Implementation of linear regression and neural network models for local climate change assessment and forecasting based on time series data

Abstract

Climate change studies are based on the data processing of the following time series for number of Ukrainian cities: daily precipitation and air temperature from the site of the European Climate Assessment & Dataset (ECAD number of outputs is 1; number of neurons in the hidden layer is 5; activation function type is sigmoid with the given slope 1) provided the consistent forecast with the horizon of 120 months. Low values of the maximum and average relative errors of the neural network model were achieved on the training set (4.45·10-2 and 2.99·10-4, respectively) and on the test set (3.60·10-2 and 5.69·10-3, respectively). Similarly, for the time series of monthly CO2 emissions for the Kyiv city after December 1, 2013, the predictive neural network model [240 × 5 × 1] provided consistent forecast with the horizon of 60 months. In general, the time series of monthly CO2 emission values are characterized by much smaller values of the consistent forecast horizon in comparison with the time series of the average monthly temperature, at least when using predictive neural network models.