# Application of Machine Learning Techniques to
## 5 Conclusions
The major cause of this uncertainty in the assessments derives from the inherent uncertainties in GCM outputs [80; 81; 82; 83]. Based on the results and interpretations presented in this study, we can draw the following conclusions:  
1. Due to its highly volatile hydroclimatic conditions, this area of Pakistan poses major scientific challenges; hence, investigations require complex methods. Multivariate techniques, such as PCA and Agglomerative Hierarchical Clustering (AHC) algorithms, are used to develop decisive precipitation statistics and delineate homogeneous precipitation regions, respectively. The entire study area was divided into 13 homogeneous precipitation regions for the warm-wet season, 13 homogeneous precipitation regions for the cold-dry season, 14 homogeneous precipitation regions for the cold-wet season and 13 homogeneous precipitation regions for the warm-dry season. The reference station, which is representative of the respective homogeneous precipitation regions, was obtained in each homogeneous precipitation region for GCM selection. Seasonal rainfall characteristics were incorporated to define the homogeneous climate regions to design the climate zones in the river basins of Pakistan. Representative rainfall station/grid points were selected in each climate zone for the climate change impact assessment. This study provides an objective demarcation structure for homogeneous precipitation regions based on the statistical characteristics of seasonal precipitation. 2. The best-correlated GCM was identified in each climate zone in the Jhelum and Chenab river basins, followed by data extraction. This selection was validated and compared spatially with the KS test. Two schemes of the daily precipitation data were derived: (1) the precipitation data sampled from the selected GCMs based on this framework and (2) the precipitation data from the MME mean (21 models of the CMIP5 mean). The KS test was used to measure the compatibility of the observed data with data from scheme 1 and then the data from scheme 2. The results clearly show the improved performance of the present framework for the selected GCMs compared with the conventional method involving the equally weighted means of all the available GCMs. 3. The precipitation pattern can represent the climatic dynamics because the atmospheric circulation covariates have a strong correlation with the precipitation patterns in the region [2]. The daily projected precipitation data (2006-2099), synthesized for the regions of the Jhelum and Chenab river basins, are publicly available. The creation of homogeneous climatic zones accommodates a deeper understanding of the dynamic spatio-temporal precipitation variability over a given region. Infrastructure development and climate forecasting are based on effective regionalization using reliable estimates of climate variables. The region should be conditionally considered as climatically homogeneous to proceed with the selection of GCMs for the climate change impact assessments. 4. The projections of the changes in the precipitation trends were presented based on two scenarios, that is, RCP 4.5 and RCP 8.5. For the RCP 4.5, a significant positive precipitation trend was observed for the warm-dry season, while insignificant positive trends were observed for the cold dry and cold wet seasons. A negative precipitation trend was observed throughout the entire study area, in which 40% of the area had a significant negative trend for the warm-wet season. For the RCP 8.5, the warm-dry season again exhibited a significant positive precipitation trend, whereas insignificant positive trends were observed for the warm-wet, cold-dry and cold-wet seasons. The effect of the precipitation change trends may have greater implications on water resources and its management. This unique study provides spatial changes in patterns, as well as temporal changes. Strong increasing and decreasing signals occurred during the warm-dry season for both scenarios in the river basins.