Öz
Tourism is one of the major sector for economy. Along with the progress of globalization, the number of tourist has been increasing in recent year. Safranbolu, which hosts domestic and foreign tourists, has started to make its place in the economy of the district with the formation of small and medium-sized touristics facilities since the early 1990s. Artificial Neural Networks, one of the methods that work by imitating human brains, are used in prediction and analysis methods. NARX (Nonlinear Autoregressive Exogenous) recurrent neural networks, an Artificial Neural Network model, are a powerful class of models that have proven to be very suitable for modeling nonlinear systems and especially time series. The NARX neural network has feedback links that surround several layers of the network. In this study an analysis of the number of the tourists coming to Safranbolu was made using the monthly exchange rate, NARXwith consumer price index model between 2003-2021. The data are trained in MATLAB environment. As a result of the study, it was concluded that NARX Neural Network is high and effective performance method in the analysis of the number of tourists in Safranbolu.
Kaynakça
- Abba, S. I., Usman, A. G., Danmaraya, Y. A., Usman, A. G., & Abdullahi, H. U. (2020). Modeling of Water Treatment Plant Performance Using Artifical Neural Network: Case Study Tamburawa Kano-Nigeria. Dutse Journal of Pure and Applied Sciences, 6(3), 135-144.
- Alamsyah, A., & Friscintia, P. B. (2019). Artificial Neural Network for Indonesian Tourism Demand Forecasting. 2019 7th International Conference on Information and Communication Technology .
- Anderson , D., & McNeill, G. (1992). Artifical Neural Newtorks Technology. State of the Art Report.
- Boussaada, Z., Curea, O., Remaci, A., Camblong, H., & Bellaaj, N. M. (2018). A Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the Prediction of the Daily Direct Solar Radiation. MDPI Energies.
- Buevich, A., Sergeev, A., Shichkin, A., & Baglaeva, E. (2021). A Two-Step Combined Algorithm Based on Narx Neural Network and the Subsequent Prediction of the Residues İmproves Prediction Accuracy of the Greenhouse Gases Concentrations. Neural Computing and Application, 1547-1557.
- Cho, V. (2003). A Comparison of Three Different Approaches to Tourist Arrival Forecasting. Tourism Management, 24(3), 323-330.
- Constantino, H. A., Fernandes, P. O., & Teixeira, J. P. (2016). Tourism Demand Modelling and Forecasting with Artificial Neural Network Models: The Mozambique Case Study. TÉKHNE - Review of Applied Management Studies.
- Diaconescu, E. (2008). The Use of NARX Neural Networks to Predict Chaotic Time Series. Wseas Transaction on Computer Research, 3(3), 182-191.
- Fernandes, P., & Teixeira, J. (2008). Applying The Artificial Neural Network Methodology For Forecasting The Tourism Time Series. 5 th International Scientific Conference Business and Managament, (s. 653- 658).
- Gurari, D. (2021). Recurrent Neural Networks. University of Texas at Austin. https://home.cs.colorado. edu/~DrG/Courses/IntroToMachineLearning/Lectures/10_RecurrentNeuralNetworks.pdf adresinden alındı
- Güner, Ş. N. (2016). Konaklama İşletmelerinin Fiyatlandırma Stratejileri ve Yöntemlerine Yönelik Algıları: Safranbolu Örneği. Yüksek Lisans Tezi. Karabük: Karabük Üniversitesi Sosyal Bilimler Enstitüsü.
- Li, Y., & Cao, H. (2018). Prediction for Tourism Flow based on LSTM Neural Network. Procedia Computer Science(129), 277–283
- Nguyen, Q. L., Fernandes , P. O., & Teixeira , J. P. (2022). Analyzing and Forecasting Tourism Demand in Vietnam with Artificial Neural Networks. MDPI Forecasting, 36–50.
- Öztemel, E. (2020). Yapay Sinir Ağları. Papatya Bilim.
- Ravazi, S., & Talson, B. A. (2011, 10). A New Formulation for Feedforward Neural Networks. IEE Transaction on Neural Networks, 22(10), 1587-1598.
