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Photometry is a convenient operational method for monitoring such dynamicallyevolving phenomena as wind waves. Nowadays machine learning allows one to avoidexplicit derivation of the solution to the problem, describing all theinstructions for transforming the input data into the final result. Instead, analgorithm is used to independently find solutions through the integrated use ofstatistical data, from which patterns are derived, on the basis of whichforecasts are made. An example of a problem for which a regular solution hastraditionally been applied is the prediction of wave height from the inputbrightness of a water surface. The task is complicated by the multitude ofpossible physical models and the need to apply calibration coefficients. Inthis paper, we solve the problem of how, basing on the obtained brightnessvalues and the corresponding heights, to train the neural network to furtherpredict the heights from the incoming brightness values with the greatestaccuracy.

Usage of single-camera video recording to measure sea surface roughness with machine learning methods