Prediction of the Numbers of Visitors at the Sinop Museums by Artificial Neural Networks

In this study, the numbers of museums ‘visitors (Archaeology, Ethnography and Historical Prison) at the city center of Sinop province have been predicted by Artificial Neural Network structures. Artificial Neural Network models have been created in MATLAB environment. These Artificial Neural Network models are feed forward and trained by Backpropagation Algorithm. For each museum, a Artificial Neural Network with 19-inputs and 1-output have been used separately. As inputs of networks, 10 different meteorological factors, time factor (month, year), tourism income (TL), exchange rate ($/TL) and monthly-yearly PPI and CPI data have been used. Output of ANNs is the daily average of number of visitors for each month. In order to train and test the Artificial Neural Networks, the number of visitors of museum at city center for total 60 months of years between 2012 and 2017, and other input data have been used. The selection of proper Artificial Neural Networks structure have been achieved by trying backpropagation training functions 50-times on 3-different activation functions structure with 8 different neuron counts at one hidden layer. Totally, 32400-network have been created by training and the best network structure for each museum have been selected. Estimation result obtained by the Artificial Neural Network models have been evaluated and discussed. As a result of this work, it has been proved that estimation of number of visitors visiting museums at Sinop province can be done by using ANN structures