Having an extreme topography and heterogeneous climate, the Upper Indus Basin (UIB) is more likely to be affected by climate change and it is a crucial area for climatological studies. Based on the monthly minimum temperature (T_min), maximum temperature (T_max) and precipitation from nine meteorological stations, the spatiotemporal variability of temperature and precipitation were analyzed on monthly, seasonal, and annual scales. Results show a widespread significant increasing trend of 0.14 °C/decade for T_max, but a significant decreasing trend of −0.08 °C/decade for T_min annually, during 1955–2016 for the UIB. Seasonally, warming in T_max is stronger in winter and spring, while the cooling in T_min is greater in summer and autumn. Results of seasonal T_max indicate increasing trends in winter, spring and autumn at rates of 0.38, 0.35 and 0.05 °C/decade, respectively, while decreasing in summer with −0.14 °C/decade. Moreover, seasonal T_min results indicate increasing trends in winter and spring at rates of 0.09 and 0.08 °C/decade, respectively, while decreasing significantly in summer and autumn at rates of −0.21 and −0.22 °C/decade respectively for the whole the UIB. Precipitation exhibits an increasing trend of 2.74 mm/decade annually, while, increasing in winter, summer and autumn at rates of 1.18, 2.06 and 0.62 mm/decade respectively. The warming in T_max and an increase in precipitation have been more distinct since the mid-1990s, while the cooling in T_min is observed in the UIB since the mid-1980s. Warming in the middle and higher altitude (1500–2800 m and >2800 m) are much stronger, and the increase is more obvious in regions with elevation >2800 m. The wavelet analysis illustrated sporadic inter-annual covariance of seasonal T_max, T_min and precipitation with ENSO, NAO, IOD and PDO in the UIB. The periodicities were usually constant over short timescales and discontinuous over longer timescales. This study offers a better understanding of the local climate characteristics and provides a scientific basis for government policymakers.

Since advanced technologies via social media, internet, virtual communities and networks and internet of things (IoT), there are more multi-view data to be collected. Multi-view clustering is a substantial tool as a natural design for clustering multi-view data. K-means (KM) clustering for (single-view) data had been extended for handling multi-view data, called multi-view KM (MV-KM). In the literature, most MV-KM clustering algorithms are reported to be influenced by initializations and also need a given number of clusters. In this paper, we propose an unsupervised type of MV-KM clustering algorithm so that it can automatically find an optimal number of clusters without any initialization. We call it unsupervised MV-KM (U-MV-KM). Moreover, we also propose three multi-view cluster validity indices, called multi-view Dunn index (MV-Dunn), multi-view generalized Dunn index (MV-G-Dunn) and multi-view modified Dunn (MV-M-Dunn) indices for MV-KM clustering algorithms. We make experiments on some synthetic and real data sets and also make comparisons with some existing algorithms. Based on the experimental results and comparisons, the proposed U-MV-KM clustering algorithm actually shows good results. We also apply U-MV-KM to real data sets, the results demonstrate the superiority and usefulness of the U-MV-KM algorithm for clustering multi-view data.

The effects of climate change are unparalleled in magnitude, ranging from changing weather patterns that endanger food production to increasing sea levels that increase the likelihood of catastrophic flooding. Therefore, determining the extent of such variations on regional and local scales is imperative. We used monthly precipitation data from 25 meteorological stations in northern Pakistan (NP) to document the observed changes in seasonal and annual precipitation. The station density in the NP is small and unevenly distributed; therefore, ERA-5 reanalysis data were used to supplement the observed dataset to assess the spatial trends in NP. The non-parametric Mann–Kendall (MK), Sen’s Slope estimator (SSE), and Sequential Mann–Kendall (SQMK) tests were performed to assess the trends. In addition, the wavelet analysis technique was used to determine the association of precipitation with various oceanic indices from 1960 to 2016. Results indicate that maximum precipitation was shown in the annual and summer seasons. In NP, annual, winter, spring, and summer precipitation declined, while an increase in autumn was observed at a rate of 0.43 mm/decade between 1989 and 2016. The spatial trends for observed and ERA-5 reanalysis datasets were almost similar in winter, spring, and autumn; however, some disagreement was observed in both datasets in the summer and annual precipitation trends in NP during 1960–2016. Between 1989 and 2016, summer and annual precipitation increased significantly in Region III. However, seasonal and annual precipitation decreased in NP between 1960 and 2016. Moreover, there were no prominent trends in annual precipitation until the mid-1980s, but an apparent increase from 1985 onwards. Annual precipitation increased in all elevations except at the 500–1000 m zone. The ENSO (El Niño–Southern Oscillation) shared notable interannual coherences among all indices above 16–64 months. Inter-decadal coherence with the ENSO, AO (Arctic Oscillation), and PDO (Pacific Decadal Oscillation) in NP for 128 months and above. Generally, AO, AMO (Atlantic Multidecadal Oscillation), and NAO (North Atlantic Oscillation) exhibited less coherence with precipitation in NP. The regression of seasonal and annual precipitation revealed that winter and spring precipitation levels had higher linear regression with the AO and ENSO, respectively, while both the AO and ENSO also dominated at the annual scale. Similarly, the IOD and PDO indices had a higher influence in summer precipitation. The findings may help water resource managers and climate researchers develop a contingency plan for better water resource management policies in the face of changing climate change in Pakistan, particularly in NP.

