# Application of Machine Learning Techniques to
## 4 Results and Discussion
### Climate Zones
#### 4.1.1 Principal Component Analysis  
The application of the Principal Component Analysis (PCA) was performed on the daily precipitation series data from APHRODITE on a network of 138 stations distributed across the Jhelum and Chenab river basins. This first led to 20 primary and physically significant principle components (PCs), which collectively explained approximately 95% of the total variability in the precipitation throughout the study area. These were retained for the cluster analysis. Scree Plot is shown in Figure 4. The PCA algorithm produced the scree plots, allowing us to select the number of PCs for the cluster analysis. For the warm-wet season, 20 PCs present 95% of the data variance from the station network (Figure 4a). For the cold-dry season, 20 PCs present 94% of the variance in the data from the station network (Figure 4b). For the cold-wet season, 20 PCs explained 95% of the variance in the data from the station network (Figure 4c). For the warm-dry season, 20 PCs explained 95% of the variance in the data from the station network (Figure 4d). The spatial distribution of the component scores is shown in Figure 5, for the first two PCs, explaining the highest percentage of variance in the data. These two PCs cumulatively produced variance of 44.8% in the warm-wet season, 41.2% in the cold-dry season, 45.7% in the cold-wet season and 41.5% in the warm-dry season. For the component scores, the first PC for the warm-wet season, as shown in Figure 5a (with 25% of the explained variance), has high negative scores in the north and southwest of the study region and medium-high positive signals in the central region. Stronger positive and negative signals exhibit an affinity of higher variation in the precipitation amount, while weaker scores indicate low variability. The second PC for the warm-wet season, as shown in Figure 5e (with 19.8% of the explained variance), has mostly positive component scores in the southeast, except for medium and strong negative signals in several southeast and central regions, respectively. In the cold-dry season, we observe higher negative variability in the southwest based on PC1, as shown in Figure 5b and in small areas of the southeast. Strong positive  
Figure 4: Scree and cumulative plots showing the percentage of explained variance by each principal component (PC) in the (**a**) warm-wet, (**b**) cold-dry, (**c**) cold-wet and (**d**) cold-dry seasons. The red line shows the explained variance corresponding to 20 principal components. Figure 5: The spatial patterns of the first two PCs for the daily seasonal precipitation across the study area. The explained variance % is presented on each panel. The component scores are presented by the color distributions across the study area (see color bar for reference): (**a**) warm-wet (PC-1), (**b**) cold-dry (PC-1), (**c**) cold-wet (PC-1), (**d**) warm-dry (PC-1), (**e**) warm-wet (PC-2), (**f**) cold-dry (PC-2), (**g**) cold-wet (PC-2) and (**h**) warm-dry (PC-2) seasons. signals were detected in the northwest area of the study region. For PC2, high positive component scores were observed in the southeast, as shown in Figure 5f (with 18.8% of the explained variance), whereas strong negative scores occurred in the southwest area. In the cold-wet season, the variability in the precipitation amount was high in the southwest region for PC1 (with 27% of the explained variance in the data), as shown in Figure 5c and high positive signals in the southeast and central areas of the region. For PC2, cold-wet season (with 18.7% of the explained variance), shown in Figure 5g, had strong positive scores in the southeast area of the region but medium to high negative scores in the rest of the study area. In the warm-dry season, high variability was observed in the precipitation in the northern and southwest regions of the area for the PC1 component score (with 22.7% of the explained variance), as shown in Figure 5d.