## 4 Results and Discussion  
In this section, the performance of the TSADL-LULCCD model is tested using the data collected from Nallamalla forest, India. The details related to the target region used for simulation analysis are shown in Table 1. Figure 3 illustrates the sample images. The confusion matrices of the TsADL-LULCCD model are clearly demonstrated in Figure 4. On 80% of the TR database, the TSADL-LULCCD method has recognized \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Class** & **Area (km2)** & **Size** & **\% of Area** \\ \hline Grassland & 321.30 & 321.00 & 30.20 \\ Agriculture & 182.90 & 183.00 & 17.19 \\ Barren land & 231.90 & 232.00 & 21.80 \\ Water & 327.70 & 328.00 & 30.80 \\ \hline
**Total** & **1063.80** & **1064.00** & **100.00** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Details on Database. Figure 3: (a) Map of Nallamalla Forest [16.0131\({}^{\circ}\) N, 78.9717\({}^{\circ}\) E] (b) Site View of Nallamalla Forest. 243 samples in grassland, 129 samples in agriculture, 177 samples in barren land, and 259 samples in water. Simultaneously on 20% of the TS database, the TSADL-LULCCD technique has recognized 66 samples in grassland, 43 samples in agriculture, 43 samples in barren land, and 54 samples in water. Concurrently on 70% of the TR database, the TSADL-LULCCD method has recognized 212 samples in grassland, 115 samples in agriculture, 145 samples in barren land, and 231 samples in water. Finally, on 20% of the TS database, the TSADL-LULCCD approach has recognized 100 samples in grassland, 50 samples in agriculture, 74 samples in barren land, and 88 samples in water. In Table 2 and Figure 5, the overall classification outcomes of the TSADL-LULCCD model with 80:20 of the TR/TS database are provided. On the TR database, the TSADL-LULCCD model has recognized grassland samples with \(accu_{y}\) of 97.30%, \(prec_{n}\) of 95.29%, \(rec_{1}\) of 95.67%, \(F_{score}\) of 95.48%, \(AUC_{score}\) of 96.83%, and MCC of 93.55%. Meanwhile, the TSADL-LULCCD model has identified agriculture samples with \(accu_{y}\) of 97.88%,  
Figure 4: Confusion matrices of TSADL-LULCCD method (**a,b**) TR and TS databases of 80:20 and (**c,d**) TR and TS databases of 70:30.  
of 94.16%, \(reca_{I}\) of 92.81%, \(F_{score}\) of 93.48%, \(AUC_{score}\) of 95.84%, and MCC of 92.22%. Furthermore, the TSADL-LULCCD model has categorized barren land samples with \(accu_{y}\) of 97.06%, \(preca_{I}\) of 92.19%, \(reca_{I}\) of 94.65%, \(F_{score}\) of 93.40%, \(AUC_{score}\) of 96.20%, and MCC of 91.53%. On the TR database, the TSADL-LULCCD technique has recognized grassland samples with \(accu_{y}\) of 98.12%, \(prec_{n}\) of 95.65%, \(reca_{I}\) of 98.51%, \(F_{score}\) of 97.06%, \(AUC_{score}\) of 98.23%, and MCC of 95.70%. In the meantime, the TSADL-LULCCD approach has identified agriculture samples with \(accu_{y}\) of 98.59%, \(prec_{n}\) of 95.56%, \(reca_{I}\) of 97.73%, \(F_{score}\) of 96.63%, \(AUC_{score}\) of 98.27%, and MCC of 95.75%. Furthermore, the TSADL-LULCCD method has categorized barren land samples with \(accu_{y}\) of 98.59%, \(prec_{n}\) of 97.73%, \(reca_{I}\) of 95.56%, \(F_{score}\) of 96.63%, \(AUC_{score}\) of 97.48%, and MCC of 95.75%. The TACC and VACC of the TSADL-LULCCD approach are inspected on 80:20 of TR/TS databases in Figure 6. The outcomes implied that the TSADL-LULCCD methodology had shown improved performance with increased values of TACC and VACC. Notably, the TSADL-LULCCD method has reached maximum TACC outcomes.