The rapid development in information technology makes it easier to collect vast numbers of data through the cloud, internet and other sources of information. Multiview clustering is a significant way for clustering multiview data that may come from multiple ways. The fuzzy c-means (FCM) algorithm for clustering (single-view) datasets was extended to process multiview datasets in the literature, called the multiview FCM (MV-FCM). However, most of the MV-FCM clustering algorithms and their extensions in the literature need prior information about the number of clusters and are also highly influenced by initializations. In this paper, we propose a novel MV-FCM clustering algorithm with an unsupervised learning framework, called the unsupervised MV-FCM (U-MV-FCM), such that it can search an optimal number of clusters during the iteration process of the algorithm without giving the number of clusters a priori. It is also free of initializations and parameter selection. We then use three synthetic and six benchmark datasets to make comparisons between the proposed U-MV-FCM and other existing algorithms and to highlight its practical implications. The experimental results show that our proposed U-MV-FCM algorithm is superior and more useful for clustering multiview datasets.

In recent decades, temperature variations have significantly affected the ecosystem and human livelihood in Pakistan. The wavelet analysis is employed to identify the associations between regional temperature change and global teleconnections, i.e. Atlantic Multidecadal Oscillation (AMO), Arctic Oscillation (AO), North Atlantic Oscillation (NAO), El Niño–Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), Tropical Northern Atlantic Index (TNA), Pacific/North American Index (PNA), North Pacific Pattern (NP), Western Pacific Index (WP), and Western Hemisphere Warm Pool (WHWP). This paper first uses monotonic modified Mann–Kendall and Sen’s slope estimator to compute the temperature changes in Pakistan and its homogenous climatic regions during 1960–2020. It is found that the temperature in Pakistan has increased significantly at 0.23 °C dec⁻¹ in the last 62 years, which is also higher than the global average increase. This increase is more evident in region IV and V in spring at 0.63 and 0.43 °C dec⁻¹ respectively, followed by summer and autumn. Temperature changes in Pakistan and its sub-regions are mainly associated with NP, WP and WHWP with higher mean significant coherences. Overall, temperature changes are significantly influenced by multiple interactions of global teleconnections, and these combinations indicate that the integrated influence of teleconnections can better explain the regional temperature changes. The teleconnections with broader significant influence over Pakistan are NP + WP + WHWP, followed by Pacific-based (ENSO, PDO, and PNA), and Atlantic-based (AMO, AO, and NAO) indices as revealed by the mean significant coherences of 0.82, 0.82 and 0.72 respectively. Annually, AMO, TNA and WHWP showed significant correlation with higher magnitudes of 0.44, 0.42 and 0.20 respectively, indicating the modulation effect of these teleconnections on temperature changes over Pakistan. The combined impacts from the various teleconnections represent a considerable advancement in the accuracy of significant temperature variations over the country. These findings have significant implications for policymakers in terms of better planning and policies in light of climate change as well as atmospheric circulation variability.

In the era of big data, cloud, internet of things, virtual communities, and interconnected networks, the prominence of multiview data is undeniable. This type of data encapsulates diverse feature components across varying perspectives, each offering unique insights into the same underlying samples. Despite being sourced from diverse settings and domains, these data serve the common purpose of describing the same samples, establishing a significant interrelation among them. Thus, there arises a necessity for the development of multiview clustering methodologies capable of leveraging the wealth of information available across multiple views. This study introduces two novel weighted multiview k-means algorithms, W-MV-KM and weighted multiview k-means using L2 regularization, W-MV-KM-L2, designed specifically for clustering multiview data. These algorithms incorporate feature weights and view weights within the k-means (KM) framework. Our approach emphasizes a weighted multiview learning strategy, which assigns varying degrees of importance to individual views. We evaluate the clustering performance of our algorithms on seven diverse benchmark datasets spanning dermatology, textual, image, and digit domains. Through extensive experimentation and comparisons with existing methods, we showcase the superior effectiveness and utility of our newly introduced W-MV-KM-L2 algorithm.