Hostname: page-component-cd9895bd7-jn8rn Total loading time: 0 Render date: 2024-12-25T17:40:25.202Z Has data issue: false hasContentIssue false
  • English
  • Français

Selecting suitable sites for an Antarctic research station: a case for a new Chinese research station

Published online by Cambridge University Press:  14 February 2014

Pang Xiaoping ,
Liu Haiyan  and
Zhao Xi
Pang Xiaoping
Affiliation:
Chinese Antarctic Center of Surveying and Mapping, Wuhan University, 129 Luoyu Road, Wuhan, Hubei Province, China
Liu Haiyan
Affiliation:
School of Resource and Environmental Science, Wuhan University, 129 Luoyu Road, Wuhan, Hubei Province, China
Zhao Xi*
Affiliation:
Chinese Antarctic Center of Surveying and Mapping, Wuhan University, 129 Luoyu Road, Wuhan, Hubei Province, China
*
*[email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Site selection for Antarctic research stations is of great importance and is necessary to support Antarctic expeditions. Site selection is affected by both the scientific investigations planned and the expected life of stations following construction. In the site allocation process, an efficient spatial data management system is required to manage various criteria and a robust allocation method is important to handle decision uncertainties. The aim of this study was to build a criteria system and to conduct a site selection process with aid from geographical information systems (GIS) and the fuzzy analytical hierarchy process (FAHP). In considering the natural environment and building conditions, fifteen factors were used as multiple evaluation sub-criteria and grouped into four main criteria: scientific research, environment, logistical support and topography. Comparisons were made between potentially suitable areas and the locations of existing stations and camps to demonstrate fitness-for-use of the allocation results. Finally, the suitability map was applied to identify candidate sites for a new Chinese research station by considering the position of current stations and areas of scientific interest. This model offers a comprehensive methodology for decision-makers in the assessment of potential Antarctic research station sites.

Keywords

criteria systemfuzzy analytical hierarchy processgeographical information systemsite selection
Type
Biological Sciences
Information
Antarctic Science , Volume 26 , Issue 5 , October 2014 , pp. 479 - 490
Copyright
Copyright © Antarctic Science Ltd 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

