A recurrent neural network model of long short-term memory (LSTM) type implemented to predict atmospheric air pollution by PM2.5 particles. Based on the distribution of meteorological values and concentrations of main air pollutants known from observations over the previous 6 hours, the task was set to predict PM2.5 concentrations for the next 3 hours. The total value of the average absolute error over the whole forecast was 2.35 μg/m3.
The model of a multilayer perceptron has been developed and implemented to predict precipitation in a few hours based on the current state of meteorological parameters. The estimate of the model accuracy was 0.86, i.e. 86% of precipitation or no precipitation forecasts are predicted correctly.
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