distance related costs in the literature. This paper proposes a new mathematical model for the OVRP. In the constraints, the last points of the routes are researched in detail. The standard route costs are determined by considering the last point of the route. Because of the NP-hard structure of the OVRP, the proposed mathematical model is solved with a hybrid metaheuristic called Civilized Genetic Algorithm (CGA). CGA is developed by hybridizing a modified genetic algorithm and a local search algorithm. The application of this study is implemented for the delivery routing of a combi boiler producer in Turkey. The third party logistic firms may use this proposed model and the solution approach for the real life applications. [ABSTRACT FROM AUTHOR]

In this study, the success of the numerical methods and a metaheuristic algorithm in parameter estimation of Weibull distribution, which is frequently used in wind energy applications, are compared. Numerical methods are Justus empirical method, moment method, graphical method, energy pattern factor method (EPM), energy trend method, maximum likelihood estimation method (MLE). The metaheuristic algorithm is manta ray foraging optimization method (MRFO). The wind data used in the study were recorded hourly in the Isparta region in the southwest of Turkey. A graphical user interface design has been made to easily perform the calculations in this study. The success of the methods was tested with four different statistical error analysis methods. According to the results of the analysis, the MRFO method was by far the most successful method. EPM and MLE methods were the most unsuccessful methods. [ABSTRACT FROM AUTHOR]

Medium Neural Networks (MNN), Whale Optimization Algorithm (WAO), and Support Vector Machine (SVM) methods are frequently used in the literature for estimating electricity demand. The objective of this study was to make an estimation of the electricity demand for Turkey's mainland with the use of mixed methods of MNN, WAO, and SVM. Imports, exports, gross domestic product (GDP), and population data are used based on input data from 1980 to 2019 for mainland Turkey, and the electricity demands up to 2040 are forecasted as an output value. The performance of methods was analyzed using statistical error metrics Root Mean Square Error (RMSE), Mean Absolute Error (MAE), R-squared, and Mean Square Error (MSE). The correlation matrix was utilized to demonstrate the relationship between the actual data and calculated values and the relationship between dependent and independent variables. The p-value and confidence interval analysis of statistical methods was performed to determine which method was more effective. It was observed that the minimum RMSE, MSE, and MAE statistical errors are 5.325 × 10−14, 28.35 × 10−28, and 2.5 × 10−14, respectively. The MNN methods showed the strongest correlation between electricity demand forecasting and real data among all the applications tested. [ABSTRACT FROM AUTHOR]

The aim of this work is to investigate whether retrieving the model parameters of self-potential (SP) anomalies using a combination of differential evolution (DE) and particle swarm optimization (PSO) is possible. This approach hybridizes DE and PSO in a parallel way. Each algorithm is self-contained and obtains a [premature] solution after a user-defined generation number. This hybrid algorithm (DE/PSO) selects the best individual in DE and PSO populations and carries it to the next iteration. Cooperation of DE and PSO can significantly improve the results. Simulations through noise-free synthetic anomalies show that the DE/PSO hybrid algorithm is successful in providing more accurate solutions than those obtained using each single metaheuristic. The algorithm also speeds up the rate of convergence to get the optimum solution. We implemented the algorithm in R programming environment using available metaheuristics packages. Then, the reliability of the code was investigated using some mathematical test functions having two and higher dimensions (unknowns). The performance of the hybrid to invert SP anomalies was tested by synthetic and field data sets. The true model parameters were well-recovered from synthetic data sets including noise-free and noisy data. In the tests with field data, SP anomalies over a shallow ore deposit in Süleymanköy (Türkiye), a deep ore deposit in Arizona (USA), and multiple sources of graphite deposits in KTB borehole site (Germany) were inverted. Low misfit values between the observed and calculated SP anomalies were obtained during the test studies. [ABSTRACT FROM AUTHOR]

Natural gas demand forecasting is critical, especially for the decision makers of countries with high energy consumption and the industrial sector. This study presents Turkey's monthly natural gas demand forecasting model according to meteorological parameters. In the study, models created with the Artificial Bee Colony Algorithm (ABC), Loaded System Search Algorithm (CSS), Crow Search Algorithm (CSA), and Harmony Search Algorithm (HSA) were compared. Linear, exponential, and quadratic mathematical models were developed in the research. The models' performances were evaluated with six different global error criteria. We used the input parameters such as average temperature, pressure, humidity, wind, and precipitation from meteorological data. The data between 2010-2017 was applied as training data, and the data between 2018-2020 was applied as test data. While the model that successfully predicts the natural gas demand forecast training dataset is the quadratic model of the CSS algorithm, it is the quadratic model of the CSA algorithm in the test data. In all three models, the model that made the most successful prediction was CSA, the quadratic model, while the CSS exponential model was the model that produced the most unsuccessful predictive value. [ABSTRACT FROM AUTHOR]

