  Photometry is a convenient operational method for monitoring such dynamically
evolving phenomena as wind waves. Nowadays machine learning allows one to avoid
explicit derivation of the solution to the problem, describing all the
instructions for transforming the input data into the final result. Instead, an
algorithm is used to independently find solutions through the integrated use of
statistical data, from which patterns are derived, on the basis of which
forecasts are made. An example of a problem for which a regular solution has
traditionally been applied is the prediction of wave height from the input
brightness of a water surface. The task is complicated by the multitude of
possible physical models and the need to apply calibration coefficients. In
this paper, we solve the problem of how, basing on the obtained brightness
values and the corresponding heights, to train the neural network to further
predict the heights from the incoming brightness values with the greatest
accuracy.