- Siami-Namini, S., Tavakoli, N., & Namin, A. S. (2019). A Comparison of ARIMA and LSTM in Forecasting Time Series. 17 th IEEE International Conference on Machine Learning and Applications, ICMLA 2018, 1394-1401).
- Sugiartawan, P., Hartati, S., & Musdholifah, A. (2018). Tourist Visits Prediction With Fully Recurrent Neural Network. The 2018 International Conference on Information Technology, Engineering, Science, and its Applications, 156-165.
- Sugiartawan, P., Hartati, S., & Musdholifah, A. (2019). Tourist Visits Prediction With Fully Recurrent Neural Network . International Conference on Information Technology ,Engineering, Science, and its Applications, 156-165.
- T.C. Kültür ve Turizm Bakanlığı. (2022). Kültür Varlıkları ve Müzeler Genel Müdürlüğü. . https://kvmgm.ktb.gov.tr/TR-44426/safranbolu-sehri-karabuk.html adresinden alındı.
- Tanty , R., & Desmukh, T. S. (2015). Application of Artificial Neural Network in Hydrology- a Review. International Journal of Engineering Research & Technology, 4(6), 184-188.
- Ulucan , E., & Kızılırmak, İ. (2018). Konaklama İşletmelerinde Talep Yöntemleri: Yapay Sinir Ağları ile İlgili Bir Araştırma. Seyahat ve Otel İşletmeciliği Dergisi, 15, 89-101.
- UNWTO. (2022). World Tourism Organization. UNTWO Academy: https://www.unwto.org/UNWTOacademy adresinden alındı
Öz
Turizm, ekonomi için ana sektörlerden biri haline geldi. Küreselleşmenin ilerlemesiyle birlikte, son yıllarda turist sayısı her geçen yıl artmaktadır. Yerli ve yabancı turistlere ev sahipli yapan Safranbolu 1990’lı yılların başından bu yana küçük ve orta ölçekli turistik tesislerin oluşumu ile ilçe ekonomisindeki yerini hissettirmeye başlamıştır. İnsan beynini taklit ederek çalışan yöntemlerden biri olan yapay sinir ağları, tahmin ve analiz yöntemlerinde kullanılmaktadır. Yapay Sinir Ağı modeli olan NARX (Nonlinear Autoregressive Exogenous) tekrarlayan sinir ağları, doğrusal olmayan sistemleri ve özellikle zaman serilerini modellemek için çok uygun oldukları kanıtlanmış güçlü bir model sınıfıdır. NARX sinir ağı, ağın birkaç katmanını çevreleyen geri besleme bağlantılarına sahiptir. Bu çalışmada 2003 -2021 yılları arası aylık döviz kuru, tüketici fiyat endeksi ile NARX sinir ağı modeli kullanılarak Safranbolu’ya gelen turist sayısına yönelik analiz yapılmıştır. Veriler MATLAB ortamında eğitilmiştir. Yapılan çalışma sonucunda Safranbolu turist sayısı analizinde NARX sinir ağının yüksek ve etkili bir performans yöntemi olduğu sonucuna ulaşılmıştır.
Kaynakça
- Abba, S. I., Usman, A. G., Danmaraya, Y. A., Usman, A. G., & Abdullahi, H. U. (2020). Modeling of Water Treatment Plant Performance Using Artifical Neural Network: Case Study Tamburawa Kano-Nigeria. Dutse Journal of Pure and Applied Sciences, 6(3), 135-144.
- Alamsyah, A., & Friscintia, P. B. (2019). Artificial Neural Network for Indonesian Tourism Demand Forecasting. 2019 7th International Conference on Information and Communication Technology .
- Anderson , D., & McNeill, G. (1992). Artifical Neural Newtorks Technology. State of the Art Report.
- Boussaada, Z., Curea, O., Remaci, A., Camblong, H., & Bellaaj, N. M. (2018). A Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the Prediction of the Daily Direct Solar Radiation. MDPI Energies.
- Buevich, A., Sergeev, A., Shichkin, A., & Baglaeva, E. (2021). A Two-Step Combined Algorithm Based on Narx Neural Network and the Subsequent Prediction of the Residues İmproves Prediction Accuracy of the Greenhouse Gases Concentrations. Neural Computing and Application, 1547-1557.
