## 3 Results
The comparison of corresponding groups of models by means of a non-parametric U Mann-Whitney test, grouped based on a range of input data used for the reconstruction of water temperatures in rivers based on values of standard error, maximum error, root mean square error, Nash-Sutcliffe model efficiency coefficient, and mean absolute percentage error, suggests that in all cases, the values were lower for multilayer perceptrone network models than for multiple linear regression models (differences significant at a level of 0.05). The values of the coefficient of determination (R2) and Nash-Sutcliffe model efficiency coefficient statistics were higher for multilayer perceptrone network models than for multiple linear regression models (differences significant at a level of 0.05). The comparison of groups of multilayer perceptrone network/g and random forest regression/g models (reconstruction based on water temperatures in the lake and air temperatures from three meteorological stations) shows that maximum error and root mean square error values averaged 1.52 \({}^{\circ}\)C and 5.03%, and 1.97 \({}^{\circ}\)C and 6.43%, respectively. Differences between maximum error and root mean square error values were significant at a level of 0.05. The standard error values averaged 0.47 \({}^{\circ}\)C for the multilayer perceptrone network/g model, and 0.59 \({}^{\circ}\)C for the random forest regression/g model, and differed at a significance level of 0.10. The remaining values of the Nash-Sutcliffe model efficiency coefficient and the mean absolute percentage error, and the coefficient of determination and Nash-Sutcliffe model efficiency coefficient, were respectively lower and higher for multilayer perceptrone network/g models, although the differences were not as statistically significant. The obtained results point to the advantage of the multilayer perceptrone network method over the multiple linear regression method. Differences between the multilayer perceptrone network/g and random forest regression/g models are smaller, with a slight advantage of the multilayer perceptrone network model. The analysis of the scope of data permitting the most credible prediction of water temperatures in rivers was performed by means of the multilayer perceptrone network method. The obtained results show that the best results are generated by models from the multilayer perceptrone network/g group, where the reconstruction is based on lake water temperatures and air temperatures from three meteorological stations. Mean values of root mean square error provide the basis for systematising multilayer perceptrone network models in the following order: g \(<\) e \(<\) c \(<\) a \(<\) b \(<\) f \(<\) b. Similar results were obtained based on the remaining statistical measures describing the range of prediction errors. Values of statistics describing the quality of the models' coefficient of determination and Nash-Sutcliffe model efficiency coefficient were at a high level in all cases, and ranged from 0.9865 to 0.9935, and from 0.9820 to 0.9925. The analysis of differences in values of standard error, maximum error, root mean square error, Nash-Sutcliffe model efficiency coefficient, and mean absolute percentage error between particular groups of multilayer perceptrone network models points to the advantage of models employing a mixed set of input data, i.e., lake water temperature and air temperature. The worst results of the estimation have been provided by models based only on a set of data regarding air temperatures.