# Application of Machine Learning Techniques to
## 5 Conclusions  
Previous studies have not currently arrived at a consensus on a universally accepted method and criteria used for GCM selection [14; 78]. Studies continue to investigate the methods, whether dynamic or statistical, to reduce the uncertainty in predicting climate change impacts. Intending to reduce the uncertainty, we presented a novel and flexible framework for the selection of GCMs in homogeneous climatic zones, which are based on daily seasonal precipitation data statistics spanning the baseline/historical period (1970-2005) for the Jhelum and Chenab river basin study areas. The GCM selected in each of the climate zones of every season can reproduce the climate distribution patterns in the study area. This has been proved by comparatively analyzing the precipitation data sets of GCMs selected and the simple composite mean of the ensemble of the 21 CMIP5 GCMs. Using these homogeneous precipitation regions, we suggest a different approach for selecting the GCMs. The GCMs were selected based on prior performance and compared with the highly resolved and gridded APHRODITE data. The GCM selection was validated for its performance to emulate the spatial precipitation patterns during the baseline period (1970-2005) for the study region. We sampled the daily precipitation data for the projected period using the selected GCM. The trends in the precipitation variability, based on the MK trend detection statistics, show that the regions in the Jhelum and Chenab river basins have negative, positive and no trends from 2006 to 2099. The Jhelum and Chenab river basins are scarcely gauged regions; hence, the APHRODITE gridded datasets were used in this study because this type of climate zoning requires a dense network of climate  
Figure 12: The seasonal precipitation trends in the study area for (**a**) warm-wet RCP 4.5, (**b**) cold-dry RCP 4.5, (**c**) cold-wet RCP 4.5, (**d**) warm-dry RCP 4.5, (**e**) warm-wet RCP 8.5, (**f**) cold-dry RCP 8.5, (**g**) cold-wet RCP 8.5 and (**h**) warm-dry RCP 8.5.  
observation stations. Numerous previous studies have verified the reliability of the APHRODITE data in this region [22; 40]. However, at high altitudes, the deviation between the gridded data and in situ observations is more than that of flat regions due to a reduced number of gauging stations in mountainous areas, which poses a challenge to precise interpolation [79]. Uncertainties in the forcing datasets persist due to the scarcity of gauging stations in high elevation zones. Furthermore, gauging stations in valleys cannot represent peripheral higher elevation zones due to the high vertical lapse rate of precipitation [41]. Currently, there is no consensus among the climate research community regarding the methods of bias correction for high elevation areas. The comparative analysis shows that the APHRODITE correlates well with the observed dataset of some gauging stations at high altitudes, leading to the assumption of having sufficient accuracy for the regionalization process. Nevertheless, this regionalization framework is equally applicable to other datasets having comparatively higher resolution and accuracy. All the analyses were conducted using the Python programming language, which is known to be powerful in machine learning. Several machine learning modules from the scikit-learn library, such as Principal Component Analysis (PCA), Agglomerative Hierarchical Clustering (AHC) and clustering validity indices, correlation coefficient and the KS test, were used in the analysis. All these machine learning algorithms were compiled to develop a program for regionalization and climate change trend detection. The program can be provided to the climate research community to augment decision- and policymaking for water resource planning and management. Potential forecasts from the GCMs, however, are fundamentally uncertain and decision-makers still find it difficult to understand or use such predictions of climate change.