<p>Within the framework of meteorology and oceanology, the importance of the cloud mass and the type of clouds cannot be underestimated. When describing and studying weather, precipitation and the movement of air masses over the ocean, the amount and type of clouds determines the flows of precipitation, their intensity, helps to predict the weather and the content of various impurities in the air, which makes the study of the properties of cloud cover one of the key aspects of meteorological and oceanological research.</p><p>The types of clouds are determined by the specialist, visually comparing the picture of the sky over the ocean with the guideline documents, the use of which reduces the possibility of the human factor affecting the determination of these parameters.</p><p>For an accurate study, study of the dynamics and dependence of climatic models on the conditions of cloud types, long-term measurements of the same type and the continuity of their methods are required. However, all these data are very unevenly distributed over the Earth's surface, and the number of ship observations is greatly reduced.</p><p>Thus, taking into account the importance of reliable determination of data related to cloudiness and the problems of their accuracy, the relevance and need to automate the determination of cloud types are obvious.</p><p>As a result of the work, an algorithm was obtained that allows classifying cloud types based on photographs taken during long-term sea expeditions.</p>
Using neural networks to model long-term dependencies in occupancy behavior
