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Coastal Scenery Assessment by Means of a Fuzzy Logic Approach

  • Ayşen ErginEmail author
Chapter
Part of the Coastal Research Library book series (COASTALRL, volume 26)

Abstract

Landscape is a major element affecting people’s life quality and coastal landscape evaluation is strongly rooted in the man-environment tradition. Coastal areas, all over the world, are under threat due to the conflicting requirements that rely on natural scenery of such as habitation, recreation, and industry. Since ‘coastal scenery’ is a natural resource, it has to be evaluated in an objective and quantitative way to provide a means of comparison against coastal activities and for environmental impact assessments. This chapter presents an evidence-based methodology called ‘Coastal Scenic Evaluation System (CSES)’. It is a technique that can be used not only for landscape preservation and protection, but also as scientific tool for envisaged coastal management and future development based upon plans formulated by an evidence-based approach. The results provide base-line information for a sound coastal management decision especially regarding intensive urban and industrial developments. CSES uses fuzzy logic to reduce subjectivity on decisions and obtain a quantitative evaluation of public survey research on 26 coastal scenic parameters having both physical and human perceptual characteristics. The weights of the scenic parameters were estimated by public survey questionnaires for Turkey, UK, Malta and Croatia and via consultations with coastal experts from the above mentioned four countries and Australia, Ireland, USA and Japan. Fuzzy logic mathematics was used to calculate a coastal scenic evaluation index (D) from the checklist of 26 scenic parameters by using the attributed weights of the parameters which enabled to categorize scenic values of the coastal areas into five distinct classes.

Keywords

Coastal Scenery Scene Assessment Public Survey Scenic Value Mathematical Fuzzy Logic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Civil Engineering, Coastal Engineering DivisionMiddle East Technical UniversityAnkaraTurkey

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