AWI. 2005. Final comprehensive environmental evaluation of the proposed activities: construction of the Neumayer III Station, operation of the Neumayer III Station, dismantling of the existing Neumayer II Station. Bremerhaven: Alfred Wegener Institute of Polar and Marine Research, 130 pp.Google Scholar
Belspo. 2007. Final comprehensive environmental evaluation: construction and operation of the new Belgian Research Station, Dronning Maud Land, Antarctica. Brussels: Belgian Federal Science Policy Office, 94 pp.Google Scholar
Brent, A.C., Rogers, D.E.C., Ramabitsa-Siimane, T.S.M. Rohwer, M.B. 2007. Application of the analytical hierarchy process to establish healthcare waste management systems that minimise infection risks in developing countries. European Journal of Operational Research, 181, 403–424.Google Scholar
Buckley, J.J. 1985. Fuzzy hierarchical analysis. Fuzzy Sets and Systems, 17, 233–247.CrossRefGoogle Scholar
BAS. 2007. Final comprehensive environmental evaluation: proposed construction and operation of Halley VI research station, and demolition and removal of Halley V research station, Brunt Ice Shelf, Antarctica. Cambridge: British Antarctic Survey, 91 pp.Google Scholar
Chang, D.Y. 1996. Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 95, 649–655.Google Scholar
Chown, S.L., Lee, J.E., Hughes, K.A. et al. 2012. Challenges to the future conservation of the Antarctic. Science, 337, 158–159.Google Scholar
Convey, P., Hughes, K.A. Tin, T. 2012. Continental governance and environmental management mechanisms under the Antarctic Treaty System: sufficient for the biodiversity challenges of this century? Biodiversity, 13, 234–248.CrossRefGoogle Scholar
Cullather, R.I., Bromwich, D.H. van Woert, M.L. 1998. Spatial and temporal variability of Antarctic precipitation from atmospheric methods. Journal of Climate, 11, 334–367.Google Scholar
Erensal, Y.C., Öncan, T. Demircan, M.L. 2006. Determining key capabilities in technology management using fuzzy analytic hierarchy process: a case study of Turkey. Information Sciences, 176, 2755–2770.Google Scholar
Hossain, M.S., Chowdhury, S.R., Das, N.G., Sharifuzzaman, S.M. Sultana, A. 2009. Integration of GIS and multicriteria decision analysis for urban aquaculture development in Bangladesh. Landscape and Urban Planning, 90, 119–133.Google Scholar
NPI. 2004. Final comprehensive environmental evaluation for the upgrading of the Norwegian summer station Troll in Dronning Maud Land, Antarctica, to permanent station. Tromsø: Norwegian Polar Institute, 105 pp.Google Scholar
Kahraman, C., Ertay, T. Büyüközkan, G. 2006. A fuzzy optimization model for QFD planning process using analytic network approach. European Journal of Operational Research, 171, 390–411.Google Scholar
KOPRI. 2012. Final comprehensive environmental evaluation for the construction and operation of the Jang Bogo Antarctic research station, Terra Nova Bay, Antarctica. Incheon: Korea Polar Research Institute and Korea Environment Institute, 90 pp.Google Scholar
Lee, A.H.I., Chen, W.C. Chang, C.J. 2008. A fuzzy AHP and BSC approach for evaluating performance of IT department in the manufacturing industry in Taiwan. Expert Systems with Applications, 34, 96–107.Google Scholar
Leung, L.C. Cao, D. 2000. On consistency and ranking of alternatives in fuzzy AHP. European Journal of Operational Research, 124, 102–113.Google Scholar
Mikhailov, L. Tsvetinov, P. 2004. Evaluation of services using a fuzzy analytic hierarchy process. Applied Soft Computing, 5, 23–33.Google Scholar
Mohajeri, N. Amin, G.R. 2010. Railway station site selection using analytical hierarchy process and data envelopment analysis. Computers & Industrial Engineering, 59, 107–114.Google Scholar
NCAOR. 2010. Final comprehensive environmental evaluation of new Indian research base at Larsemann Hills, Antarctica. Goa: National Centre for Antarctic and Ocean Research, 51–90.Google Scholar
Saaty, T.L. 1980. The analytic hierarchy process: planning, priority setting, resource allocation. New York, NY: McGraw-Hill, 287 pp.Google Scholar
SCAR. 1993. SCAR Bulletin No. 110. Protocol on Environmental Protection to the Antarctic Treaty. Polar Record, 29, 256–275.Google Scholar
Steig, E.J., Schneider, D.P., Rutherford, S.D., Mann, M.E., Comiso, J.C. Shindell, D.T. 2009. Warming of the Antarctic ice-sheet surface since the 1957 International Geophysical Year. Nature, 457, 459–462. Corrigendum: Nature, 460, 766.Google Scholar
Tejedo, P., Justel, A., Benayas, J., Rico, E., Convey, P. Quesada, A. 2009. Soil trampling in an Antarctic Specially Protected Area: tools to assess levels of human impact. Antarctic Science, 21, 229–236.Google Scholar
Tin, T., Fleming, Z.L., Hughes, K.A., Ainley, D.G., Convey, P., Moreno, C.A., Pfeiffer, S., Scott, J. Snape, I. 2009. Impacts of local human activities on the Antarctic environment. Antarctic Science, 21, 3–33.Google Scholar
Vahidnia, M.H., Alesheikh, A.A. Alimohammadi, A. 2009. Hospital site selection using fuzzy AHP and its derivatives. Journal of Environmental Management, 90, 3048–3056.Google Scholar
van Laarhoven, P.J.M. Pedrycz, W. 1983. A fuzzy extension of Saaty's priority theory. Fuzzy Sets and Systems, 11, 199–227.Google Scholar
Wang, Y.M., Luo, Y. Hua, Z. 2008. On the extent analysis method for fuzzy AHP and its applications. European Journal of Operational Research, 186, 735–747.CrossRefGoogle Scholar
Yong, D. 2006. Plant location selection based on fuzzy TOPSIS. International Journal of Advanced Manufacturing Technology, 28, 839–844.Google Scholar