Optimizing aircraft maintenance, repair and overhaul (MRO) planning, i.e., reviewing aircraft history based on when and where the aircraft should be serviced, is essential to ensuring aviation safety and operations. This paper examines a method for optimizing the scheduling of MRO operations for complex products. It has been shown in the literature that scheduling optimization of MRO operations is an NP-hard problem. In this study, the planning problem for MRO service resources is first analyzed, a mathematical model for the MRO process planning problem is established, and a multi-objective mathematical model for minimizing makespan time, maintenance time, and cost is proposed to represent the planning problem. Then, to optimize the makespan time, maintenance time, and cost, we first generate the maintenance, repair and overhaul transaction path (MROTP) data to utilize the PLM system efficiently, which ensures the effective operation of MRO actions throughout the aircraft lifecycle. MROTP data are obtained with engineering studies in our proposed research, and received data are retained in the PLM system. Finally, the modified ACO algorithm that performs scheduling optimization of the MRO process and the integration process of PLM with MRO operations is presented. The proposed algorithm is tested and validated by simulations in MATLAB software with the data obtained from the leading MRO company in Turkey. The Gurobi Solver is used to obtain the optimal solution by converting the problem into mixed-integer linear programming (MILP). The experimental setup is run using Gurobi with a 1-hour time limit, and the best solution is considered with an average run time calculated in seconds. When solving complicated instances, the MILP model-based approach is challenging. The study and comparison of the findings demonstrate that the proposed ACO-based algorithms have an advantage in planning MRO operations. The proposed ACO algorithm performs better than the other metaheuristic algorithms and the MILP model regarding maintenance time and cost. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) is of paramount importance in irrigation scheduling, infiltration, runoff, and agricultural drought monitoring. This work aimed at evaluating the performance of the classical ANFIS (Adaptive Neuro-Fuzzy Inference System) model as well as ANFIS coupled with three bio-inspired metaheuristic optimization methods including whale optimization algorithm (ANFIS-WOA), krill herd algorithm (ANFIS-KHA) and firefly algorithm (ANFIS-FA) in estimating SM. Daily air temperature, relative humidity, wind speed and sunshine hours data at Istanbul Bolge station in Turkey and soil temperature values measured over 2008–2009 were fed into the models under six different scenarios. ANFIS-WOA (RMSE = 1.68, MAPE = 0.04) and ANFIS (RMSE = 2.55, MAPE = 0.07) exhibited the best and worst performance in SM estimation, respectively. All three hybrid models (ANFIS-WOA, ANFIS-KHA and ANFIS-FA) improved SM estimates, reducing RMSE by 34, 28 and 27% relative to the base ANFIS model, respectively. A more detailed analysis of model performances in estimating moisture content over three intervals including [15–25), [25–35) and ≥35% revealed that ANFIS-WOA has had the lowest errors with RMSEs of 1.69, 1.89 and 1.55 in the three SM intervals, respectively. From the perspective of under- or over-estimation of moisture values, ANFIS-WOA (RMSE = 1.44, MAPE = 0.03) in under-estimation set and ANFIS-KHA (RMSE = 1.94, MAPE = 0.05) in over-estimation set showed the highest accuracies. Overall, all three hybrid models performed better in the underestimation set compared to overestimation set. [ABSTRACT FROM AUTHOR]

The ability to accurately estimate energy consumption in the medium and long term based on actual indications is critical for countries to plan and prioritize their futures and take the appropriate actions. This paper proposes a new modified artificial bee colony (M-ABC) method that can adaptively select an optimal search equation to more accurately estimate Turkey's energy consumption. In the study, linear (M-ABCL) and quadratic (M-ABCQ) mathematical models were developed, and gross domestic product (GDP), population, import, and export data were used as input parameters for energy demand estimation. The weight values in the regression models are calculated according to the objective function with the proposed M-ABC. In this way, the weight values that will produce estimations with the lowest error according to the selected years are found, and then the most appropriate energy demand estimations are made. We compared the performance of our proposed M-ABC algorithm with ant colony optimization (ACO), particle swarm optimization (PSO), and hybrid ACO and PSO (HAP) algorithms. In addition, various estimation suggestions are presented under four different scenarios using input parameters. According to the results, the models suggested with the M-ABC algorithm were more successful in estimating the energy demand. According to the results of the presented four scenarios, the energy demand in 2025 is 145.26, 139.85, 126.26, and 144.17 million tons of oil equivalent (Mtoe) for the M-ABCL model, and 185.62, 161.94, 118.96, and 159.71 Mtoe for the M-ABCQ model, respectively. Thus, it is predicted that average consumption will increase by 51.65% in the linear model and 70.94% in the quadratic model. [ABSTRACT FROM AUTHOR]

