Landsat-8 Görüntüleri Kullanılarak Mogan Gölü’nde Askıdaki Katı Madde Konsantrasyonunun Hibrit RF–ANN–CNN Modelleme Yaklaşımıyla Tahmin Edilmesi

Abstract

Göl ekosistemlerinin sürdürülebilir yönetimi için su kalitesinin sürekli ve güvenilir bir şekilde izlenmesi büyük önem taşımaktadır. Askıdaki katı madde (AKM) konsantrasyonu, su kalitesinin değerlendirilmesinde temel bir gösterge olmakla birlikte, geleneksel ölçüm yöntemleri yüksek maliyetli olup sınırlı mekânsal ve zamansal kapsama sahiptir. Uzaktan algılama teknikleri, geniş alanları kapsayan ve tekrarlanabilir gözlemler sunarak bu kısıtları önemli ölçüde aşmaktadır. Bu çalışmada, Landsat-8 OLI uydu görüntüleri kullanılarak Mogan Gölü’ndeki askıdaki katı madde konsantrasyonları hibrit bir uzaktan algılama yaklaşımıyla tahmin edilmiştir. İlk aşamada, sınırlı sayıda mevcut olan arazi ölçümleri Rastgele Orman (Random Forest, RF) modeli kullanılarak çoğaltılmış ve daha güçlü bir eğitim veri seti oluşturulmuştur. Ardından, bu veri seti; B2, B3 ve B4 bantları ile spektral indisler kullanılarak Yapay Sinir Ağı (ANN) ve Evrişimli Sinir Ağı (CNN) modellerinin eğitilmesinde kullanılmıştır. Elde edilen sonuçlara göre, CNN modeli en yüksek doğruluğu sağlamış (R² = 0.97, RMSE = 0.17, MAE = 0.13) ve göl genelinde askıdaki katı madde dağılım haritalarının üretilmesinde kullanılmıştır. Genel olarak, önerilen RF–ANN–CNN çerçevesi, arazi verilerinin sınırlı olduğu küçük göllerde askıdaki katı madde tahminini önemli ölçüde iyileştirmekte olup, uzaktan algılama tabanlı derin öğrenme yöntemlerinin sürdürülebilir su kalitesi izleme çalışmalarında güçlü bir potansiyele sahip olduğunu ortaya koymaktadır.

Keywords

• Uzaktan Algılama
• Askıdaki Katı Madde
• Derin Öğrenme
• Landsat-8 OLI
• Mogan Gölü

A Hybrid RF–ANN–CNN modelling approach for suspended sediment estimation in Mogan Lake using Landsat-8 imagery

Abstract

Continuous and reliable monitoring of water quality is crucial for the sustainable management of lake ecosystems. Suspended Solids (SS) concentration is a key indicator, yet traditional measurement methods are costly and offer limited spatial and temporal coverage. Remote sensing addresses these constraints by providing wide area, repeatable observations. This study estimated SS concentrations in Lake Mogan using a hybrid remote sensing approach with Landsat-8 OLI imagery. First, scarce in-situ data were augmented with a Random Forest (RF) model to create a more robust training set. This dataset then supported Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) models, using B2, B3, and B4 bands along with spectral indices. The CNN model yielded the highest accuracy (R² = 0.97, RMSE = 0.17, MAE = 0.13) and was used to generate lake wide SS maps. Overall, the RF–ANN–CNN framework significantly improves SS estimation in small lakes with limited field data, demonstrating the strong potential of remote-sensing-based deep learning for sustain-able water-quality monitoring.

Keywords

• Remote Sensing
• Suspended Solids
• Deep Learning
• Landsat-8 OLI
• Lake Mogan

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