## 1 Introduction
Later, comparison of a look-up table approach and machine learning methods applied for gap-filling of methane fluxes [34] demonstrated that decision tree algorithms performed the best in cross-validation experiments. A multiple gap-filling technique for time series of trace gases fluxes [44] and providing ensemble results of gap-filled sums will help to minimize the influence of a single technique and thus lead to a more robust flux aggregation. A full-factorial scheme proposed by [32] for gap-filling of daytime evapotranspiration can be produced hourly and daily with superior quality compared to the existing typical gap-filling methods. Since the variations of meteorological variables are complex and nonlinear, machine learning models are powerful when implemented for problems whose resolutions require knowledge that is hard to specify [27]. Machine learning approaches could be considered as an effective and efficient technique to model meteorological processes, based on their effectiveness in modelling dynamic systems in a variety of applications of science and engineering. The data-driven models have proven to be better performance solutions in runoff modelling and flood prediction [24], terrestrial water storage [23], Arctic sea ice [46; 47], incoming solar irradiance [30], precipitation [29], and other meteorological variables [14; 26; 27; 48]. Comparison of an artificial neural network, random forest, decision tree, extreme gradient boosting, and long short-term memory algorithms [30] for the forecast of photovoltaic solar power output shows that that NNs are the most reliable and applicable algorithms resulting in the best mean absolute error, root mean squared error, and coefficient of determination. [PERSON] et al. [27] indicated that in either NN architecture, there is good potential to predict daily and monthly meteorological variable values with an acceptable accuracy. Rainfall forecasting is a complicated task related to high spatial-temporal variability of precipitations. Machine learning algorithms of boosted decision tree regression and decision forest regression were successfully applied [29] for the prediction of daily, weekly, 10-day, and monthly rainfall sums at Terengganu, Malaysia. A gradient-boosting-based model applied to the Arctic sea ice area [46] produced the most unbiased, precise, and robust estimates when compared to alternative estimates, such as monthly mean albedo values or estimates from monthly linear regression models. Integration of machine learning techniques (deep-learning neural networks, generalized linear model, and gradient boosting machine) and eight climatic and hydrological input variables was successfully applied [23] to fill gaps and reconstructed the terrestrial water storage at both global grid and basin scales. The main limitations of these more sophisticated gap-filling methods are the lack of standard tools for evaluating their performances and a non-standardized application. Implementation of a specific gap-filling method should be preceded by the investigation and comparison of the various techniques most suitable for application, taking into consideration the structure of available meteorological time series. The motivation of the present study was in the development of continuous time series for automated weather station operation since 2010 in a typical West Siberian peatland in Russia. The obtained in situ data records have large and frequent gaps. The present work is aimed to compare various regression models and an artificial neural network for the reconstruction of a temporal series of meteorological data and to choose the model for further application. The model comparison should be based on objective numerical estimations.