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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]

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]

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]

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]

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]