SavedAdd your journal

Journals / International Journal of Environment and Geoinformatics / 2023 / Cilt: 10 - Sayı: 1

Water-body Segmentation in Heterogeneous Hydrodynamic and Morphodynamic Structured Coastal Areas by Machine Learning

İrem GÜMÜŞÇÜ
Furkan ALTAŞ
Beril TÜRKEKUL
Hasan Alper KAYA
Fırat ERDEM
Tolga BAKIRMAN
Bülent BAYRAM
Pages
100–110
Publication Date
2023-03-19
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Experimental results show that the utilized machine learning methods provide satisfactory results for combinations involving the NIR band in all study areas.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Coastal areas constitute the most important part of the world when considered in terms of their socio-economic and natural values.
📊
Key Findings & Data:Measuring and monitoring the coastal areas accurately is an important issue for coastal management.
💡
Conclusion & Impact:Experimental results show that the utilized machine learning methods provide satisfactory results for combinations involving the NIR band in all study areas.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Sentinel-2 · shoreline extraction · Support Vector Machines

Abstract

Coastal areas constitute the most important part of the world when considered in terms of their socio-economic and natural values. Measuring and monitoring the coastal areas accurately is an important issue for coastal management. Compared to ground-based studies, remote sensing applications enriched with machine learning algorithms such as Random Forest (RF) and Support Vector Machine (SVM) provide significant benefits in terms of cost, time, and size of the study area. Within the scope of this study, Sentinel-2 images for five coastal areas located in Turkey with different morphological and hydrodynamic properties were classified as land and water-bodies using SVM and RF algorithms. Water-body segmentation results of the SVM and RF classification for the different band combinations of Sentinel-2 images have been compared. The reasons affecting the results of the accuracy analysis were examined in accordance with the geography of each area. Experimental results show that the utilized machine learning methods provide satisfactory results for combinations involving the NIR band in all study areas.

🔖

Cite This Article

GÜMÜŞÇÜ, İ., ALTAŞ, F., TÜRKEKUL, B., KAYA, H. A., ERDEM, F., BAKIRMAN, T., BAYRAM, B. (2023). Water-body Segmentation in Heterogeneous Hydrodynamic and Morphodynamic Structured Coastal Areas by Machine Learning. International Journal of Environment and Geoinformatics, Cilt: 10 - Sayı: 1, 100–110. https://ajindex.com/articles/886486

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (11)

  1. Ai, B., Zhang, R., Zhang, H., Ma, C., & Gu, F. (2019). Dynamic process and artificial mechanism of coastline change in the Pearl River Estuary. Regional Studies in Marine Science, 30, 100715. Aksu A. E., Piper D. J. W. & Konak T. (1987). Quaternary Growth Patterns of the Büyük Menderes and Küçük Mederes Deltas, Western Turkey. Marine Geology, 76, 89-104.
  2. Ataol, M., Kale, M. M., & Tekkanat, İ. S. (2019). Assessment of the changes in shoreline using digital shoreline analysis system: a case study of Kızılırmak Delta in northern Turkey from 1951 to 2017. Environmental Earth Sciences, 78(19), 579. Balázs, B., Bíró, T., Dyke, G., Singh, S. K., & Szabó, S. (2018). Extracting water-related features using reflectance data and principal component analysis of Landsat images. Hydrological sciences journal, 63(2), 269-284.
  3. Bamdadinejad, M., Ketabdari, M. J., & Chavooshi, S. M. H. (2021). Shoreline Extraction Using Image Processing of Satellite Imageries. Journal of the Indian Society of Remote Sensing, 1-11.
  4. Bangira, T., Alfieri, S. M., Menenti, M., & Van Niekerk, A. (2019). Comparing thresholding with machine learning classifiers for mapping complex water. Remote Sensing, 11(11), 1351. Belgiu, M., & Drăguţ, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS journal of photogrammetry and remote sensing, 114, 24-31.
  5. Berkün, M., Anılan, T., & Aras, E. (2010). Doğu Karadeniz Bölgesi’nde Sediment Taşınması ve Kıyı Erozyonu Etkileşimleri. Türkiye Mühendislik Haberleri (TMH), 3(4), 461-462.
  6. Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.
  7. Cao, W., & Wong, M. H. (2007). Current status of coastal zone issues and management in China: a review. Environment International, 33(7), 985-992.
  8. Choung, Y. J., & Jo, M. H. (2017). Comparison between a machine-learning-based method and a water-index-based method for shoreline mapping using a high-resolution satellite image acquired in Hwado Island, South Korea. Journal of Sensors, 2017.
  9. Coiman, A. (2020). Shoreline evolution of Valencia lake and land use and land cover changes in Zamora municipality, Aragua state, Venezuela, period 1986-2016. International Journal of Environment and Geoinformatics, 7(3), 305-318.
  10. Çiftçi, N. S. (2011). Türkiye Denizleri Açık Sularının Ekim 2000'deki Fitoplankton Kompozisyonu. Süleyman Demirel Üniversitesi Eğirdir Su Ürünleri Fakültesi Dergisi, 7(2), 23-36.
  11. Demir, N., Oy, S., Erdem, F., Şeker, D. Z., & Bayram, B. (2017). Integrated shoreline e