## 4 Discussion  
Water temperature in rivers is an important property determining its quality, yet data regarding it in many river systems are scarce or non-existent [43]. According to [PERSON] et al. [44], in regions poor in data, modelling water temperature in rivers is necessary to predict potential stress-inducing factors for endangered species. Further, the same authors predicted water temperature (for three rivers in Canada) based on air temperature from nearby meteorological stations, and revealed that the best model proved to be linear regression using a 5-day delay in air temperature. [PERSON] et al. [45] used modified sinusoidal functions and sinusoidal wave functions (MSSWF) for the estimation of hourly water temperatures in rivers. It was, among other things, determined that the distance of the river monitoring site from the meteorological station has little effect on the efficiency of particular models (in the case of a distance smaller than 300 km). In the case of the analysed rivers, it was also determined that the distance of the site of air temperature measurement from the hydrological station (at maximum not reaching 130 km) has no effect on the accuracy of the models. Possibilities not achievable in hydrology to date are offered by the implementation of an approach based on the assumptions of artificial intelligence. Machine learning is increasingly frequently applied due to its ability to model complex and non-linear dependencies between river water temperature and its predictors [46]. Machine learning models offer an empirical approach to predicting water temperature with a high level of accuracy [47], and the advantages of models of artificial neural networks in modelling water temperature involve their simplicity of use, good ability to generalise, and flexibility [48]. Due to all the above, it is possible to obtain results at a good adjustment level, as confirmed by the results obtained in the paper referring to the reconstruction of the discontinued in situ measurements. According to current studies, the scope and availability of input data and the adopted modelling methodology have a different character. The use of classic models of machine learning for forecasting monthly water temperatures (USA) showed that such an approach can be applied for the spatial and temporal modelling of the majority of locations, with an accuracy of \(<\)1 \({}^{\circ}\)C [49]. Water temperature modelling by means of neural networks and multiple linear regression in the case of the Catamaran Brook (Canada) showed that neural networks offered better results in terms of adjustment, but an inconsiderable difference in results suggests that both approaches are equally good [50]. The development of a model composed of artificial neural networks (ANN) for predicting mean daily water temperature in individual sections of streams showed that the most important predictors were mean daily air temperature, prior 7-day mean air temperature, and network catchment area [51]. The analysis of the dependency between mean daily air temperature and water temperature in the Drava River (Croatia) by means of linear regression, stochastic modelling, non-linear regression, and multilayer perceptron showed that the latter are considerably better models in the estimation and prediction of mean daily river temperature [48]. The findings are in accordance with results presented in the present paper, where the multilayer perceptrone network also obtained the best results against the background of other proposed methodical solutions. Along with this variable that is commonly used in this type of research on air temperature [27; 52], this paper employed additional information regarding water temperature in lakes (unlike in the case of some rivers--subject to continuous monitoring). The combination of both input attributes permitted obtaining even better results in the reconstruction of the missing water temperature records.