This study aimed to develop a new adaptive artificial bee colony (A-ABC) algorithm that can adaptively select an appropriate search equation to more accurately estimate transport energy demand (TED). Also, A-ABC and canonical artificial bee colony (C-ABC) algorithms were compared in terms of efficiency and performance. The input parameters used in the proposed TED model were the official economic indicators of Turkey, including gross domestic product (GDP), population, and total vehicle kilometer per year (TKM). Three mathematical models, linear (A-ABCL), exponential (A-ABCE), and quadratic (A-ABCQ) were developed and tested. Also, economic variables were generated using the "curve fitting" technique to see TED's projections for the year 2034, under two different scenarios. In the first scenario, the results of linear, exponential, and quadratic models according to 2034 TED estimates were 40.1, 31.6, and 70.5 million tons of oil equivalent (Mtoe), respectively. In the second scenario, the results of linear, exponential, and quadratic models according to the TED estimates for 2034 were found as 40.0, 31.5, and 66.5 Mtoe, respectively. The presented models, A-ABCL, A-ABCE, A-ABCQ for the solution of the TED problem, produced successful results compared to the studies in the literature. Besides that, according to global error metrics, developed models generated lower error values than C-ABC. Furthermore, consumption estimation values of A-ABCL and A-ABCE were lower than A-ABCQ. According to A-ABCQ model estimations for both scenarios, the TED value would increase approximately three times from 2013 to 2034. [ABSTRACT FROM AUTHOR]

In recent years, Unmanned Aerial Vehicles (UAVs) are a good alternative for the problem of ensuring the security of the borders of the countries. UAVs are preferred because of their speed, ease of use, being able to observe many points at the same time, and being more cost-effective in total compared to other security tools. This study is dealt with the problem of the use of UAVs for the security of the Turkey-Syria borderline which becomes sensitive in recent years and the problem is modeled as a UAV routing problem. To solve the problem, a Genetic Algorithm Based Matheuristic (GABM) approach has been developed and 12 scenarios have been created covering the departure bases, daily patrol numbers, and ranges of UAVs. GABM finds the minimum number of UAVs to use in scenarios with the help of a GA run first and tries to find the optimal routes for these UAVs. If GABM can find an optimal route for the determined UAV number, it decreases the UAV number and tries to solve the problem again. GABM proposes a hybrid approach in which a metaheuristic with a mathematical model works together and the metaheuristic sets an upper limit for the number of UAVs in the model. In computational studies, when compared GA with GABM it is seen that GABM has obtained good results and decreased the utilized number of UAVs (up to 400%) and their flight distances (up to 85.99%) for the problem in very short CPU times (max. 122.17 s. for GA and max. 46.39 s. for GABM in addition to GA). [ABSTRACT FROM AUTHOR]

Residual drift of structural systems after seismic events can make buildings uninhabitable and may cause their collapse in aftershocks, as seen in the 2023 Turkey-Syria earthquake. Self-centering rocking-core systems can eliminate plastic deformation of structures after earthquakes. In the current study, a bi-rocking steel-braced frame was designed using the modified modal superposition (MMS) method. The optimal location of the secondary joint was found to minimize the base shear, overturning moment, peak floor acceleration and inter-story drift. Damage to non-structural components has been considered. The models included 12-, 18- and 24- story frames that were subjected to far-field (FF), near-field-pulse (NP) and near-field-no-pulse (N) ground motion records. The seismic records were scaled to the maximum credible earthquake level. Nonlinear time-history analyses were performed using OpenSees open-source finite element software. Additionally, post-tensioned cables and energy dissipation fuses as variable parameters were optimized using particle swarm optimization. The performance of seven metaheuristic algorithms was compared and the results showed that placing the secondary joint at the mid-height of the structure reduced all seismic demand. The results also suggest that the story moment and FF records were most sensitive to the location of the secondary joint. It was observed that frames with lower stiffness and yield strength of the damper exhibited better seismic performance. Based on this, corrective percentages have been proposed for the design of the cable and dampers. A self-centering ratio of 1.07 was suggested at the mid-height and 1.19 for the base damper. The colliding bodies optimization algorithm performed best for time-history analysis-based optimization. • Using a bi-rocking mechanism to reduce the higher mode effects of high-rise structures. • Designing the bi-rocking steel-braced frame with modified modal superposition method. • Optimization of the main design parameters of bi-rocking joints using metaheuristic algorithms. • Investigating the performance of the bi-rocking steel-braced frame under far- and near-field ground motions. [ABSTRACT FROM AUTHOR]

