Neural Network Estimation of Tourism Climatic Index (TCI) Based on Temperature-Humidity Index (THI)-Jordan Region Using Sensed Datasets

Open access

Abstract

Jordan which is located in the heart of the world contains hundreds of historical and archaeological locations that have a supreme potential in enticing visitors. The impact of clime is important on many aspects of life such as the development of tourism and human health, tourists always wanted to choose the most convenient time and place that have appropriate weather circumstances. The goal of this study is to specify the preferable months (time) for tourism in Jordan regions. Neural network has been utilized to analyze several parameters of meteorologist (raining, temperature, speed of wind, moisture, sun radiation) by analyzing and specify tourism climatic index (TCI) and equiponderate it with THI index. The outcomes of this study shows that the finest time of the year to entice tourists is “ April” which is categorized as to be “extraordinary” for visitors. TCI outcomes indicates that conditions are not convenient for tourism from July to August because of high temperature.

[1] DE FREITAS, C. R. SCOTT, D AND MCBOYLE, GEOFF, A second generation climate index for tourism (CIT):specification and verification. International Journal of Biometeorology. Volume 52, Number 5 / May, 399-407,2008

[2] A. Bigano, J. M. Hamilton, Tol RSJ. 2006. The impact of climate on holiday destination choice. Climatic Change 76: 389–406.

[3] J. M. Hamilton,, M. A. Lau 2005. Tourism and global environmental change: Ecological, social, economic and political interrelationships. London: Routledge, Taylor and Francis Group. 229–250.

[4] C. R. De Freitas, D. Scott, G. McBoyle, 2008. A second generation climate index for tourism: specification and verification. International Journal of Biometeorology, 52, 399-407.

[5] J. P. Besancenot, 1990. Climate et tourisme. Massonedit. Collection geographie: Paris.

[6] C. R. De Freita, 2003. Tourism climatology: evaluating environmental information for decision making and business planning in the recreation and tourism sector. Int. J. Biometeorology 48: 45-54.

[7] Z. Mieczkowski, 1985. The tourism climate index: A method for evaluating world climates for tourism, The Canadian Geographer. 29: 220-233.

[8] Vitt R., Gulyás A. and Matzarakis A. (2015). Temporal Differences of Urban-Rural Human Biometeorological Factors for Planning and Tourism in Szeged, Hungary. Advances in Meteorology, Volume 2015.

[9] Xinghuo Yu, M. Onder Efe, and Okyay Kaynak, A General Back propagation Algorithm for Feedforward Neural Networks Learning. IEEE Transactions On Neural Networks,Vol. 13, No.1, january 2002.

[10] Mohsen Hayati, and Zahra Mohebi,” Application of Artificial Neural Networks for Temperature Forecasting,” World Academy of Science, Engineering and Technology “ 2007.

[11] Y. Saika and M. Nakagawa, “Dynamics of predicting temperature-humidity index and power consumption in small-scale system utilizing Bayesian inference via the EAP estimation,” 2017 17th International Conference on Control, Automation and Systems (ICCAS), Jeju, 2017, pp. 975-980.

[12] K. Okamoto, M. Yokozawa and H. Kawashima, “Evaluation of changes in climatic indices using combined analysis of remote sensing and GIS,” 2001 International Conferences on Info-Tech and Info-Net. Proceedings (Cat. No.01EX479), Beijing, 2001, pp. 133-138 vol.1.

[13] Hao Guo, Anming Bao, Tie Liu, Sheng Chen, and Felix Ndayisaba,” Evaluation of PERSIANN-CDR for Meteorological Drought Monitoring over China” 2016, remote sensing article, vol. 8, no. 5, pp. 379.

[14] Anđelković G, Pavlović S, Đurđić S, Belij M, Stojković S,”tourism climate comfort index (tcci) – an attempt to evaluate the climate comfort for tourism purposes: the example of serbia” global nest journal, vol 18, 2016.

[15] Ghislain Dubois, Jean Paul Ceron, Clotilde Dubois, Maria Dolores Frias and Sixto Herrera,”Reliability and usability of tourism climate indices” Earth Perspectives, 2016, Volume 3, Number 1, Page 1.

[16] Bender, F. (Friedrich), Geology of Jordan.Berlin : Gebrüder Borntraeger, 1974, ISBN: 3443117074 9783443117078.

[17] The World Bank Group, “Climate Change Knowledge Portal, For Development Practitioners and Policy Makers” [Online]. Available: http://sdwebx.worldbank.org/climateportal/index.cfm?page=country_historical_climate&ThisRegion=Asia&ThisCCode=JOR .[Accessed Sept. 12, 2018].

[18] Iowa State University of Science and Technology “College of Agriculture_Department_of_Agronomy”[Online].Available:https://mesonet.agron.iastate.edu/sites/locate.php?network=JO__ASOS. [Accessed Sept. 12, 2018].

Journal Information

Metrics

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 106 106 8
PDF Downloads 83 83 14