Globally, natural hazards have become more destructive in recent times because of rapid urban development and exposure. Consequently, significant human life loss, the damage to property and infrastructure, and the collapse of the environment directed the attention of geoscientists to control the consequences and risk management in relation to geo-hazards. In this research, an effort was made to produce a compound map, geo-visualizing the susceptibility of multi-hazards, to select suitable sites for sustainable future development and other economic activities in the region. Muzaffarabad District was chosen as a case research area due to the high magnitude of hydro-meteorological and geological hazards. On the one hand, both selected geo-hazard inventories were developed using the field survey and remote sensing data. The subjective and objective weight of all the causative factors and their classes were calculated using the assembled geospatial techniques, such as the Analytical Hierarchy Process (AHP) and Frequency Ratio (FR) in the Geographic Information System (GIS). The results reveal that the most suitable areas are distributed in the southern and northwestern parts, which can be used for future sustainable development and other economic activities. In contrast, the eastern and western regions, including Muzaffarabad City, are within high and very susceptibility zones. Finally, more than 50% of the land area is located in very low and low susceptibility zones. The validation of the proposed model was checked by using three different techniques: the Receiver Operative Characteristic (ROC) curve, Seed Cell Area Index (SCAI), and Frequency Ratio (FR). Both ROCs, the Success Rate Curve (SRC) and the Predictive Rate Curve (PRC), showed the goodness of fit for both the selected geo-hazards: landslides (81.3%) and floods (93.2%), at 80.1% and 91.7%, respectively. All the validation techniques showed good fitness for both the individual and multi-hazard maps. The proposed model sets a baseline for policy implementation for all the stakeholders to minimize the risk and sustainable future development in areas of high frequent geo-hazards.

The Indus River is Asia’s longest river, having its origin in the Tibet Mountain northwest of Pakistan. Routed from northern Gilgit and flowing to the plains, the river passes through several provinces and is connected by numerous small and large tributaries. The river was formed tectonically due to the collusion of the Indian and Eurasian plates, which is referred to as the Indus suture Plains zone (ISPZ). The geological setting of the study area is mainly composed of igneous and metamorphic rocks. The river passed through a variety of climatic zones and areas, although the predominant climate is subtropic arid and sub arid to subequatorial. Locally and globally, anthropogenic activities such as building, dams, and water canals for irrigation purposes, mining exploration, and industries and factories all affected the physical and chemical behaviors of the sediments in various rivers. The main effect of human activities is the reworking of weathered soil smectite, a chemical weathering indicator that rises in the offshore record about 5000 years ago. This material indicates increased transport of stronger chemically weathered material, which may result from agriculture-induced erosion of older soil. However, we also see evidence for the incision of large rivers into the floodplain, which is also driving the reworking of this type of material, so the signal may be a combination of the two. Sediments undergo significant changes in form and size due to clashing with one another in the high-charge river.

Dengue is one of Pakistan's major health concerns. In this study, we aimed to advance our understanding of the levels of knowledge, attitudes, and practices (KAPs) in Pakistan's Dengue Fever (DF) hotspots. Initially, at-risk communities were systematically identified via a well-known spatial modeling technique, named, Kernel Density Estimation, which was later targeted for a household-based cross-sectional survey of KAPs. To collect data on sociodemographic and KAPs, random sampling was utilized (n = 385, 5 % margin of error). Later, the association of different demographics (characteristics), knowledge, and attitude factors-potentially related to poor preventive practices was assessed using bivariate (individual) and multivariable (model) logistic regression analyses. Most respondents (>90 %) identified fever as a sign of DF; headache (73.8 %), joint pain (64.4 %), muscular pain (50.9 %), pain behind the eyes (41.8 %), bleeding (34.3 %), and skin rash (36.1 %) were identified relatively less. Regression results showed significant associations of poor knowledge/attitude with poor preventive practices; dengue vector (odds ratio [OR] = 3.733, 95 % confidence interval [CI ] = 2.377-5.861; P < 0.001), DF symptoms (OR = 3.088, 95 % CI = 1.949-4.894; P < 0.001), dengue transmission (OR = 1.933, 95 % CI = 1.265-2.956; P = 0.002), and attitude (OR = 3.813, 95 % CI = 1.548-9.395; P = 0.004). Moreover, education level was stronger in bivariate analysis and the strongest independent factor of poor preventive practices in multivariable analysis (illiterate: adjusted OR = 6.833, 95 % CI = 2.979-15.672; P < 0.001) and primary education (adjusted OR = 4.046, 95 % CI = 1.997-8.199; P < 0.001). This situation highlights knowledge gaps within urban communities, particularly in understanding dengue transmission and signs/symptoms. The level of education in urban communities also plays a substantial role in dengue control, as observed in this study, where poor preventive practices were more prevalent among illiterate and less educated respondents.

Deep-learning research on flood inundation mapping is driven largely by architectural innovation and single-run reporting. We ask whether the segmentation architecture or the input representation contributes more to accuracy in flood-event surface-water mapping. We introduce GeoHybridNet, a hybrid CNN–Transformer network, and compare it on the hand-labeled Sen1Floods11 benchmark with a U-Net, an Attention U-Net, a parameter-matched wide U-Net, and SegFormer under matched data and training. The four convolutional and hybrid models differ by at most 1.4 IoU points, and none are significantly different under event-level tests; a compact SegFormer trained from scratch is about 3.5 points lower. In factorial designs crossing three architectures with up to five input representations (five seeds each), input representation explains about 95% of the variance in water IoU and architecture about 1%. The dominant input factor is radiometric normalization: raw bands standardized per chip reach only 60% IoU and adding NDWI restores 76%, whereas the same bands standardized with training-set statistics reach 77.5% without any index. The pattern holds when permanent water is excluded. On three held-out flood events, accuracy drops by 24–26 IoU points for every model. On this benchmark, input preparation and honest evaluation matter more than architecture.