Aim. In the present investigation artificial neural network (ANN) and ARIMA-model are compared for forecasting of data of colour of water.Methods. Data corresponds to the colour of water of groundwater and drinking water of water intake of south-east region of the Republic of Belarus. The definition of colour was carried out for the period from 2009 to 2017. twice a day, the time series of values included 5215 values. The parameters of the models were estimated by 85% of the time series values, and the remaining 15% of the values (the test period) compared the forecast values with the actual ones. Optimal configurations of ARIMA-models were determined from the results of comparing the averaged values of the root mean squared errors (RMSE); optimal configurations of ANN were determined from the results of comparing the averaged values of RMSE and correlation coefficients (CC) on the test periods.Results. Comparison of forecasting methods was carried out on the basis of the averaged values of mean absolute error and mean relative error on the test periods. It was revealed that ANN allows to obtain the predicted values of colour of water more accurate than ARIMA-model.Main conclusions. Software implementation of ANN in the MATLAB environment empowers with sufficient accuracy get forecast values of groundwater and drinking water for 100 values.
Aim. Comparison of water quality according to 19 indicators: odour at 20°C, odour at 60°C, taste, colour, turbidity, total iron, permanganate oxidation, dry residue, total hardness, oil products, surfactants, phenolic index, nitrates (NO3‐), chlorides (Сl‐), fluorides (F‐), sulphates (SO42‐), zinc (Zn2+), copper (Сu, total), pH value of two infiltration water intakes in the south‐eastern region of the Republic of Belarus. Identification and analysis of linear trends, and determination of trends in the dynamics of indicator values. Material and Methods. As initial data we used the results of quarterly measurements of the values of borehole water indicators of the infiltration water intakes. Results. By comparing the relative concentrations of the mean annual values of the studied quality indicators for two water intakes, it was revealed that the priority indicators are odour at 20°C, odour at 60°C, taste, chromaticity, turbidity, and iron. By comparing the relative concentrations of total iron, it was found that the concentration in the wells of WI 1 is much higher than in the wells of WI 2, probably due to the presence of rocks and minerals from moraine and fluvioglacial complexes of glacial deposits. For other indicators the values were approximately on the same level. Conclusion. By constructing matrices of pair correlation and geographic proximity for each of the priority indicators, well groups were identified whose water quality is interrelated and which were subsequently aggregated as a single group.
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