# Metaheuristic Optimized Multi-Level Classification Learning System for Engineering Management
## 5 Engineering Applications
### Multi-Level Problems
#### 5.2.3 Case 3--Urban Land Cover  
Another dataset, concerning urban land cover (675 data points), was obtained from the UCI Machine Learning Repository [82]. Information about land use is important in every city because it is used for many purposes [83], including tax assessment, setting land use policy, city planning, zoning regulation, analysis of environmental processes, and management of natural resources. The assessment of land cover is very important for scientists and authorities that are concerned with mapping the patterns of land cover on global, regional as well as local scales, to understand geographical changes [79]. Therefore, accurate and readily produced land cover classification maps are of great importance in studies of global change.  
The land cover dataset includes a total of 147 features, which include the spectral, magnitude, formal and textural properties of an image of land. The spectral, magnitude, formal and textural properties of the image consist of 21 features. Afterwards, these features were repeated on each coarse scales (20, 40, 60, 80, 100, 120, and 140), yielding 147 features [79]. Table 10 shows the features used in the dataset. The data specify nine forms of land cover--trees (Class 1), concrete (Class 2), shadows (Class 3), asphalt (Class 4), buildings (Class 5), grass (Class 6), pools (Class 7), cars (Class 8) and soil (Class 9)--which are treated as the predictive classes, and listed in Table 11.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Max. Value** & **Min. Value** & **Mean** & **Standard** \\ \hline Input & & & & **Deviation** \\ Secchi disk depth (SD) & 8.375 & 0.1 & 1.8605 & 1.1026 \\ Chlorophyll a (Chla) & 151.4 & 0.1 & 7.9216 & 12.2305 \\ Total phosphorus (TP) & 2.0495 & 0.0022 & 0.0677 & 0.214 \\ \hline Output-Reservoir water & & & & \\ quality & Excellent—Class 1 & & & \\ Good—Class 2 & & N/A & & \\ Average—Class 3 & & & & \\ Fair—Class 4 & & & & \\ Poor—Class 5 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Statistical attributes of reservoir water quality dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Factor/Index** & **Excellent 1** & **Good 2** & **Average 3** & **Fair 4** & **Poor 5** \\ \hline Secchi disk depth (SD) & \textgreater{}4.5 & 4.5–3.7 & 3.7–2.3 & 2.3–1.7 & \textless{}1.7 \\ Chlorophyll a (Chla) & \textless{}2 & 2.0–3.0 & 3.0–7.0 & 7.0–10.0 & \textgreater{}10 \\ Total phosphorus (TP) & \textless{}8 & 8–12 & 12–28 & 28–40 & \textgreater{}40 \\ Carlon’s Trophic State & & & & \\ Index (CTSI) & \textless{}20 & 20–40 & 40–50 & 50–70 & \textgreater{}70 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Single indicator water quality differentiations.  
[PERSON] [79] used three classification algorithms, k-NN, SVM and extreme learning machine (ELM), each combined with the OAR scheme, to predict urban land cover. To verify the effectiveness of the proposed Optimized-OAO-LSSVM model in classifying urban land cover, the performance of the proposed model is compared with their experimental results.  
Table 7 compares the predictive accuracies of the SMO, Multiclass Classifier, Naive Bayes, Logistic, LibSVM, OAO-LSSVM, and the proposed models with that, experimentally determined, of k-NN, SVM, and ELM. As shown in Table 10, the Optimized-OAO-LSSVM had an accuracy of 87.274%, a precision of 87.048%, a sensitivity of 89.918%, a specificity of 87.297% and an AUC of 0.886. Clearly, the Optimized-OAO-LSSVM model outperformed the other models in all these respects. Notably, the Optimized-OAO-LSSVM model is more efficient than the other models at a significance level of 1%.