An automated approach for determination and prioritization of urban potential risk areas within the scope of superstructure

AKSOY E., SELİM S.

NATURAL HAZARDS, cilt.103, sa.1, ss.1077-1091, 2020 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 103 Sayı: 1
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1007/s11069-020-04026-4
  • Dergi Adı: NATURAL HAZARDS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, IBZ Online, PASCAL, Aerospace Database, Agricultural & Environmental Science Database, Aquatic Science & Fisheries Abstracts (ASFA), CAB Abstracts, Communication Abstracts, Environment Index, Geobase, INSPEC, Metadex, PAIS International, Pollution Abstracts, Sociological abstracts, Veterinary Science Database, DIALNET, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.1077-1091
  • Anahtar Kelimeler: Geographic information systems, Multi-criteria decision analysis, Risk assessment, Spatial planning, Superstructure, ANALYTIC HIERARCHY PROCESS, SUSTAINABILITY ASSESSMENT, MULTICRITERIA ANALYSIS, DECISION, GIS, SELECTION, AHP
  • Akdeniz Üniversitesi Adresli: Evet

Özet

In the face of natural disasters, it is important to determine the potential risk areas in a fast and high accuracy and to take necessary precaution in urban areas. In this study, the potential risk areas according to the existing legislation are determined and prioritized by using geographic information systems and multi-criteria decision-making analysis. The main aim is to develop an automatic approach for assessing quickly the risk status of superstructure. This study was carried out in the south Mediterranean coast of Turkey in Antalya, which is the fifth largest city in terms of population. The rapid population growth and uncontrolled urbanization were effective in the selection of this region. In addition, the areas where the developed method is applied are dense in terms of slum and non-regulatory structures. As a result, 8.49 ha of the application area was detected to be high risky, 1.22 ha was risky, 9.51 ha was low risky, and 6.24 ha was minimum risk. Hence, the ratio of the areas in the very risky class to the total area is 33%, the ratio of the area in the risky class is 4%, the ratio of the area in the low risk class is 38%, and the rate of the area that appears to be the minimum risk is 25%. In this context, risk classes were mapped and priority intervention areas were determined. This study proposes and applies a method that will help and guide the related management and implementation level, especially urban and regional planners in urban settlements, in determining the potential risk areas due to superstructure.