In this study, eight parameters are selected and their historical data are collected to predict the future of the energy demand of Turkey. The initial eight parameters were the gross domestic product (GDP) of Turkey, average annual US crude oil price (COP), inflation for Turkey in percentages (INF), the population of Turkey, total vehicle travel in kilometers for Turkey, total amount of goods transported on motorways, employment for Turkey, and trade of Turkey. However, after these eight parameters data are analyzed using Pearson and Spearman correlation methods, it is found out that five of these parameters are highly correlated. The remaining three parameters are the GDP of Turkey, COP, and INF for Turkey. Afterward, five separate scenarios are developed to forecast the future of the energy demand of Turkey. The first two scenarios involve the third- and fourth-order polynomial fitting, the third and fourth scenarios employ static and recurrent neural networks, and the fifth scenario utilizes autoregressive models to predict the future energy demand of Turkey. The efficient hybridization of the seagull optimization and very optimistic method of minimization metaheuristic algorithms is carried out to achieve the polynomial fitting of the data. The optimization performance of the hybrid algorithm is assessed by applying the algorithm on benchmark optimization problems and comparing the results with that of some other metaheuristic optimizers. Moreover, it is seen that the forecasts of the first scenario agree well with the Ministry of the Energy and Natural Resources estimates. [ABSTRACT FROM AUTHOR]

The hybridization of multiple energy sources improves the system efficiency and the reliability of the supply than single-source generators. This article presents a multi-objective optimization of developed multiple sources consisting of solar and wind turbine systems, micro-gas turbine and battery energy storage. This optimization technique is based on the use of a metaheuristic method as Harmony Search Algorithm to solve the optimization problem, taking into account a control of energy management of the hybrid configuration system, supply a residence load (e.g., buildings) located in Turkey. The applied method allows to minimize the total system cost and the emission of the carbon dioxide. A comparative study has been analyzed for different configuration systems based on the suggested hybrid system to select the optimal solution about the cost, the sizing and the reduction in the CO2 emission. The simulation results, obtained under MATLAB environment, demonstrated that the optimal configuration system is selected by the proposed hybrid (PV/wind/micro-gas turbine/battery) energy system. The improved algorithm by Global Dynamic Harmony Search method has better efficacy and proves its performance in the optimization process as it converges faster than other tested methods with a better running time. [ABSTRACT FROM AUTHOR]

This study proposed a metaheuristic pansharpening (MP) method, which performs in a Synthetic Variable Ratio (SVR)-like manner. The proposed method introduced the Symbiotic Organisms Search (SOS) algorithm, an advanced nature-based optimization algorithm, to estimate a weight for each multispectral (MS) band to achieve the optimum intensity. The SVR pansharpening formula was used as the objective function and the Root Mean Square Error (RMSE) metric was used as the fitness function of the SOS algorithm to optimize the intensity. The spectral and spatial quality of the results of the MP method were qualitatively and quantitatively compared against those of 15 widely-used pansharpening methods in 5 test sites in Turkey with different land cover features. The experiments aimed to spatially enhance WorldView-2 MS images by using WorldView-2 panchromatic (PAN) bands and a UAV-derived PAN orthophoto. It was also aimed to sharpen IKONOS MS images by using a QuickBird pansharpened image and an IKONOS PAN band. The MATLAB software was used to implement the proposed method and to compute the spatial and spectral quality metrics. The spatial quality of each pansharpened image was evaluated at full-scale, whereas the spectral quality of each pansharpened image was evaluated at both full-scale and reduced-scale. A scoring strategy based on giving a performance score with respect to the spatial and spectral quality metrics was used to ensure a fair comparison among the pansharpening methods used. The results demonstrated that, out of 16 pansharpening methods, the MP method achieved the highest overall spectral and spatial quality scores of 15.5 and 15.5, respectively. The proposed method was found to perform successfully with both singlesensor and multisensor input images. It was also concluded that the proposed method is able to deal with high spatial resolution ratio between the input images. [ABSTRACT FROM AUTHOR]

