## 6 Discussion  
Among the attributes derived from satellite images, there was significant variability in their contribution to the accuracy of the resultant regression models. Moreover, there were significant differences in their contribution not only for different target attributes but also for different attribute selection methods. In general, the highest accuracy was typically achieved by random forest models, which we plan to use in the near future when we obtain additional attributes, either extracted from the NEIS system or derived from acoustic emissions of polluted insulators. We also plan to use new satellite-derived attributes, since we do not think our present ones were able to extract all of the useful information from satellite images. From the perspective of attribute selection methods, the highest model accuracies were achieved with FSRF-Test, which proved to be the most suitable in four cases (for S and Sr target attributes). For the target attribute g02, the forward selection and the relief methods achieved better results, each in one case. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Linear Model** & **Gaussian Process** & **SMO Regression** & **M5P Tree** & **Random Forest** \\ \hline \multicolumn{6}{c}{Models using attributes SHMU, Satellites (Relief 20 attr), without Day of Year} \\ \hline Correlation coefficient & 0.4135 & 0.3942 & 0.3798 & 0.3853 & 0.4601 \\ Relative absolute error & 0.903282 & 0.905045 & 0.910703 & 0.912017 & 0.881088 \\ Root relative sq. error & 0.912632 & 0.918536 & 0.935326 & 0.929368 & 0.889698 \\ \hline \multicolumn{6}{c}{Models using attributes SHMU, Satellites (Forward selection 20 attr), without Day of Year} \\ \hline Correlation coefficient & 0.3155 & 0.3276 & 0.3257 & 0.3426 & 0.4708 \\ Relative absolute error & 0.942383 & 0.931576 & 0.946136 & 0.910879 & 0.873131 \\ Root relative sq. error & 0.953646 & 0.946278 & 0.955216 & 0.951316 & 0.883867 \\ \hline \multicolumn{6}{c}{Models using attributes SHMU, Satellites (Manual selection 3 attr), Day of Year} \\ \hline Correlation coefficient & 0.5163 & 0.5245 & 0.5092 & 0.6275 & 0.6004 \\ Relative absolute error & 0.855031 & 0.848659 & 0.849584 & 0.76101 & 0.78007 \\ Root relative sq. error & 0.855314 & 0.849386 & 0.863360 & 0.77716 & 0.800759 \\ \hline \multicolumn{6}{c}{Models using attributes SHMU, Satellites (Forward selection 5 attr), Day of Year} \\ \hline Correlation coefficient & 0.5562 & 0.5565 & 0.5305 & 0.6058 & 0.6434 \\ Relative absolute error & 0.827117 & 0.82688 & 0.835880 & 0.788339 & 0.761934 \\ Root relative sq. error & 0.830543 & 0.829219 & 0.852279 & 0.795252 & 0.764349 \\ \hline \multicolumn{6}{c}{Models using attributes SHMU, Satellites (FSRF-TEST 5 attr), Day of Year} \\ \hline Correlation coefficient & 0.5635 & 0.5661 & 0.5354 & 0.5608 & 0.626 \\ Relative absolute error & 0.822849 & 0.82399 & 0.850483 & 0.825195 & 0.762599 \\ Root relative sq. error & 0.825459 & 0.822494 & 0.849352 & 0.830468 & 0.779014 \\ \hline \multicolumn{6}{c}{Models using attributes SHMU, Satellites (Relief 5 attr), Day of Year} \\ \hline Correlation coefficient & 0.4891 & 0.5099 & 0.4858 & 0.5494 & 0.6368 \\ Relative absolute error & 0.87403 & 0.861349 & 0.869334 & 0.822957 & 0.759978 \\ Root relative sq. error & 0.872218 & 0.858429 & 0.881012 & 0.837942 & 0.769692 \\ \hline \multicolumn{6}{c}{Linear model} & Gaussian process & SMO Regression & M5P Tree & Random forest \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._As for the number of selected input attributes, there is no clear universally optimal number; it largely depends on the degree of relevance of input attributes to a given target attribute. If highly relevant input attributes are available (e.g., from NEIS [16] or from acoustic measurements), the number of required input attributes may significantly decrease, while, paradoxically, the accuracy of the resultant model may increase.