Integration of Physical Diffusion Modelling and Machine Learning Methods in Coastal Morpholithodynamics (Sakhalin Example)

It has been shown that against the backdrop of a global trend towards increased reform of coastal areas due to climate change and rising sea levels, quantitative forecasts of coastal morpholithodynamics and engineering and environmental solutions based on the latest scientific knowledge are needed. The paper presents a method for automated morpholithodynamic classification of coastal profiles based on the integration of physical diffusion modelling and machine learning methods. The methodological capabilities presented open up avenues for further research aimed at creating tools for short- and medium-term forecasts of morpholithodynamic coastal processes. High accuracy allows for reconnaissance and assessment studies and determines the feasibility of using labour-intensive deterministic physical modelling to obtain quantitative forecasts of deformations and design engineering measures.