Forecasting instantaneous sea level changes with machine learning methods in Aegean Sea

Abstract

Forecasting instantaneous sea-level is of great importance in terms of determination of geodetic vertical datum and updating, conservation of coastal areas, monitoring coastal ecosystems, maintenance, and planning of coastal structures, monitoring of climate change effects. Traditional methods used for instantaneous sea level estimation are often based on linear assumptions. However, contributors to sea levels are very various and their effects vary from region to region. Generally, they have complex and nonlinear dependence structures. Therefore, nonlinear sea-level cannot be determined with high precision using linear models. Recently, machine learning prediction methods have been frequently used in the modelling of complex dependency structures between variables. Within the scope of this study, to predict the instantaneous sea level with high accuracy and to compare linear estimation methods with nonlinear estimation methods, the Multiple Linear Regression (MLR) linear model, Support Vector Regression (SVR) non-linear model, and Random Forest Regression (RFR) non-linear model algorithms were used, and their prediction performances were compared. As a result of the study, the highest prediction performance for instantaneous sea level was obtained with RFR, and the lowest prediction performance was obtained with the MLR method. As a result, it has been shown that instantaneous sea level can be predicted with high precision using RFR with the features used in this study, and the linear prediction models are insufficient in modelling the complex dependency structure of instantaneous sea level.  

Keywords

• Sea level
• Machine learning
• Aegean Sea
• Multiple linear regression
• Support vector
• Random forest

Ege Denizi’nde makine öğrenimi yöntemleri ile anlık deniz seviyesi değişimlerinin tahmini

Abstract

Anlık deniz seviyesinin tahmini, jeodezik düşey datumun belirlenmesi ve güncellenmesi, kıyı alanlarının korunması, kıyı ekosistemlerinin izlenmesi, kıyı yapılarının planlanması ve bakımı, iklim değişikliği etkilerinin gözlenmesi açısından büyük önem taşımaktadır. Anlık deniz seviyesi tahmini için kullanılan geleneksel yöntemler genellikle doğrusal varsayımlara dayanmaktadır. Ancak deniz seviyesini etkileyen faktörler çok çeşitlidir ve etkileri bölgeden bölgeye değişmektedir. Genellikle doğrusal olmayan ve karmaşık bağımlılık yapılarına sahiptirler. Bu nedenle, doğrusal olmayan deniz seviyeleri doğrusal modeller kullanılarak yüksek duyarlıkta belirlenemez. Makine öğrenimi tahmin yöntemleri ise, son zamanlarda değişkenler arasındaki karmaşık bağımlılık yapılarının modellenmesinde sıklıkla kullanılmaktadır. Bu çalışma kapsamında, anlık deniz seviyesini yüksek doğrulukta tahmin etmek ve doğrusal tahmin yöntemleri ile doğrusal olmayan tahmin yöntemlerini karşılaştırmak amacıyla makine öğrenimi tahmin yöntemlerinden Çoklu Doğrusal Regresyon (ÇDR) doğrusal modeli, Destek Vektör Regresyonu (DVR) doğrusal olmayan model ve Rastgele Orman Regresyonu (ROR) doğrusal olmayan model algoritmaları kullanılmış ve tahmin performansları karşılaştırılmıştır. Çalışma sonucunda anlık deniz seviyesi için en yüksek tahmin performansı ROR ile elde edilmiş olup, en düşük tahmin performansı ise ÇDR yöntemi ile elde edilmiştir. Sonuç olarak anlık deniz seviyelerinin çalışmada kullanılan öncül bilgiler ile ROR kullanılarak yüksek hassasiyette tahmin edilebileceği ve doğrusal tahmin modelinin anlık deniz seviyesinin karmaşık bağımlılık yapısının modellenmesinde yetersiz olduğu gösterilmiştir.

Keywords

• Deniz Seviyesi
• Makine öğrenimi
• Ege Denizi
• Çoklu doğrusal regresyon
• Destek vektör
• Rastgele orman

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