Uluabat Gölü yüzey alanının zamansal değişim analizi üzerinden DSAS ve yapay sinir ağları modellerine göre gelecek tahminleri

Öz

Göller jeomorfolojik, hidrografik, klimatolojik ve antropojenik etkenlerle birçok özellik açısından değişimlere uğramaktadır. Bu çalışmanın amacı Uluabat Gölü’nün yüzey alanı ve kıyı çizgisi değişimini zamansal, mekânsal açıdan saptamak ve çeşitli tekniklerle farklı senaryolar kapsamında geleceğe dair öngörüler üretmektir. Çalışmada 1975-2020 arasında 5’er yıllık ile 2023 yılı ve 2022 yılındaki her aya ait birer Landsat uydu görüntüsü kullanılmıştır. Multispektral görüntüler üzerinden su indis analizleri ve eşik yöntemi ile göl yüzey alanı ve kıyı çizgisi çıkarımı yapılmıştır. Daha sonra gölde farklı değişimleri ve geleceğe dönük senaryoları üretmek için 1975-2023, 1985-2023 ve 2005-2023 dönemlerine Sayısal Kıyı Çizgisi Analiz Sisteminde (DSAS) yer alan NSM, SCE, EPR ve LLR istatistik analizleri uygulanmıştır. Dönem verileri temel alınarak Kalman Filter yöntemi ile 10 ve 20 yıllık göl yüzey alanı tahminleri 3 farklı senaryo kapsamında yapılmıştır. Uzun dönem ve aylık kıyı değişimi, DSAS analizleri, batimetri verisi ve geçiş olasılıklarıyla toplamda 8 farklı kriter, Yapay Sinir Ağları (YSA) yönteminde analiz edilmiş ve göl yüzey alanı tahminleri tekrar üretilmiştir. Bulgular, gölün yüzey alanının 1975’den 2023’e % 20 azaldığı ve güney kesimdeki deltada 3000 m’lik kıyı ilerlemesinin olduğunu göstermektedir. YSA modelinde 2023’e göre göl yüzey alanının 2033 yılında % 8, 2043 yılında ise % 13,6 küçüleceği tahmin edilmiştir.

Anahtar Kelimeler

• Sulak alan değişimi
• DSAS
• Yapay Sinir Ağları (YSA)
• Uzaktan Algılama
• Uluabat Gölü

Future predictions according to DSAS and artificial neural network models based on temporal change analysis of Uluabat Lake surface area

Abstract

Lakes undergo changes in terms of many characteristics due to geomorphological, hydrographic, climatological and anthropogenic factors. The aim of this study is to determine the temporal and spatial changes in the surface area and shoreline of Lake Uluabat and to produce predictions for the future under different scenarios with various techniques. In the study, Landsat satellite images were used at 5-year intervals between 1975 and 2020, and one Landsat satellite image for each month in 2023 and 2022. Water indexs analyses and threshold method were used to infer lake surface area and shoreline on multispectral images. Then, NSM, SCE, EPR and LLR statistical analyses in the DSAS tool were applied for the periods 1975-2023, 1985-2023 and 2005-2023 to produce different changes in the lake and future scenarios. Based on the period data, 10 and 20-year lake surface area estimates were made within the scope of 3 different scenarios with the Kalman Filter method. Long-term and monthly coastal change, DSAS analyses, bathymetry data and transition probabilities, 8 different criteria in total, were analysed in Artificial Neural Networks (ANN) method and lake surface area estimates were reproduced. The findings show that the surface area of the lake decreased by 20% from 1975 to 2023 and there is a 3000 m shore advance in the delta in the southern part. The ANN model predicts that the lake surface area will shrink by 8% in 2033 and 13.6% in 2043 compared to 2023.

Keywords

• Wetland change
• DSAS
• Artificial Neural Networks (ANN)
• Remote Sensing
• Uluabat Lake

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