Browsing by Author "Ari D."

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    An Experimental Design Frame for Active Dam Reserve Ratio Forecasting Using Neural Networks
    (EDP Sciences, 2024) Mizrak Ozfirat P.; Ari D.
    Today, one of the important and frequently spoken problems of the world is global warming and climate change. Due to these subjects, water drought and scarcity may become a trouble in the future. To prevent these problems, scientific studies are being carried out, solutions are being recommended and preventive applications are developing. In this study, to examine and foresee the decrease in water resources, active dam reserve ratio is considered and estimated using artificial neural networks. Time series analysis is performed using the active dam reserve ratio of Guzelhisar Dam, located in city of Izmir, Turkiye. Active reserve ratio data between 2012 and 2023 are considered on monthly basis. Since the data set displays high seasonality, this cyclic effect is extracted out of the data to get non-seasonal series. Then, using non-linear autoregressive artificial neural network method, both original seasonal data and non-seasonal data is forecasted. Three parameters are considered for neural network models: Input neurons, middle layer neurons and backpropagation algorithm. Results are compared according to mean absolute percent error. In the result, values of parameters to give minimum error are presented. In addition, performances of backpropagation algorithms are compared. © The Authors, published by EDP Sciences, 2024.
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    Comparison of artificial neural networks and regression analysis for airway passenger estimation
    (Elsevier Ltd, 2024) Ari D.; Mizrak Ozfirat P.
    With the increasing demand in operations, time is getting more important. In order to use time and energy more effectively, it is becoming more important for airline companies and airport managements to make strategic plans for the future. To make beneficial and correct strategic plans for airways, one of the factors that is needed to be considered is future passenger numbers. With more accurate passenger number forecasts, airport managements can act more efficiently and reduce time, energy consumption and hence would be able to reduce costs. In this study, airway passenger number estimation is handled. Three metropolitan cities’ airport passenger numbers are considered. Artificial neural networks and regression analysis are carried out to estimate passenger number. In addition, data are handled in two different ways. Firstly, ANN and regression analysis are applied using original data series. In the second step, seasonal decomposition is applied on the data series and both approaches are repeated for deseasonal series. In Artificial Neural Networks approach, an experimental design is developed considering training algorithms, number of input nodes and number of nodes in the hidden layer which make up 960 design points. In the results of these experiments, performance of ANN approach is tested for three input factors and high-performance design points are identified. Furthermore, for benchmarking purposes, regression analysis is carried out. Linear, logarithmic, power, exponential, and polynomial models are developed. Finally, results of ANN and regression approaches are compared in terms of mean absolute percent error, and it is found that ANN overperformed compared to regression analysis. © 2024 Elsevier Ltd