Comparison of ANN and SVR based models in sea level prediction for the Black Sea coast of Sinop

Abstract

Seawater level oscillations are very critical to coastal construction, flood prevention and human living conditions. However, it is difficult to accurately project the daily future for seawater level due to the effects of wind, precipitation and other atmospheric conditions. For this reason, in this paper, artificial intelligence (AI) based Artificial Neural Networks (ANN) and Support Vector Regression (SVR) methods are applied for the estimation of seawater level in Sinop Coast. In addition, Multiple Linear Regression (MLR) is used as a benchmarking model. In this study, coefficient of determination (R2) and root mean square error (RMSE) were applied as model evaluation criteria. Besides, 15 minutes (approximately 22 months) sea water level data of Sinop Station were collected and used as is. The findings revealed that the ANN model can predict the water level for 1st, 2nd, 3rd, 4th days with correlation coefficients (R2) of 0.84, 0.67, 0.64, 0.63, respectively, and the SVR model can predict for 1st, 2nd days with correlation coefficients (R2) of 0.86, 0.66, respectively.

Keywords

• ANN
• SVR
• Artificial Intelligence
• Black Sea
• Sinop coast

Sinop'un Karadeniz kıyısı için deniz seviyesi tahmininde YSA ve SVR tabanlı modellerin karşılaştırılması

Abstract

Deniz suyu seviyesi salınımları, kıyı inşaatı, taşkın önleme ve insan yaşam koşulları için çok büyük önem arz etmektedir. Ancak rüzgar, yağış ve diğer atmosferik koşulların etkileri nedeniyle deniz suyu seviyesinin günlük hareketini doğru bir şekilde tahmin etmek zordur. Bu nedenle bu çalışma bünyesinde Sinop Sahili'nde deniz suyu seviyesi tahmini için yapay zeka (AI) tabanlı Yapay Sinir Ağları (YSA) ve Destek Vektör Regresyon (DVR) yöntemleri uygulanmaktadır. Bunların yanında kıyaslama modeli olarak Çoklu Doğrusal Regresyon (ÇDR) kullanılmaktadır. Model değerlendirme kriteri olarak ise determinasyon katsayısı (R2) ve ortalama karesel hata (RMSE) yöntemleri kullanılmıştır. Bunlarla beraber, Sinop İstasyonu'nun 15 dakikalık (toplamda yaklaşık 22 aylık) deniz suyu seviyesi verileri toplanmış ve olduğu gibi kullanılmıştır. Sonuç olarak bulgular, YSA modelinin sırasıyla 0.84, 0.67, 0.64, 0.63 korelasyon katsayıları (R2) değerleri ile 1., 2., 3. ve 4. günler için su seviyesini tahmin edebildiğini ve DVR modelinin 1., 2. günler için sırasıyla 0.86, 0.66 korelasyon katsayıları (R2) değerleri ile tahmin edebildiğini ortaya koymuştur.

Keywords

• YSA
• DVR
• Yapay Zeka
• Karadeniz
• Sinop Sahili

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