Abstract
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.