Wind power prediction is crucial for successfully integrating large-scale wind energy with the grid and achieving a carbon-neutral energy mix. However, previous studies encountered challenges to noise reduction, information extraction, error handling, and uncertainty estimation. To address these limitations, this study proposed a novel wind power forecasting system, systematically enhancing forecasting capability by refining the raw time series using horizontal denoising and vertical granulation methods, utilizing a bidirectional deep learning model for capturing nonlinear features and optimizing its core hyperparameters with a newly developed multi-objective enhanced version of metaheuristic algorithm to achieve Pareto optimal solutions, employing the traditional statistical approach for residual linear feature extraction from error series and effective correction, and integrating quantile regression for interval prediction. The predictive performance was evaluated based on wind power, speed, and direction data from two different countries, the system yielded correlation coefficients of 0.9796, 0.9943, 0.9795, and 0.9780 in spring, summer, autumn and winter for China, and 0.9950 for Turkey, demonstrating its pronounced advantages over comparative models. Overall, the proposed system contributes to the advancement of wind-power prediction theory and holds substantial potential for practical applications. • A novel hybrid wind power forecasting system was proposed. • Solve problems of noise interference and high computing burden by data refining. • A strategy to fully extract nonlinear and linear data features was designed. • An enhanced multi-objective golden jackal algorithm was raised for optimization. • Pareto optimal solutions were theoretically proven to be achieved. [ABSTRACT FROM AUTHOR]

This study considers an extension of the two-Echelon Location-Routing Problem (2E-LRP) namely two-Echelon Location-Routing Problem with Simultaneous Pickup and Delivery (2E-LRPSPD). A Mixed Integer Programming (MIP) formulation is developed and strengthened by the use of valid inequalities to solve the 2E-LRPSPD, and then a Branch and Cut-based exact algorithm (B&C) is presented. To improve the upper bounds, Iterated Local Search and Variable Neighborhood Search-based metaheuristic algorithms, namely, ILS-VNS and VNS, are developed and embedded into the B&C. Computational results, conducted on instances produced from the literature, demonstrate that the B&C yields tight lower and upper bounds, and also is capable of solving some instances optimally with up to 50 customers and 10 satellites in a reasonable time. Furthermore, sensitivity analysis reveals that the heuristic approaches and an increase in the runtime limit assist the algorithm to reach tight upper bounds, whereas the number of potential depots does not have any effect on the performance of the algorithm. Besides, further computational analysis on the 2E-LRPSPD with a single depot, which is studied in the literature, shows that the B&C improves some best-known solutions while proving the optimality of some instances. Finally, the B&C is applied to a two-echelon supermarket distribution system in Türkiye. The computational results show that simultaneous pickup and delivery operations, as well as depot location decisions are critical elements to consider in the design of cost-effective distribution networks. [ABSTRACT FROM AUTHOR]

In this study, a novel artificial neural network (ANN)-Jaya algorithm hybrid artificial intelligence model was developed to estimate Turkey's future energy use. The model estimates energy consumption based on gross domestic product (GDP), population, import data, and export data. The Jaya algorithm used in our model's development is a simple and powerful metaheuristic algorithm that overcomes the complexity of difficult optimization problems; it provides optimal results quickly owing to its ease of applicability and simple structure. Our ANN-Jaya model's performance was compared with the performance of artificial bee colony (ABC) and teaching learning based optimization (TLBO) algorithm-trained ANN models. According to the root mean square error (RMSE) values obtained for the test set, the proposed ANN-Jaya model performed 36.7% and 46.2% better than the ANN-ABC and ANN-TLBO models, respectively. After defining the optimal configurations, three energy consumption prediction scenarios were developed and compared with previously published forecasts. [ABSTRACT FROM AUTHOR]

Decision support systems (DSSs) are indispensable tools in preparing a forest management plan for a better combination of multiple forest values. This study attempted to develop and explain a stand-based forest management DSS (Ecosystem-based multiple-use forest planning [ETÇAP]) comprising a traditional simulation, linear programming (LP), metaheuristics and geographic information system. The model consists of five submodels; traditional management approach to handle inventory data, an empirical growth and yield model, a simulation to conceptualize management actions, a LP technique to optimize resource allocation and a simulated annealing approach to directly create a spatially feasible harvest schedule. The ETÇAP model has been implemented in a comparative two case study areas; Denizli–Honaz and Akseki–Ibradı. Both simulation and optimization models outperformed to the traditional management plan. The periodical change of growing stock, allowable cuts, carbon sequestration and water production are used as performance indicators. The results showed that more amount of wood could be harvested over time compared to traditional level of harvesting. It could be concluded that various management strategies allowed managers to stimulate more decision options for better outputs through intertemporal trade-offs of management interventions as the model provided tools to quantify forest dynamics over time and space. Challenges exist to establish the functional relationships between forest structure and values for better quantification and integration into the management plans. [ABSTRACT FROM PUBLISHER]