Assessment of the Kalman filter-based future shoreline prediction method

Ciritci D., Türk T.

INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY, cilt.17, ss.3801-3816, 2020 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 17
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1007/s13762-020-02733-w
  • Dergi Adı: INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Agricultural & Environmental Science Database, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), Biotechnology Research Abstracts, CAB Abstracts, Compendex, Environment Index, Geobase, INSPEC, Pollution Abstracts, Veterinary Science Database
  • Sayfa Sayıları: ss.3801-3816
  • Anahtar Kelimeler: Shoreline change analysis, Shoreline prediction, GIS, Kalman filter, Remote sensing
  • Sivas Cumhuriyet Üniversitesi Adresli: Evet

Özet

The prediction of the future position of the shoreline is of great importance in planning studies in coastal areas, in making effective decisions in coastal management, and in determining changes occurring on the coast. In this study, coastal change analyses were performed in two different study areas (the Gulf of Izmit and the Goksu Delta) by using satellite images of different dates, and the accuracy of the Kalman filter-based future shoreline prediction method was determined by statistical methods. In this context, by using the 19-period Landsat satellite image belonging to different dates between 1975 and 2019 for the Gulf of Izmit and the 10-period Landsat satellite image belonging to different dates between 1984 and 2018 for the Goksu Delta, shorelines were extracted automatically, and coastal changes were analyzed at a 95% CI by the statistical methods of end point rate, linear regression rate, and weighted linear regression rate (WLR). Afterward, the shorelines extracted automatically on the determined dates were compared with 10-year and 20-year predicted shorelines by the Kalman filter-based prediction method, and their accuracy was statistically analyzed. As a result, the 10-year predicted shorelines by the WLR method were found to provide the highest accuracy.