- Cho, V. (2003). A Comparison of Three Different Approaches to Tourist Arrival Forecasting. Tourism Management, 24(3), 323-330.
- Constantino, H. A., Fernandes, P. O., & Teixeira, J. P. (2016). Tourism Demand Modelling and Forecasting with Artificial Neural Network Models: The Mozambique Case Study. TÉKHNE - Review of Applied Management Studies.
- Diaconescu, E. (2008). The Use of NARX Neural Networks to Predict Chaotic Time Series. Wseas Transaction on Computer Research, 3(3), 182-191.
- Fernandes, P., & Teixeira, J. (2008). Applying The Artificial Neural Network Methodology For Forecasting The Tourism Time Series. 5 th International Scientific Conference Business and Managament, (s. 653- 658).
- Gurari, D. (2021). Recurrent Neural Networks. University of Texas at Austin. https://home.cs.colorado. edu/~DrG/Courses/IntroToMachineLearning/Lectures/10_RecurrentNeuralNetworks.pdf adresinden alındı
- Güner, Ş. N. (2016). Konaklama İşletmelerinin Fiyatlandırma Stratejileri ve Yöntemlerine Yönelik Algıları: Safranbolu Örneği. Yüksek Lisans Tezi. Karabük: Karabük Üniversitesi Sosyal Bilimler Enstitüsü.
- Li, Y., & Cao, H. (2018). Prediction for Tourism Flow based on LSTM Neural Network. Procedia Computer Science(129), 277–283
- Nguyen, Q. L., Fernandes , P. O., & Teixeira , J. P. (2022). Analyzing and Forecasting Tourism Demand in Vietnam with Artificial Neural Networks. MDPI Forecasting, 36–50.
- Öztemel, E. (2020). Yapay Sinir Ağları. Papatya Bilim.
- Ravazi, S., & Talson, B. A. (2011, 10). A New Formulation for Feedforward Neural Networks. IEE Transaction on Neural Networks, 22(10), 1587-1598.
- Siami-Namini, S., Tavakoli, N., & Namin, A. S. (2019). A Comparison of ARIMA and LSTM in Forecasting Time Series. 17 th IEEE International Conference on Machine Learning and Applications, ICMLA 2018, 1394-1401).
- Sugiartawan, P., Hartati, S., & Musdholifah, A. (2018). Tourist Visits Prediction With Fully Recurrent Neural Network. The 2018 International Conference on Information Technology, Engineering, Science, and its Applications, 156-165.
- Sugiartawan, P., Hartati, S., & Musdholifah, A. (2019). Tourist Visits Prediction With Fully Recurrent Neural Network . International Conference on Information Technology ,Engineering, Science, and its Applications, 156-165.
- T.C. Kültür ve Turizm Bakanlığı. (2022). Kültür Varlıkları ve Müzeler Genel Müdürlüğü. . https://kvmgm.ktb.gov.tr/TR-44426/safranbolu-sehri-karabuk.html adresinden alındı.
- Tanty , R., & Desmukh, T. S. (2015). Application of Artificial Neural Network in Hydrology- a Review. International Journal of Engineering Research & Technology, 4(6), 184-188.
- Ulucan , E., & Kızılırmak, İ. (2018). Konaklama İşletmelerinde Talep Yöntemleri: Yapay Sinir Ağları ile İlgili Bir Araştırma. Seyahat ve Otel İşletmeciliği Dergisi, 15, 89-101.
- UNWTO. (2022). World Tourism Organization. UNTWO Academy: https://www.unwto.org/UNWTOacademy adresinden alındı
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Konular | Turizm (Diğer) |
| Bölüm | Araştırma Makaleleri |
| Yazarlar | |
| Yayımlanma Tarihi | 31 Ocak 2024 |
| Gönderilme Tarihi | 27 Kasım 2023 |
| Kabul Tarihi | 22 Ocak 2024 |
| Yayımlandığı Sayı | Yıl 2024 Cilt: 4 Sayı: 1 |
